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venv/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/LICENSE

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+Apache License
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venv/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: aiosignal
+Version: 1.3.1
+Summary: aiosignal: a list of registered asynchronous callbacks
+Home-page: https://github.com/aio-libs/aiosignal
+Maintainer: aiohttp team <[email protected]>
+Maintainer-email: [email protected]
+License: Apache 2.0
+Project-URL: Chat: Gitter, https://gitter.im/aio-libs/Lobby
+Project-URL: CI: GitHub Actions, https://github.com/aio-libs/aiosignal/actions
+Project-URL: Coverage: codecov, https://codecov.io/github/aio-libs/aiosignal
+Project-URL: Docs: RTD, https://docs.aiosignal.org
+Project-URL: GitHub: issues, https://github.com/aio-libs/aiosignal/issues
+Project-URL: GitHub: repo, https://github.com/aio-libs/aiosignal
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Framework :: AsyncIO
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: frozenlist (>=1.1.0)
+
+=========
+aiosignal
+=========
+
+.. image:: https://github.com/aio-libs/aiosignal/workflows/CI/badge.svg
+   :target: https://github.com/aio-libs/aiosignal/actions?query=workflow%3ACI
+   :alt: GitHub status for master branch
+
+.. image:: https://codecov.io/gh/aio-libs/aiosignal/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/aio-libs/aiosignal
+   :alt: codecov.io status for master branch
+
+.. image:: https://badge.fury.io/py/aiosignal.svg
+   :target: https://pypi.org/project/aiosignal
+   :alt: Latest PyPI package version
+
+.. image:: https://readthedocs.org/projects/aiosignal/badge/?version=latest
+   :target: https://aiosignal.readthedocs.io/
+   :alt: Latest Read The Docs
+
+.. image:: https://img.shields.io/discourse/topics?server=https%3A%2F%2Faio-libs.discourse.group%2F
+   :target: https://aio-libs.discourse.group/
+   :alt: Discourse group for io-libs
+
+.. image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/aio-libs/Lobby
+   :alt: Chat on Gitter
+
+Introduction
+============
+
+A project to manage callbacks in `asyncio` projects.
+
+``Signal`` is a list of registered asynchronous callbacks.
+
+The signal's life-cycle has two stages: after creation its content
+could be filled by using standard list operations: ``sig.append()``
+etc.
+
+After you call ``sig.freeze()`` the signal is *frozen*: adding, removing
+and dropping callbacks is forbidden.
+
+The only available operation is calling the previously registered
+callbacks by using ``await sig.send(data)``.
+
+For concrete usage examples see the `Signals
+<https://docs.aiohttp.org/en/stable/web_advanced.html#aiohttp-web-signals>
+section of the `Web Server Advanced
+<https://docs.aiohttp.org/en/stable/web_advanced.html>` chapter of the `aiohttp
+documentation`_.
+
+
+Installation
+------------
+
+::
+
+   $ pip install aiosignal
+
+The library requires Python 3.6 or newer.
+
+
+Documentation
+=============
+
+https://aiosignal.readthedocs.io/
+
+Communication channels
+======================
+
+*gitter chat* https://gitter.im/aio-libs/Lobby
+
+Requirements
+============
+
+- Python >= 3.6
+- frozenlist >= 1.0.0
+
+License
+=======
+
+``aiosignal`` is offered under the Apache 2 license.
+
+Source code
+===========
+
+The project is hosted on GitHub_
+
+Please file an issue in the `bug tracker
+<https://github.com/aio-libs/aiosignal/issues>`_ if you have found a bug
+or have some suggestions to improve the library.
+
+.. _GitHub: https://github.com/aio-libs/aiosignal
+.. _aiohttp documentation: https://docs.aiohttp.org/

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+Project-URL: Homepage, https://github.com/tox-dev/py-filelock
+Project-URL: Source, https://github.com/tox-dev/py-filelock
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+# filelock
+
+[![PyPI](https://img.shields.io/pypi/v/filelock)](https://pypi.org/project/filelock/)
+[![Supported Python
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+[![Documentation
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+[![Code style:
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+[![Downloads](https://static.pepy.tech/badge/filelock/month)](https://pepy.tech/project/filelock)
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venv/lib/python3.10/site-packages/filelock-3.13.4.dist-info/licenses/LICENSE

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+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+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 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.
+
+For more information, please refer to <http://unlicense.org>

venv/lib/python3.10/site-packages/functorch/__init__.py

@@ -0,0 +1,38 @@
+# 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.
+import torch
+
+from torch._functorch.deprecated import (
+    combine_state_for_ensemble,
+    functionalize,
+    grad,
+    grad_and_value,
+    hessian,
+    jacfwd,
+    jacrev,
+    jvp,
+    make_functional,
+    make_functional_with_buffers,
+    vjp,
+    vmap,
+)
+
+# utilities. Maybe these should go in their own namespace in the future?
+from torch._functorch.make_functional import (
+    FunctionalModule,
+    FunctionalModuleWithBuffers,
+)
+
+# Top-level APIs. Please think carefully before adding something to the
+# top-level namespace:
+# - private helper functions should go into torch._functorch
+# - very experimental things should go into functorch.experimental
+# - compilation related things should go into functorch.compile
+
+# Was never documented
+from torch._functorch.python_key import make_fx
+
+__version__ = torch.__version__

venv/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

venv/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 = dict.fromkeys(op_properties.pointwise, True)
+
+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

venv/lib/python3.10/site-packages/functorch/dim/batch_tensor.py

@@ -0,0 +1,25 @@
+# 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 contextlib import contextmanager
+
+from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers
+
+_enabled = False
+
+
+@contextmanager
+def _enable_layers(dims):
+    global _enabled
+    assert not _enabled
+    input = sorted((d._level, d.size) for d in dims if not isinstance(d, int))
+    n = len(input)
+    try:
+        _vmap_add_layers(input)
+        _enabled = True
+        yield
+    finally:
+        _enabled = False
+        _vmap_remove_layers(n)

venv/lib/python3.10/site-packages/functorch/dim/delayed_mul_tensor.py

@@ -0,0 +1,77 @@
+# 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.
+import torch
+
+from . import _Tensor, Tensor
+from .reference import _dims, _enable_layers, llist, ltuple
+
+
+class DelayedMulTensor(_Tensor):
+    def __init__(self, lhs, rhs):
+        self._lhs, self._rhs = lhs, rhs
+        self._data = None
+        self._levels_data = None
+        self._has_device = lhs._has_device or rhs._has_device
+        self._batchtensor_data = None
+        self._tensor_data = None
+
+    @property
+    def _levels(self):
+        if self._levels_data is None:
+            levels = llist(self._lhs._levels)
+            for l in self._rhs._levels:
+                if l not in levels:
+                    levels.append(l)
+            self._levels_data = ltuple(levels)
+        return self._levels_data
+
+    @property
+    def _batchtensor(self):
+        if self._batchtensor_data is None:
+            with _enable_layers(self._levels):
+                print("bt multiply fallback")
+                self._batchtensor_data = self._lhs._batchtensor * self._rhs._batchtensor
+        return self._batchtensor_data
+
+    @property
+    def _tensor(self):
+        if self._tensor_data is None:
+            self._tensor_data = Tensor.from_batched(
+                self._batchtensor, self._has_device
+            )._tensor
+        return self._tensor_data
+
+    @property
+    def ndim(self):
+        return self._batchtensor.ndim
+
+    @property
+    def dims(self):
+        return ltuple(super().dims)
+
+    def sum(self, dim):
+        dims = _dims(dim, 0, False, False)
+        n = ord("a")
+        all_levels = self._levels
+
+        def to_char(d):
+            return chr(n + all_levels.index(d))
+
+        plhs, levelslhs = self._lhs._tensor, self._lhs._levels
+        prhs, levelsrhs = self._rhs._tensor, self._rhs._levels
+        new_dims = tuple(d for d in self.dims if d not in dims)
+        new_levels = [l for l in self._levels if l not in dims]
+        fmt = "".join(
+            [
+                *(to_char(d) for d in levelslhs),
+                ",",
+                *(to_char(d) for d in levelsrhs),
+                "->",
+                *(to_char(d) for d in new_levels),
+            ]
+        )
+        result_data = torch.einsum(fmt, (plhs, prhs))
+        return Tensor.from_positional(result_data, new_levels, True)

venv/lib/python3.10/site-packages/functorch/dim/magic_trace.py

@@ -0,0 +1,42 @@
+# 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.
+import os
+import signal
+import subprocess
+from contextlib import contextmanager
+
+
+@contextmanager
+def magic_trace(output="trace.fxt", magic_trace_cache="/tmp/magic-trace"):
+    pid = os.getpid()
+    if not os.path.exists(magic_trace_cache):
+        print(f"Downloading magic_trace to: {magic_trace_cache}")
+        subprocess.run(
+            [
+                "wget",
+                "-O",
+                magic_trace_cache,
+                "-q",
+                "https://github.com/janestreet/magic-trace/releases/download/v1.0.2/magic-trace",
+            ]
+        )
+        subprocess.run(["chmod", "+x", magic_trace_cache])
+    args = [magic_trace_cache, "attach", "-pid", str(pid), "-o", output]
+    p = subprocess.Popen(args, stderr=subprocess.PIPE, encoding="utf-8")
+    while True:
+        x = p.stderr.readline()
+        print(x)
+        if "Attached" in x:
+            break
+    try:
+        yield
+    finally:
+        p.send_signal(signal.SIGINT)
+        r = p.wait()
+        print(p.stderr.read())
+        p.stderr.close()
+        if r != 0:
+            raise ValueError(f"magic_trace exited abnormally: {r}")

venv/lib/python3.10/site-packages/functorch/dim/op_properties.py

@@ -0,0 +1,311 @@
+# 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.
+import torch
+
+# pointwise operators can go through a faster pathway
+
+tensor_magic_methods = ["add", ""]
+pointwise_magic_methods_with_reverse = (
+    "add",
+    "sub",
+    "mul",
+    "floordiv",
+    "div",
+    "truediv",
+    "mod",
+    "pow",
+    "lshift",
+    "rshift",
+    "and",
+    "or",
+    "xor",
+)
+pointwise_magic_methods = (
+    *(x for m in pointwise_magic_methods_with_reverse for x in (m, "r" + m)),
+    "eq",
+    "gt",
+    "le",
+    "lt",
+    "ge",
+    "gt",
+    "ne",
+    "neg",
+    "pos",
+    "abs",
+    "invert",
+    "iadd",
+    "isub",
+    "imul",
+    "ifloordiv",
+    "idiv",
+    "itruediv",
+    "imod",
+    "ipow",
+    "ilshift",
+    "irshift",
+    "iand",
+    "ior",
+    "ixor",
+    "int",
+    "long",
+    "float",
+    "complex",
+)
+
+pointwise_methods = (*(f"__{m}__" for m in pointwise_magic_methods),)
+
+pointwise = (
+    *(getattr(torch.Tensor, m) for m in pointwise_methods),
+    torch.nn.functional.dropout,
+    torch.where,
+    torch.Tensor.abs,
+    torch.abs,
+    torch.Tensor.acos,
+    torch.acos,
+    torch.Tensor.acosh,
+    torch.acosh,
+    torch.Tensor.add,
+    torch.add,
+    torch.Tensor.addcdiv,
+    torch.addcdiv,
+    torch.Tensor.addcmul,
+    torch.addcmul,
+    torch.Tensor.addr,
+    torch.addr,
+    torch.Tensor.angle,
+    torch.angle,
+    torch.Tensor.asin,
+    torch.asin,
+    torch.Tensor.asinh,
+    torch.asinh,
+    torch.Tensor.atan,
+    torch.atan,
+    torch.Tensor.atan2,
+    torch.atan2,
+    torch.Tensor.atanh,
+    torch.atanh,
+    torch.Tensor.bitwise_and,
+    torch.bitwise_and,
+    torch.Tensor.bitwise_left_shift,
+    torch.bitwise_left_shift,
+    torch.Tensor.bitwise_not,
+    torch.bitwise_not,
+    torch.Tensor.bitwise_or,
+    torch.bitwise_or,
+    torch.Tensor.bitwise_right_shift,
+    torch.bitwise_right_shift,
+    torch.Tensor.bitwise_xor,
+    torch.bitwise_xor,
+    torch.Tensor.ceil,
+    torch.ceil,
+    torch.celu,
+    torch.nn.functional.celu,
+    torch.Tensor.clamp,
+    torch.clamp,
+    torch.Tensor.clamp_max,
+    torch.clamp_max,
+    torch.Tensor.clamp_min,
+    torch.clamp_min,
+    torch.Tensor.copysign,
+    torch.copysign,
+    torch.Tensor.cos,
+    torch.cos,
+    torch.Tensor.cosh,
+    torch.cosh,
+    torch.Tensor.deg2rad,
+    torch.deg2rad,
+    torch.Tensor.digamma,
+    torch.digamma,
+    torch.Tensor.div,
+    torch.div,
+    torch.dropout,
+    torch.nn.functional.dropout,
+    torch.nn.functional.elu,
+    torch.Tensor.eq,
+    torch.eq,
+    torch.Tensor.erf,
+    torch.erf,
+    torch.Tensor.erfc,
+    torch.erfc,
+    torch.Tensor.erfinv,
+    torch.erfinv,
+    torch.Tensor.exp,
+    torch.exp,
+    torch.Tensor.exp2,
+    torch.exp2,
+    torch.Tensor.expm1,
+    torch.expm1,
+    torch.feature_dropout,
+    torch.Tensor.float_power,
+    torch.float_power,
+    torch.Tensor.floor,
+    torch.floor,
+    torch.Tensor.floor_divide,
+    torch.floor_divide,
+    torch.Tensor.fmod,
+    torch.fmod,
+    torch.Tensor.frac,
+    torch.frac,
+    torch.Tensor.frexp,
+    torch.frexp,
+    torch.Tensor.gcd,
+    torch.gcd,
+    torch.Tensor.ge,
+    torch.ge,
+    torch.nn.functional.gelu,
+    torch.nn.functional.glu,
+    torch.Tensor.gt,
+    torch.gt,
+    torch.Tensor.hardshrink,
+    torch.hardshrink,
+    torch.nn.functional.hardshrink,
+    torch.nn.functional.hardsigmoid,
+    torch.nn.functional.hardswish,
+    torch.nn.functional.hardtanh,
+    torch.Tensor.heaviside,
+    torch.heaviside,
+    torch.Tensor.hypot,
+    torch.hypot,
+    torch.Tensor.i0,
+    torch.i0,
+    torch.Tensor.igamma,
+    torch.igamma,
+    torch.Tensor.igammac,
+    torch.igammac,
+    torch.Tensor.isclose,
+    torch.isclose,
+    torch.Tensor.isfinite,
+    torch.isfinite,
+    torch.Tensor.isinf,
+    torch.isinf,
+    torch.Tensor.isnan,
+    torch.isnan,
+    torch.Tensor.isneginf,
+    torch.isneginf,
+    torch.Tensor.isposinf,
+    torch.isposinf,
+    torch.Tensor.isreal,
+    torch.isreal,
+    torch.Tensor.kron,
+    torch.kron,
+    torch.Tensor.lcm,
+    torch.lcm,
+    torch.Tensor.ldexp,
+    torch.ldexp,
+    torch.Tensor.le,
+    torch.le,
+    torch.nn.functional.leaky_relu,
+    torch.Tensor.lerp,
+    torch.lerp,
+    torch.Tensor.lgamma,
+    torch.lgamma,
+    torch.Tensor.log,
+    torch.log,
+    torch.Tensor.log10,
+    torch.log10,
+    torch.Tensor.log1p,
+    torch.log1p,
+    torch.Tensor.log2,
+    torch.log2,
+    torch.nn.functional.logsigmoid,
+    torch.Tensor.logical_and,
+    torch.logical_and,
+    torch.Tensor.logical_not,
+    torch.logical_not,
+    torch.Tensor.logical_or,
+    torch.logical_or,
+    torch.Tensor.logical_xor,
+    torch.logical_xor,
+    torch.Tensor.logit,
+    torch.logit,
+    torch.Tensor.lt,
+    torch.lt,
+    torch.Tensor.maximum,
+    torch.maximum,
+    torch.Tensor.minimum,
+    torch.minimum,
+    torch.nn.functional.mish,
+    torch.Tensor.mvlgamma,
+    torch.mvlgamma,
+    torch.Tensor.nan_to_num,
+    torch.nan_to_num,
+    torch.Tensor.ne,
+    torch.ne,
+    torch.Tensor.neg,
+    torch.neg,
+    torch.Tensor.nextafter,
+    torch.nextafter,
+    torch.Tensor.outer,
+    torch.outer,
+    torch.polar,
+    torch.Tensor.polygamma,
+    torch.polygamma,
+    torch.Tensor.positive,
+    torch.positive,
+    torch.Tensor.pow,
+    torch.pow,
+    torch.Tensor.prelu,
+    torch.prelu,
+    torch.nn.functional.prelu,
+    torch.Tensor.rad2deg,
+    torch.rad2deg,
+    torch.Tensor.reciprocal,
+    torch.reciprocal,
+    torch.Tensor.relu,
+    torch.relu,
+    torch.nn.functional.relu,
+    torch.nn.functional.relu6,
+    torch.Tensor.remainder,
+    torch.remainder,
+    torch.Tensor.round,
+    torch.round,
+    torch.rrelu,
+    torch.nn.functional.rrelu,
+    torch.Tensor.rsqrt,
+    torch.rsqrt,
+    torch.rsub,
+    torch.selu,
+    torch.nn.functional.selu,
+    torch.Tensor.sgn,
+    torch.sgn,
+    torch.Tensor.sigmoid,
+    torch.sigmoid,
+    torch.nn.functional.sigmoid,
+    torch.Tensor.sign,
+    torch.sign,
+    torch.Tensor.signbit,
+    torch.signbit,
+    torch.nn.functional.silu,
+    torch.Tensor.sin,
+    torch.sin,
+    torch.Tensor.sinc,
+    torch.sinc,
+    torch.Tensor.sinh,
+    torch.sinh,
+    torch.nn.functional.softplus,
+    torch.nn.functional.softshrink,
+    torch.Tensor.sqrt,
+    torch.sqrt,
+    torch.Tensor.square,
+    torch.square,
+    torch.Tensor.sub,
+    torch.sub,
+    torch.Tensor.tan,
+    torch.tan,
+    torch.Tensor.tanh,
+    torch.tanh,
+    torch.nn.functional.tanh,
+    torch.threshold,
+    torch.nn.functional.threshold,
+    torch.trapz,
+    torch.Tensor.true_divide,
+    torch.true_divide,
+    torch.Tensor.trunc,
+    torch.trunc,
+    torch.Tensor.xlogy,
+    torch.xlogy,
+    torch.rand_like,
+)

venv/lib/python3.10/site-packages/multidict-6.0.5.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

venv/lib/python3.10/site-packages/multidict-6.0.5.dist-info/LICENSE

@@ -0,0 +1,13 @@
+   Copyright 2016 Andrew Svetlov and aio-libs contributors
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

venv/lib/python3.10/site-packages/multidict-6.0.5.dist-info/METADATA

@@ -0,0 +1,132 @@
+Metadata-Version: 2.1
+Name: multidict
+Version: 6.0.5
+Summary: multidict implementation
+Home-page: https://github.com/aio-libs/multidict
+Author: Andrew Svetlov
+Author-email: [email protected]
+License: Apache 2
+Project-URL: Chat: Gitter, https://gitter.im/aio-libs/Lobby
+Project-URL: CI: GitHub, https://github.com/aio-libs/multidict/actions
+Project-URL: Coverage: codecov, https://codecov.io/github/aio-libs/multidict
+Project-URL: Docs: RTD, https://multidict.aio-libs.org
+Project-URL: GitHub: issues, https://github.com/aio-libs/multidict/issues
+Project-URL: GitHub: repo, https://github.com/aio-libs/multidict
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+
+=========
+multidict
+=========
+
+.. image:: https://github.com/aio-libs/multidict/workflows/CI/badge.svg
+   :target: https://github.com/aio-libs/multidict/actions?query=workflow%3ACI
+   :alt: GitHub status for master branch
+
+.. image:: https://codecov.io/gh/aio-libs/multidict/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/aio-libs/multidict
+   :alt: Coverage metrics
+
+.. image:: https://img.shields.io/pypi/v/multidict.svg
+   :target: https://pypi.org/project/multidict
+   :alt: PyPI
+
+.. image:: https://readthedocs.org/projects/multidict/badge/?version=latest
+   :target: http://multidict.aio-libs.org/en/latest/?badge=latest
+   :alt: Documentation
+
+.. image:: https://img.shields.io/pypi/pyversions/multidict.svg
+   :target: https://pypi.org/project/multidict
+   :alt: Python versions
+
+.. image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/aio-libs/Lobby
+   :alt: Chat on Gitter
+
+Multidict is dict-like collection of *key-value pairs* where key
+might occur more than once in the container.
+
+Introduction
+------------
+
+*HTTP Headers* and *URL query string* require specific data structure:
+*multidict*. It behaves mostly like a regular ``dict`` but it may have
+several *values* for the same *key* and *preserves insertion ordering*.
+
+The *key* is ``str`` (or ``istr`` for case-insensitive dictionaries).
+
+``multidict`` has four multidict classes:
+``MultiDict``, ``MultiDictProxy``, ``CIMultiDict``
+and ``CIMultiDictProxy``.
+
+Immutable proxies (``MultiDictProxy`` and
+``CIMultiDictProxy``) provide a dynamic view for the
+proxied multidict, the view reflects underlying collection changes. They
+implement the ``collections.abc.Mapping`` interface.
+
+Regular mutable (``MultiDict`` and ``CIMultiDict``) classes
+implement ``collections.abc.MutableMapping`` and allows them to change
+their own content.
+
+
+*Case insensitive* (``CIMultiDict`` and
+``CIMultiDictProxy``) assume the *keys* are case
+insensitive, e.g.::
+
+   >>> dct = CIMultiDict(key='val')
+   >>> 'Key' in dct
+   True
+   >>> dct['Key']
+   'val'
+
+*Keys* should be ``str`` or ``istr`` instances.
+
+The library has optional C Extensions for speed.
+
+
+License
+-------
+
+Apache 2
+
+Library Installation
+--------------------
+
+.. code-block:: bash
+
+   $ pip install multidict
+
+The library is Python 3 only!
+
+PyPI contains binary wheels for Linux, Windows and MacOS.  If you want to install
+``multidict`` on another operating system (or *Alpine Linux* inside a Docker) the
+tarball will be used to compile the library from source.  It requires a C compiler and
+Python headers to be installed.
+
+To skip the compilation, please use the `MULTIDICT_NO_EXTENSIONS` environment variable,
+e.g.:
+
+.. code-block:: bash
+
+   $ MULTIDICT_NO_EXTENSIONS=1 pip install multidict
+
+Please note, the pure Python (uncompiled) version is about 20-50 times slower depending on
+the usage scenario!!!
+
+
+
+Changelog
+---------
+See `RTD page <http://multidict.aio-libs.org/en/latest/changes>`_.

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