diff --git a/.gitattributes b/.gitattributes index b117c79927f77e63ee15d7596796a1ae4ed53883..8059d9222e5dffe012ae070a85efa856ea1f3f36 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1553,3 +1553,5 @@ infer_4_47_1/lib/python3.10/site-packages/torch/__pycache__/_meta_registrations. evalkit_internvl/lib/python3.10/site-packages/nvidia/cufft/lib/libcufft.so.10 filter=lfs diff=lfs merge=lfs -text infer_4_30_0/lib/python3.10/site-packages/vllm/vllm_flash_attn/_vllm_fa3_C.abi3.so filter=lfs diff=lfs merge=lfs -text evalkit_internvl/lib/python3.10/site-packages/xformers/_C_flashattention.so filter=lfs diff=lfs merge=lfs -text +infer_4_30_0/lib/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2 filter=lfs diff=lfs merge=lfs -text +evalkit_tf433/lib/python3.10/site-packages/xformers/_C.so filter=lfs diff=lfs merge=lfs -text diff --git a/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/LICENSE b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cc633905d333c4b42c1a0c8b34e9f734adeb6e1e --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Just van Rossum + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/RECORD b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..7cb1099685b630c67f6ffafdf527dcd28052053d --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/RECORD @@ -0,0 +1,643 @@ +../../../bin/fonttools,sha256=7oBiMentBKfqzCF1hWjdihOS_HgtNNszTdZZmIy3WgY,239 +../../../bin/pyftmerge,sha256=goiWS8bD1fOklXftvb7TxWCN9F7dMsiBMjyDriCxpQM,236 +../../../bin/pyftsubset,sha256=hKD4vHOlNNIRmyS2uMO4TWUo8ll2NwoVPujIwlnvugU,237 +../../../bin/ttx,sha256=s2cIG-jwmFxba94kpNlsb2_ZtbkDFEXg8F880TvP7ho,234 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0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/WHEEL b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..f5487b22db738ec9d4f3fcf1afc701af226d4171 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: setuptools (75.6.0) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64 +Tag: cp310-cp310-manylinux2014_x86_64 + diff --git a/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/entry_points.txt b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..87ae781f169a63f0cf672a9050474035bfa5add4 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/fonttools-4.55.3.dist-info/entry_points.txt @@ -0,0 +1,5 @@ +[console_scripts] +fonttools = fontTools.__main__:main +pyftmerge = fontTools.merge:main +pyftsubset = fontTools.subset:main +ttx = fontTools.ttx:main diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/__pycache__/__init__.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..53ff8d2057b4d5d8acab8c0f933c1b970cfa04ce Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/__pycache__/__init__.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3de7787df0c3304207b42b51e9fb62da9d33c7d0 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py @@ -0,0 +1,4 @@ +# This file has moved to under torch/_functorch. It is not public API. +# If you are not a PyTorch developer and you are relying on the following +# imports, please file an issue. +from torch._functorch.make_functional import _swap_state diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/compile/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/compile/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..96b853cd2e27e947bf25b63325741637a801426f --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/compile/__init__.py @@ -0,0 +1,31 @@ +from torch._functorch import config +from torch._functorch.aot_autograd import ( + aot_function, + aot_module, + aot_module_simplified, + compiled_function, + compiled_module, + get_aot_compilation_context, + get_aot_graph_name, + get_graph_being_compiled, + make_boxed_compiler, + make_boxed_func, +) +from torch._functorch.compilers import ( + debug_compile, + default_decompositions, + draw_graph_compile, + memory_efficient_fusion, + nnc_jit, + nop, + print_compile, + ts_compile, +) +from torch._functorch.fx_minifier import minifier +from torch._functorch.partitioners import ( + default_partition, + draw_graph, + draw_joint_graph, + min_cut_rematerialization_partition, +) +from torch._functorch.python_key import pythonkey_decompose diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e8c6b0df0d58019bf1256d7307521fb090d0b376 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/__init__.py @@ -0,0 +1,179 @@ +import dis +import inspect +from typing import Sequence, Union + +import torch + +import functorch._C +from functorch._C import dim as _C +from .tree_map import tree_flatten, tree_map +from .wrap_type import wrap_type + +_C._patch_tensor_class() +dims, DimList, dimlists = _C.dims, _C.DimList, _C.dimlists + + +class DimensionMismatchError(Exception): + pass + + +class DimensionBindError(Exception): + pass + + +from . import op_properties + +# use dict to avoid writing C++ bindings for set +pointwise = {t: True for t in op_properties.pointwise} + +use_c = True +if not use_c: + from . import reference + + +class _Tensor: + # fast path around slow wrapping/unwrapping logic for simply queries used + # by the implementation... + + @property + def dims(self): + return tuple(d for d in self._levels if isinstance(d, Dim)) + + def dim(self): + return self.ndim + + if use_c: + __torch_function__ = classmethod(_C.__torch_function__) + expand = _C._instancemethod(_C.expand) + else: + __torch_function__ = reference.__torch_function__ + expand = reference.expand + + index = _C._instancemethod(_C.index) + + def __repr__(self): + tensor, levels, ndim = self._tensor, self._levels, self.ndim + return f"{tensor}\nwith dims={tuple(l + ndim if isinstance(l, int) else l for l in levels)} sizes={tuple(tensor.size())}" + + +TensorLike = (_Tensor, torch.Tensor) + + +class Dim(_C.Dim, _Tensor): + # note that _C.Dim comes before tensor because we want the Dim API for things like size to take precendence. + # Tensor defines format, but we want to print Dims with special formatting + __format__ = object.__format__ + + +class Tensor(_Tensor, _C.Tensor): + if not use_c: + from_batched = staticmethod(_C.Tensor_from_batched) + from_positional = staticmethod(_C.Tensor_from_positional) + sum = _C._instancemethod(_C.Tensor_sum) + + +def cat(tensors, dim, new_dim): + n = dims() + return stack(tensors, n, dim).index([n, dim], new_dim) + + +if use_c: + _wrap = _C._wrap + + def _def(name, *args, **kwargs): + orig = getattr(torch.Tensor, name) + setattr(_Tensor, name, _C._instancemethod(_wrap(orig, *args, **kwargs))) + + t__getitem__ = _C._instancemethod(_C.__getitem__) + stack = _C.stack + split = _C._instancemethod(_C.split) +else: + _wrap, _def = reference._wrap, reference._def + t__getitem__ = reference.t__getitem__ + stack = reference.stack + split = reference.split + +# note: there is no python reference +t__setitem__ = _C._instancemethod(_C.__setitem__) +# this is patched in the C API because otherwise torch.Tensor will +# no longer be considered a sequence and things will break +# torch.Tensor.__getitem__ = t__getitem__ + +_Tensor.__getitem__ = t__getitem__ +# torch.Tensor.__setitem__ = t__setitem__ +_Tensor.__setitem__ = t__setitem__ + +torch.Tensor.split = split +_Tensor.split = split +torch.Tensor.expand = _C._instancemethod(_C.expand) +torch.Tensor.index = _C._instancemethod(_C.index) +wrap_type(use_c, _Tensor, torch.Tensor, _Tensor.__torch_function__) +del _Tensor.ndim + +if use_c: + _Tensor.order = _C._instancemethod(_C.order) +else: + _Tensor.order = reference.positional + +_def("mean") +_def("sum") +_def("all") +_def("amax") +_def("amin") +_def("aminmax") +_def("any") +_def("count_nonzero") +_def("logsumexp") +_def("nanmean") +_def("nansum") +_def("prod") +_def("std", keepdim_offset=2) +_def("var", keepdim_offset=2) +_def("max", single_dim=True) +_def("min", single_dim=True) +_def("argmax", single_dim=True) +_def("argmin", single_dim=True) +_def("kthvalue", single_dim=True) +_def("median", single_dim=True) +_def("nanmedian", single_dim=True) +_def("mode", single_dim=True) +_def("sort", reduce=False) +_def("argsort", reduce=False) +_def("unbind", single_dim=True) +_def("chunk", dim_offset=1, reduce=False) +_def("cummax", single_dim=True, reduce=False) +_def("cummin", single_dim=True, reduce=False) +_def("cumprod", single_dim=True, reduce=False) +_def("cumprod_", single_dim=True, reduce=False) +_def("cumsum", single_dim=True, reduce=False) +_def("cumsum_", single_dim=True, reduce=False) +_def("logcumsumexp", single_dim=True, reduce=False) +_def("renorm", dim_offset=1, single_dim=True, reduce=False) +_def("softmax", single_dim=True, reduce=False) +softmax = _wrap(torch.nn.functional.softmax, single_dim=True, reduce=False) + +# stuff to handle in the future, because they require special +# binding logic for dims +# cross +# diag_embed +# diagonal +# diagonal_scatter +# diff +# nanquantile +# quantile +# roll +# rot90 +# topk (new dimes on output) +# should these all be subsumed by inplace indexing? +# index_add_ +# index_add +# index_copy +# index_copy_ +# index_fill +# index_fill_ +# index_select +# scatter +# scatter_ +# scatter_add +# scatter_add_ +# scatter_reduce diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/reference.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/reference.py new file mode 100644 index 0000000000000000000000000000000000000000..29d65b13ab3aac89f24fa0ed97ef12928eb4f74c --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/reference.py @@ -0,0 +1,645 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# reference python implementations for C ops +import torch + +from functorch._C import dim as _C +from . import op_properties +from .batch_tensor import _enable_layers +from .tree_map import tree_flatten, tree_map + +DimList = _C.DimList +import operator +from functools import reduce + + +# use dict to avoid writing C++ bindings for set +pointwise = set(op_properties.pointwise) + + +def prod(x): + return reduce(operator.mul, x, 1) + + +def _wrap_dim(d, N, keepdim): + from . import Dim + + if isinstance(d, Dim): + assert not keepdim, "cannot preserve first-class dimensions with keepdim=True" + return d + elif d >= 0: + return d - N + else: + return d + + +def _dims(d, N, keepdim, single_dim): + from . import Dim + + if isinstance(d, (Dim, int)): + return ltuple((_wrap_dim(d, N, keepdim),)) + assert not single_dim, f"expected a single dimension or int but found: {d}" + return ltuple(_wrap_dim(x, N, keepdim) for x in d) + + +def _bind_dims_to_size(lhs_size, rhs, lhs_debug): + from . import DimensionMismatchError + + not_bound = tuple((i, r) for i, r in enumerate(rhs) if not r.is_bound) + if len(not_bound) == 1: + idx, d = not_bound[0] + rhs_so_far = prod(r.size for r in rhs if r.is_bound) + if lhs_size % rhs_so_far != 0: + rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs) + raise DimensionMismatchError( + f"inferred dimension does not evenly fit into larger dimension: {lhs_size} vs {rhs_s}" + ) + new_size = lhs_size // rhs_so_far + d.size = new_size + elif len(not_bound) > 1: + rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs) + raise DimensionMismatchError( + f"cannot infer the size of two dimensions at once: {rhs} with sizes {rhs_s}" + ) + else: + rhs_size = prod(r.size for r in rhs) + if lhs_size != rhs_size: + raise DimensionMismatchError( + f"Dimension sizes to do not match ({lhs_size} != {rhs_size}) when matching {lhs_debug} to {rhs}" + ) + + +def _tensor_levels(inp): + from . import _Tensor + + if isinstance(inp, _Tensor): + return inp._tensor, llist(inp._levels), inp._has_device + else: + return inp, llist(range(-inp.ndim, 0)), True + + +def _match_levels(v, from_levels, to_levels): + view = [] + permute = [] + requires_view = False + size = v.size() + for t in to_levels: + try: + idx = from_levels.index(t) + permute.append(idx) + view.append(size[idx]) + except ValueError: + view.append(1) + requires_view = True + if permute != list(range(len(permute))): + v = v.permute(*permute) + if requires_view: + v = v.view(*view) + return v + + +# make a single dimension positional but do not permute it, +# used to do multi-tensor operators where the dim being acted on +# should not physically move if possible +def _positional_no_permute(self, dim, expand_dim=False): + from . import Tensor + + ptensor, levels = self._tensor, llist(self._levels) + try: + idx = levels.index(dim) + except ValueError: + if not expand_dim: + raise + idx = 0 + ptensor = ptensor.expand(dim.size, *ptensor.size()) + levels.insert(0, 0) + idx_batched = 0 + for i in range(idx): + if isinstance(levels[i], int): + levels[i] -= 1 + idx_batched += 1 + levels[idx] = -idx_batched - 1 + return Tensor.from_positional(ptensor, levels, self._has_device), idx_batched + + +def seq(a, b): + from . import Dim + + if isinstance(a, Dim) != isinstance(b, Dim): + return False + if isinstance(a, Dim): + return a is b + else: + return a == b + + +class isin: + def __contains__(self, item): + for x in self: + if seq(item, x): + return True + return False + + def index(self, item): + for i, x in enumerate(self): + if seq(item, x): + return i + raise ValueError + + +class llist(isin, list): + pass + + +class ltuple(isin, tuple): + pass + + +empty_dict = {} + + +@classmethod +def __torch_function__(self, orig, cls, args, kwargs=empty_dict): + from . import _Tensor, Tensor, TensorLike + from .delayed_mul_tensor import DelayedMulTensor + + if orig is torch.Tensor.__mul__: + lhs, rhs = args + if ( + isinstance(lhs, _Tensor) + and isinstance(rhs, _Tensor) + and lhs.ndim == 0 + and rhs.ndim == 0 + ): + return DelayedMulTensor(lhs, rhs) + all_dims = llist() + flat_args, unflatten = tree_flatten((args, kwargs)) + device_holding_tensor = None + for f in flat_args: + if isinstance(f, _Tensor): + if f._has_device: + device_holding_tensor = f._batchtensor + for d in f.dims: + if d not in all_dims: + all_dims.append(d) + + def unwrap(t): + if isinstance(t, _Tensor): + r = t._batchtensor + if device_holding_tensor is not None and not t._has_device: + r = r.to(device=device_holding_tensor.device) + return r + return t + + if orig in pointwise: + result_levels = llist() + arg_levels = llist() + to_expand = [] + for i, f in enumerate(flat_args): + if isinstance(f, TensorLike): + ptensor, levels, _ = _tensor_levels(f) + if ( + isinstance(f, _Tensor) + and not f._has_device + and device_holding_tensor is not None + ): + ptensor = ptensor.to(device=device_holding_tensor.device) + flat_args[i] = ptensor + for l in levels: + if l not in result_levels: + result_levels.append(l) + to_expand.append((i, levels)) + + for i, levels in to_expand: + flat_args[i] = _match_levels(flat_args[i], levels, result_levels) + args, kwargs = unflatten(flat_args) + result = orig(*args, **kwargs) + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_positional( + t, result_levels, device_holding_tensor is not None + ) + return t + + return tree_map(wrap, result) + else: + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_batched(t, device_holding_tensor is not None) + return t + + with _enable_layers(all_dims): + print(f"batch_tensor for {orig}") + args, kwargs = unflatten(unwrap(f) for f in flat_args) + result = orig(*args, **kwargs) + # print("END", orig) + return tree_map(wrap, result) + + +def positional(self, *dims): + from . import Dim, Tensor + + ptensor, levels = self._tensor, llist(self._levels) + flat_dims = llist() + view = [] + needs_view = False + ndim = self.ndim + for d in dims: + if isinstance(d, DimList): + flat_dims.extend(d) + view.extend(e.size for e in d) + elif isinstance(d, Dim): + flat_dims.append(d) + view.append(d.size) + elif isinstance(d, int): + d = _wrap_dim(d, ndim, False) + flat_dims.append(d) + view.append(ptensor.size(d)) + else: + flat_dims.extend(d) + view.append(prod(e.size for e in d)) + needs_view = True + + permute = list(range(len(levels))) + nflat = len(flat_dims) + for i, d in enumerate(flat_dims): + try: + idx = levels.index(d) + except ValueError as e: + raise DimensionBindError( + f"tensor of dimensions {self.dims} does not contain dim {d}" + ) from e + p = permute[idx] + del levels[idx] + del permute[idx] + levels.insert(i, 0) + permute.insert(i, p) + ptensor = ptensor.permute(*permute) + seen = 0 + for i in range(len(levels) - 1, -1, -1): + if isinstance(levels[i], int): + seen += 1 + levels[i] = -seen + result = Tensor.from_positional(ptensor, levels, self._has_device) + if needs_view: + result = result.reshape(*view, *result.size()[len(flat_dims) :]) + return result + + +def _contains_dim(input): + from . import Dim + + for i in input: + if isinstance(i, Dim): + return True + + +def expand(self, *sizes): + if not _contains_dim(sizes): + return self.__torch_function__(torch.Tensor.expand, None, (self, *sizes)) + dims = sizes + sizes = [d.size for d in dims] + [-1] * self.ndim + self = self.expand(*sizes) + return self[dims] + + +_not_present = object() + + +def _getarg(name, offset, args, kwargs, default): + if len(args) > offset: + return args[offset] + return kwargs.get(name, default) + + +def _patcharg(name, offset, args, kwargs, value): + if len(args) > offset: + args[offset] = value + else: + kwargs[name] = value + + +def _wrap( + orig, dim_offset=0, keepdim_offset=1, dim_name="dim", single_dim=False, reduce=True +): + from . import Dim, Tensor, TensorLike + + def fn(self, *args, **kwargs): + dim = _getarg(dim_name, dim_offset, args, kwargs, _not_present) + if dim is _not_present or (single_dim and not isinstance(dim, Dim)): + with _enable_layers(self.dims): + print(f"dim fallback batch_tensor for {orig}") + return Tensor.from_batched( + orig(self._batchtensor, *args, **kwargs), self._has_device + ) + keepdim = ( + _getarg("keepdim", keepdim_offset, args, kwargs, False) if reduce else False + ) + t, levels = self._tensor, llist(self._levels) + dims = _dims(dim, self._batchtensor.ndim, keepdim, single_dim) + dim_indices = tuple(levels.index(d) for d in dims) + if reduce and not keepdim: + new_levels = [l for i, l in enumerate(levels) if i not in dim_indices] + else: + new_levels = levels + + if len(dim_indices) == 1: + dim_indices = dim_indices[ + 0 + ] # so that dims that really only take a single argument work... + args = list(args) + _patcharg(dim_name, dim_offset, args, kwargs, dim_indices) + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_positional(t, new_levels, self._has_device) + return t + + with _enable_layers(new_levels): + print(f"dim used batch_tensor for {orig}") + r = orig(t, *args, **kwargs) + return tree_map(wrap, r) + + return fn + + +def _def(name, *args, **kwargs): + from . import _Tensor + + orig = getattr(torch.Tensor, name) + setattr(_Tensor, name, _wrap(orig, *args, **kwargs)) + + +no_slice = slice(None) + +_orig_getitem = torch.Tensor.__getitem__ + + +class dim_tracker: + def __init__(self): + self.dims = llist() + self.count = [] + + def record(self, d): + if d not in self.dims: + self.dims.append(d) + self.count.append(1) + + def __getitem__(self, d): + return self.count[self.dims.index(d)] + + +def t__getitem__(self, input): + from . import _Tensor, Dim, DimensionBindError, DimList, Tensor, TensorLike + + # * bail to original example if we have a single non-Dim tensor, or a non-tensor + # * locate ... or an unbound tensor list, and determine its size, bind dim list + # (remember that None does not count to the total dim count) + # * bind simple dims and dim-packs to their sizes, count the number of uses of each dim, + # produce the re-view if needed + # * for each single-use dim index, replace with no_slice and mark that it will be added + # (keep track of whether we have to call super) + # * call super if needed + # * if we have dims to bind, bind them (it will help if we eliminated ... and None before) + + # this handles bool indexing handling, as well as some other simple cases. + + is_simple = ( + not isinstance(input, Dim) + and not isinstance(input, (tuple, list)) + and + # WAR for functorch bug where zero time tensors in getitem are not handled correctly. + not (isinstance(input, TensorLike) and input.ndim == 0) + ) + + if is_simple: + if isinstance(self, _Tensor): + return _Tensor.__torch_function__(_orig_getitem, None, (self, input)) + else: + return _orig_getitem(self, input) + + # can further optimize this case + if not isinstance(input, tuple): + input = [input] + else: + input = list(input) + + dims_indexed = 0 + expanding_object = None + dimlists = [] + for i, s in enumerate(input): + if s is ... or isinstance(s, DimList) and not s.is_bound: + if expanding_object is not None: + msg = ( + "at most one ... or unbound dimension list can exist in indexing list but" + f" found 2 at offsets {i} and {expanding_object}" + ) + raise DimensionBindError(msg) + expanding_object = i + + if isinstance(s, DimList): + dims_indexed += len(s) if s.is_bound else 0 + dimlists.append(i) + elif s is not None and s is not ...: + dims_indexed += 1 + + ndim = self.ndim + if dims_indexed > ndim: + raise IndexError( + f"at least {dims_indexed} indices were supplied but the tensor only has {ndim} dimensions." + ) + if expanding_object is not None: + expanding_ndims = ndim - dims_indexed + obj = input[expanding_object] + if obj is ...: + input[expanding_object : expanding_object + 1] = [ + no_slice + ] * expanding_ndims + else: + obj.bind_len(expanding_ndims) + # flatten the dimslists into the indexing + for i in reversed(dimlists): + input[i : i + 1] = input[i] + dims_indexed = 0 + requires_view = False + size = self.size() + view_sizes = [] + dims_seen = dim_tracker() + + def add_dims(t): + if not isinstance(t, _Tensor): + return + for d in t.dims: + dims_seen.record(d) + + add_dims(self) + dim_packs = [] + for i, idx in enumerate(input): + if idx is None: + input[i] = no_slice + view_sizes.append(1) + requires_view = True + else: + sz = size[dims_indexed] + if isinstance(idx, Dim): + idx.size = sz + dims_seen.record(idx) + view_sizes.append(sz) + elif isinstance(idx, (tuple, list)) and idx and isinstance(idx[0], Dim): + for d in idx: + dims_seen.record(idx) + _bind_dims_to_size(sz, idx, f"offset {i}") + view_sizes.extend(d.size for d in idx) + requires_view = True + dim_packs.append(i) + else: + add_dims(idx) + view_sizes.append(sz) + dims_indexed += 1 + if requires_view: + self = self.view(*view_sizes) + for i in reversed(dim_packs): + input[i : i + 1] = input[i] + + # currenty: + # input is flat, containing either Dim, or Tensor, or something valid for standard indexing + # self may have first-class dims as well. + + # to index: + # drop the first class dims from self, they just become direct indices of their positions + + # figure out the dimensions of the indexing tensors: union of all the dims in the tensors in the index. + # these dimensions will appear and need to be bound at the first place tensor occures + + if isinstance(self, _Tensor): + ptensor_self, levels = self._tensor, list(self._levels) + # indices to ptensor rather than self which has first-class dimensions + input_it = iter(input) + flat_inputs = [next(input_it) if isinstance(l, int) else l for l in levels] + has_device = self._has_device + to_pad = 0 + else: + ptensor_self, flat_inputs = self, input + to_pad = ptensor_self.ndim - len(flat_inputs) + has_device = True + + result_levels = [] + index_levels = [] + tensor_insert_point = None + to_expand = {} + requires_getindex = False + for i, inp in enumerate(flat_inputs): + if isinstance(inp, Dim) and dims_seen[inp] == 1: + flat_inputs[i] = no_slice + result_levels.append(inp) + elif isinstance(inp, TensorLike): + requires_getindex = True + if tensor_insert_point is None: + tensor_insert_point = len(result_levels) + ptensor, levels, _ = _tensor_levels(inp) + to_expand[i] = levels + flat_inputs[i] = ptensor + for l in levels: + if l not in index_levels: + index_levels.append(l) + else: + requires_getindex = True + result_levels.append(0) + + if tensor_insert_point is not None: + result_levels[tensor_insert_point:tensor_insert_point] = index_levels + + for i, levels in to_expand.items(): + flat_inputs[i] = _match_levels(flat_inputs[i], levels, index_levels) + + if requires_getindex: + result = _orig_getitem(ptensor_self, flat_inputs) + else: + result = ptensor_self + + next_positional = -1 + if to_pad > 0: + result_levels.extend([0] * to_pad) + for i, r in enumerate(reversed(result_levels)): + if isinstance(r, int): + result_levels[-1 - i] = next_positional + next_positional -= 1 + + return Tensor.from_positional(result, result_levels, has_device) + + +# XXX - dim is optional and can be the outer-most dimension... +def stack(tensors, new_dim, dim=0, out=None): + if isinstance(dim, int): + return torch.stack(tensors, dim, out).index(dim, new_dim) + index = None + if out is not None: + out, index = _positional_no_permute(out, dim, expand_dim=True) + ptensors = [] + for t in tensors: + pt, pi = _positional_no_permute(t, dim, expand_dim=True) + if index is not None and pi != index: + pt = pt.move_dim(pi, index) + else: + index = pi + ptensors.append(pt) + pr = torch.stack(ptensors, index, out=out) + return pr.index((index, index + 1), (new_dim, dim)) + + +_orig_split = torch.Tensor.split + + +def split(self, split_size_or_sections, dim=0): + from . import _Tensor, Dim + + if isinstance(split_size_or_sections, int) or any( + isinstance(t, int) for t in split_size_or_sections + ): + if isinstance(dim, Dim): + raise ValueError( + "when dim is specified as a Dim object, split sizes must also be dimensions." + ) + return _orig_split(self, split_size_or_sections, dim=dim) + + if isinstance(dim, Dim): + assert isinstance(self, _Tensor), f"Tensor does not have dimension {dim}" + self, dim = _positional_no_permute(self, dim) + + size = self.size(dim) + total_bound_size = 0 + unbound = [] + sizes = [] + for i, d in enumerate(split_size_or_sections): + if d.is_bound: + sizes.append(d.size) + total_bound_size += d.size + else: + sizes.append(0) + unbound.append(i) + + if unbound: + assert ( + total_bound_size <= size + ), f"result dimensions are larger than original: {total_bound_size} vs {size} ({split_size_or_sections})" + remaining_size = size - total_bound_size + chunk_size = -(-remaining_size // len(unbound)) + for u in unbound: + sz = min(chunk_size, remaining_size) + split_size_or_sections[u].size = sz + sizes[u] = sz + remaining_size -= sz + else: + assert ( + total_bound_size == size + ), f"result dimensions do not match original: {total_bound_size} vs {size} ({split_size_or_sections})" + return tuple( + t.index(dim, d) + for d, t in zip(split_size_or_sections, _orig_split(self, sizes, dim=dim)) + ) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/wrap_type.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/wrap_type.py new file mode 100644 index 0000000000000000000000000000000000000000..e2146c4a21a144dc3942e304d1406ace47df0e57 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/dim/wrap_type.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from types import ( + BuiltinMethodType, + FunctionType, + GetSetDescriptorType, + MethodDescriptorType, + WrapperDescriptorType, +) + +from functorch._C import dim as _C + +_wrap_method = _C._wrap_method + +FUNC_TYPES = ( + FunctionType, + MethodDescriptorType, + BuiltinMethodType, + WrapperDescriptorType, +) +PROPERTY_TYPES = (GetSetDescriptorType, property) + + +def _py_wrap_method(orig, __torch_function__): + def impl(*args, **kwargs): + return __torch_function__(orig, None, args, kwargs) + + return impl + + +def wrap_type(use_c, to_patch, pattern, __torch_function__): + if use_c: + wrap_method = _wrap_method + else: + wrap_method = _py_wrap_method + + all = {} + for t in reversed(pattern.mro()[:-1]): # skip object + all.update(t.__dict__) + + def wrap_attr(orig): + return property(wrap_method(orig.__get__, __torch_function__)) + + for name, obj in all.items(): + if name in ( + "__dict__", + "__new__", + "__init__", + "__repr__", + "__weakref__", + "__doc__", + "__module__", + "__dir__", + ): + continue + + # skip things that have been overloaded + # things that come from object like `__eq__` still need to be patched, however. + if hasattr(to_patch, name) and getattr(to_patch, name) is not getattr( + object, name, None + ): + continue + + if isinstance(obj, FUNC_TYPES): + setattr(to_patch, name, wrap_method(obj, __torch_function__)) + elif isinstance(obj, PROPERTY_TYPES): + setattr(to_patch, name, wrap_attr(obj)) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..23fc8381cc2358880f935064edd7eeff9766fec6 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__init__.py @@ -0,0 +1,6 @@ +# PyTorch forward-mode is not mature yet +from torch._functorch.apis import chunk_vmap +from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_ +from torch._functorch.eager_transforms import hessian, jacfwd, jvp + +from functorch import functionalize diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/control_flow.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/control_flow.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..76a81e9555f0a7332eba5b415f8c961e81ab9811 Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/control_flow.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/ops.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/ops.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..06a99c57fd82c07e336b66b2b4a559537555292c Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/__pycache__/ops.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/control_flow.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/control_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..e24fc6142820013002f6cbc1d6f85e7e132aade8 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/control_flow.py @@ -0,0 +1,8 @@ +from torch import cond # noqa: F401 +from torch._higher_order_ops.cond import UnsupportedAliasMutationException # noqa: F401 + +from torch._higher_order_ops.map import ( # noqa: F401 + _stack_pytree, + _unstack_pytree, + map, +) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/ops.py b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..7a502ef2b002cd824e7b67d08fccac872b313110 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/functorch/experimental/ops.py @@ -0,0 +1 @@ +from torch._ops import HigherOrderOperator # noqa: F401 diff --git a/evalkit_cambrian/lib/python3.10/site-packages/starlette/__pycache__/__init__.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/starlette/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..721bc14c9c0ea628f17573cf31d9827afd46add2 Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/starlette/__pycache__/__init__.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/starlette/__pycache__/_compat.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/starlette/__pycache__/_compat.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4cd7f78f3b8ec79670b637fd36acf4ed126e2749 Binary files /dev/null and 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b/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/authentication.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import typing + +from starlette.authentication import ( + AuthCredentials, + AuthenticationBackend, + AuthenticationError, + UnauthenticatedUser, +) +from starlette.requests import HTTPConnection +from starlette.responses import PlainTextResponse, Response +from starlette.types import ASGIApp, Receive, Scope, Send + + +class AuthenticationMiddleware: + def __init__( + self, + app: ASGIApp, + backend: AuthenticationBackend, + on_error: typing.Callable[[HTTPConnection, AuthenticationError], Response] | None = None, + ) -> None: + self.app = app + self.backend = backend + self.on_error: typing.Callable[[HTTPConnection, AuthenticationError], Response] = ( + on_error if on_error is not None else self.default_on_error + ) + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if scope["type"] not in ["http", "websocket"]: + await self.app(scope, receive, send) + return + + conn = HTTPConnection(scope) + try: + auth_result = await self.backend.authenticate(conn) + except AuthenticationError as exc: + response = self.on_error(conn, exc) + if scope["type"] == "websocket": + await send({"type": "websocket.close", "code": 1000}) + else: + await response(scope, receive, send) + return + + if auth_result is None: + auth_result = AuthCredentials(), UnauthenticatedUser() + scope["auth"], scope["user"] = auth_result + await self.app(scope, receive, send) + + @staticmethod + def default_on_error(conn: HTTPConnection, exc: Exception) -> Response: + return PlainTextResponse(str(exc), status_code=400) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/base.py b/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/base.py new file mode 100644 index 0000000000000000000000000000000000000000..f51b13f733a9739a0ebd7111b82096bf576a8b87 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/base.py @@ -0,0 +1,228 @@ +from __future__ import annotations + +import typing + +import anyio +from anyio.abc import ObjectReceiveStream, ObjectSendStream + +from starlette._utils import collapse_excgroups +from starlette.requests import ClientDisconnect, Request +from starlette.responses import AsyncContentStream, Response +from starlette.types import ASGIApp, Message, Receive, Scope, Send + +RequestResponseEndpoint = typing.Callable[[Request], typing.Awaitable[Response]] +DispatchFunction = typing.Callable[[Request, RequestResponseEndpoint], typing.Awaitable[Response]] +T = typing.TypeVar("T") + + +class _CachedRequest(Request): + """ + If the user calls Request.body() from their dispatch function + we cache the entire request body in memory and pass that to downstream middlewares, + but if they call Request.stream() then all we do is send an + empty body so that downstream things don't hang forever. + """ + + def __init__(self, scope: Scope, receive: Receive): + super().__init__(scope, receive) + self._wrapped_rcv_disconnected = False + self._wrapped_rcv_consumed = False + self._wrapped_rc_stream = self.stream() + + async def wrapped_receive(self) -> Message: + # wrapped_rcv state 1: disconnected + if self._wrapped_rcv_disconnected: + # we've already sent a disconnect to the downstream app + # we don't need to wait to get another one + # (although most ASGI servers will just keep sending it) + return {"type": "http.disconnect"} + # wrapped_rcv state 1: consumed but not yet disconnected + if self._wrapped_rcv_consumed: + # since the downstream app has consumed us all that is left + # is to send it a disconnect + if self._is_disconnected: + # the middleware has already seen the disconnect + # since we know the client is disconnected no need to wait + # for the message + self._wrapped_rcv_disconnected = True + return {"type": "http.disconnect"} + # we don't know yet if the client is disconnected or not + # so we'll wait until we get that message + msg = await self.receive() + if msg["type"] != "http.disconnect": # pragma: no cover + # at this point a disconnect is all that we should be receiving + # if we get something else, things went wrong somewhere + raise RuntimeError(f"Unexpected message received: {msg['type']}") + self._wrapped_rcv_disconnected = True + return msg + + # wrapped_rcv state 3: not yet consumed + if getattr(self, "_body", None) is not None: + # body() was called, we return it even if the client disconnected + self._wrapped_rcv_consumed = True + return { + "type": "http.request", + "body": self._body, + "more_body": False, + } + elif self._stream_consumed: + # stream() was called to completion + # return an empty body so that downstream apps don't hang + # waiting for a disconnect + self._wrapped_rcv_consumed = True + return { + "type": "http.request", + "body": b"", + "more_body": False, + } + else: + # body() was never called and stream() wasn't consumed + try: + stream = self.stream() + chunk = await stream.__anext__() + self._wrapped_rcv_consumed = self._stream_consumed + return { + "type": "http.request", + "body": chunk, + "more_body": not self._stream_consumed, + } + except ClientDisconnect: + self._wrapped_rcv_disconnected = True + return {"type": "http.disconnect"} + + +class BaseHTTPMiddleware: + def __init__(self, app: ASGIApp, dispatch: DispatchFunction | None = None) -> None: + self.app = app + self.dispatch_func = self.dispatch if dispatch is None else dispatch + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if scope["type"] != "http": + await self.app(scope, receive, send) + return + + request = _CachedRequest(scope, receive) + wrapped_receive = request.wrapped_receive + response_sent = anyio.Event() + + async def call_next(request: Request) -> Response: + app_exc: Exception | None = None + send_stream: ObjectSendStream[typing.MutableMapping[str, typing.Any]] + recv_stream: ObjectReceiveStream[typing.MutableMapping[str, typing.Any]] + send_stream, recv_stream = anyio.create_memory_object_stream() + + async def receive_or_disconnect() -> Message: + if response_sent.is_set(): + return {"type": "http.disconnect"} + + async with anyio.create_task_group() as task_group: + + async def wrap(func: typing.Callable[[], typing.Awaitable[T]]) -> T: + result = await func() + task_group.cancel_scope.cancel() + return result + + task_group.start_soon(wrap, response_sent.wait) + message = await wrap(wrapped_receive) + + if response_sent.is_set(): + return {"type": "http.disconnect"} + + return message + + async def close_recv_stream_on_response_sent() -> None: + await response_sent.wait() + recv_stream.close() + + async def send_no_error(message: Message) -> None: + try: + await send_stream.send(message) + except anyio.BrokenResourceError: + # recv_stream has been closed, i.e. response_sent has been set. + return + + async def coro() -> None: + nonlocal app_exc + + async with send_stream: + try: + await self.app(scope, receive_or_disconnect, send_no_error) + except Exception as exc: + app_exc = exc + + task_group.start_soon(close_recv_stream_on_response_sent) + task_group.start_soon(coro) + + try: + message = await recv_stream.receive() + info = message.get("info", None) + if message["type"] == "http.response.debug" and info is not None: + message = await recv_stream.receive() + except anyio.EndOfStream: + if app_exc is not None: + raise app_exc + raise RuntimeError("No response returned.") + + assert message["type"] == "http.response.start" + + async def body_stream() -> typing.AsyncGenerator[bytes, None]: + async with recv_stream: + async for message in recv_stream: + assert message["type"] == "http.response.body" + body = message.get("body", b"") + if body: + yield body + if not message.get("more_body", False): + break + + if app_exc is not None: + raise app_exc + + response = _StreamingResponse(status_code=message["status"], content=body_stream(), info=info) + response.raw_headers = message["headers"] + return response + + with collapse_excgroups(): + async with anyio.create_task_group() as task_group: + response = await self.dispatch_func(request, call_next) + await response(scope, wrapped_receive, send) + response_sent.set() + + async def dispatch(self, request: Request, call_next: RequestResponseEndpoint) -> Response: + raise NotImplementedError() # pragma: no cover + + +class _StreamingResponse(Response): + def __init__( + self, + content: AsyncContentStream, + status_code: int = 200, + headers: typing.Mapping[str, str] | None = None, + media_type: str | None = None, + info: typing.Mapping[str, typing.Any] | None = None, + ) -> None: + self.info = info + self.body_iterator = content + self.status_code = status_code + self.media_type = media_type + self.init_headers(headers) + self.background = None + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if self.info is not None: + await send({"type": "http.response.debug", "info": self.info}) + await send( + { + "type": "http.response.start", + "status": self.status_code, + "headers": self.raw_headers, + } + ) + + async for chunk in self.body_iterator: + await send({"type": "http.response.body", "body": chunk, "more_body": True}) + + await send({"type": "http.response.body", "body": b"", "more_body": False}) + + if self.background: + await self.background() diff --git a/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/errors.py b/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..76ad776be2272b6ee6ad1e5cd948762465bf0dcc --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/starlette/middleware/errors.py @@ -0,0 +1,260 @@ +from __future__ import annotations + +import html +import inspect +import sys +import traceback +import typing + +from starlette._utils import is_async_callable +from starlette.concurrency import run_in_threadpool +from starlette.requests import Request +from starlette.responses import HTMLResponse, PlainTextResponse, Response +from starlette.types import ASGIApp, Message, Receive, Scope, Send + +STYLES = """ +p { + color: #211c1c; +} +.traceback-container { + border: 1px solid #038BB8; +} +.traceback-title { + background-color: #038BB8; + color: lemonchiffon; + padding: 12px; + font-size: 20px; + margin-top: 0px; +} +.frame-line { + padding-left: 10px; + font-family: monospace; +} +.frame-filename { + font-family: monospace; +} +.center-line { + background-color: #038BB8; + color: #f9f6e1; + padding: 5px 0px 5px 5px; +} +.lineno { + margin-right: 5px; +} +.frame-title { + font-weight: unset; + padding: 10px 10px 10px 10px; + background-color: #E4F4FD; + margin-right: 10px; + color: #191f21; + font-size: 17px; + border: 1px solid #c7dce8; +} +.collapse-btn { + float: right; + padding: 0px 5px 1px 5px; + border: solid 1px #96aebb; + cursor: pointer; +} +.collapsed { + display: none; +} +.source-code { + font-family: courier; + font-size: small; + padding-bottom: 10px; +} +""" + +JS = """ + +""" + +TEMPLATE = """ + +
+ +Traceback
+File {frame_filename}, + line {frame_lineno}, + in {frame_name} + {collapse_button} +
++{lineno}. {line}
+""" + +CENTER_LINE = """ ++{lineno}. {line}
+""" + + +class ServerErrorMiddleware: + """ + Handles returning 500 responses when a server error occurs. + + If 'debug' is set, then traceback responses will be returned, + otherwise the designated 'handler' will be called. + + This middleware class should generally be used to wrap *everything* + else up, so that unhandled exceptions anywhere in the stack + always result in an appropriate 500 response. + """ + + def __init__( + self, + app: ASGIApp, + handler: typing.Callable[[Request, Exception], typing.Any] | None = None, + debug: bool = False, + ) -> None: + self.app = app + self.handler = handler + self.debug = debug + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if scope["type"] != "http": + await self.app(scope, receive, send) + return + + response_started = False + + async def _send(message: Message) -> None: + nonlocal response_started, send + + if message["type"] == "http.response.start": + response_started = True + await send(message) + + try: + await self.app(scope, receive, _send) + except Exception as exc: + request = Request(scope) + if self.debug: + # In debug mode, return traceback responses. + response = self.debug_response(request, exc) + elif self.handler is None: + # Use our default 500 error handler. + response = self.error_response(request, exc) + else: + # Use an installed 500 error handler. + if is_async_callable(self.handler): + response = await self.handler(request, exc) + else: + response = await run_in_threadpool(self.handler, request, exc) + + if not response_started: + await response(scope, receive, send) + + # We always continue to raise the exception. + # This allows servers to log the error, or allows test clients + # to optionally raise the error within the test case. + raise exc + + def format_line(self, index: int, line: str, frame_lineno: int, frame_index: int) -> str: + values = { + # HTML escape - line could contain < or > + "line": html.escape(line).replace(" ", " "), + "lineno": (frame_lineno - frame_index) + index, + } + + if index != frame_index: + return LINE.format(**values) + return CENTER_LINE.format(**values) + + def generate_frame_html(self, frame: inspect.FrameInfo, is_collapsed: bool) -> str: + code_context = "".join( + self.format_line( + index, + line, + frame.lineno, + frame.index, # type: ignore[arg-type] + ) + for index, line in enumerate(frame.code_context or []) + ) + + values = { + # HTML escape - filename could contain < or >, especially if it's a virtual + # file e.g.