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llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/__init__.py

@@ -0,0 +1,624 @@
+import re
+import itertools
+import textwrap
+import functools
+
+try:
+    from importlib.resources import files  # type: ignore
+except ImportError:  # pragma: nocover
+    from importlib_resources import files  # type: ignore
+
+from jaraco.functools import compose, method_cache
+from jaraco.context import ExceptionTrap
+
+
+def substitution(old, new):
+    """
+    Return a function that will perform a substitution on a string
+    """
+    return lambda s: s.replace(old, new)
+
+
+def multi_substitution(*substitutions):
+    """
+    Take a sequence of pairs specifying substitutions, and create
+    a function that performs those substitutions.
+
+    >>> multi_substitution(('foo', 'bar'), ('bar', 'baz'))('foo')
+    'baz'
+    """
+    substitutions = itertools.starmap(substitution, substitutions)
+    # compose function applies last function first, so reverse the
+    #  substitutions to get the expected order.
+    substitutions = reversed(tuple(substitutions))
+    return compose(*substitutions)
+
+
+class FoldedCase(str):
+    """
+    A case insensitive string class; behaves just like str
+    except compares equal when the only variation is case.
+
+    >>> s = FoldedCase('hello world')
+
+    >>> s == 'Hello World'
+    True
+
+    >>> 'Hello World' == s
+    True
+
+    >>> s != 'Hello World'
+    False
+
+    >>> s.index('O')
+    4
+
+    >>> s.split('O')
+    ['hell', ' w', 'rld']
+
+    >>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
+    ['alpha', 'Beta', 'GAMMA']
+
+    Sequence membership is straightforward.
+
+    >>> "Hello World" in [s]
+    True
+    >>> s in ["Hello World"]
+    True
+
+    Allows testing for set inclusion, but candidate and elements
+    must both be folded.
+
+    >>> FoldedCase("Hello World") in {s}
+    True
+    >>> s in {FoldedCase("Hello World")}
+    True
+
+    String inclusion works as long as the FoldedCase object
+    is on the right.
+
+    >>> "hello" in FoldedCase("Hello World")
+    True
+
+    But not if the FoldedCase object is on the left:
+
+    >>> FoldedCase('hello') in 'Hello World'
+    False
+
+    In that case, use ``in_``:
+
+    >>> FoldedCase('hello').in_('Hello World')
+    True
+
+    >>> FoldedCase('hello') > FoldedCase('Hello')
+    False
+
+    >>> FoldedCase('ß') == FoldedCase('ss')
+    True
+    """
+
+    def __lt__(self, other):
+        return self.casefold() < other.casefold()
+
+    def __gt__(self, other):
+        return self.casefold() > other.casefold()
+
+    def __eq__(self, other):
+        return self.casefold() == other.casefold()
+
+    def __ne__(self, other):
+        return self.casefold() != other.casefold()
+
+    def __hash__(self):
+        return hash(self.casefold())
+
+    def __contains__(self, other):
+        return super().casefold().__contains__(other.casefold())
+
+    def in_(self, other):
+        "Does self appear in other?"
+        return self in FoldedCase(other)
+
+    # cache casefold since it's likely to be called frequently.
+    @method_cache
+    def casefold(self):
+        return super().casefold()
+
+    def index(self, sub):
+        return self.casefold().index(sub.casefold())
+
+    def split(self, splitter=' ', maxsplit=0):
+        pattern = re.compile(re.escape(splitter), re.I)
+        return pattern.split(self, maxsplit)
+
+
+# Python 3.8 compatibility
+_unicode_trap = ExceptionTrap(UnicodeDecodeError)
+
+
+@_unicode_trap.passes
+def is_decodable(value):
+    r"""
+    Return True if the supplied value is decodable (using the default
+    encoding).
+
+    >>> is_decodable(b'\xff')
+    False
+    >>> is_decodable(b'\x32')
+    True
+    """
+    value.decode()
+
+
+def is_binary(value):
+    r"""
+    Return True if the value appears to be binary (that is, it's a byte
+    string and isn't decodable).
+
+    >>> is_binary(b'\xff')
+    True
+    >>> is_binary('\xff')
+    False
+    """
+    return isinstance(value, bytes) and not is_decodable(value)
+
+
+def trim(s):
+    r"""
+    Trim something like a docstring to remove the whitespace that
+    is common due to indentation and formatting.
+
+    >>> trim("\n\tfoo = bar\n\t\tbar = baz\n")
+    'foo = bar\n\tbar = baz'
+    """
+    return textwrap.dedent(s).strip()
+
+
+def wrap(s):
+    """
+    Wrap lines of text, retaining existing newlines as
+    paragraph markers.
+
+    >>> print(wrap(lorem_ipsum))
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
+    eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad
+    minim veniam, quis nostrud exercitation ullamco laboris nisi ut
+    aliquip ex ea commodo consequat. Duis aute irure dolor in
+    reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla
+    pariatur. Excepteur sint occaecat cupidatat non proident, sunt in
+    culpa qui officia deserunt mollit anim id est laborum.
+    <BLANKLINE>
+    Curabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam
+    varius, turpis et commodo pharetra, est eros bibendum elit, nec luctus
+    magna felis sollicitudin mauris. Integer in mauris eu nibh euismod
+    gravida. Duis ac tellus et risus vulputate vehicula. Donec lobortis
+    risus a elit. Etiam tempor. Ut ullamcorper, ligula eu tempor congue,
+    eros est euismod turpis, id tincidunt sapien risus a quam. Maecenas
+    fermentum consequat mi. Donec fermentum. Pellentesque malesuada nulla
+    a mi. Duis sapien sem, aliquet nec, commodo eget, consequat quis,
+    neque. Aliquam faucibus, elit ut dictum aliquet, felis nisl adipiscing
+    sapien, sed malesuada diam lacus eget erat. Cras mollis scelerisque
+    nunc. Nullam arcu. Aliquam consequat. Curabitur augue lorem, dapibus
+    quis, laoreet et, pretium ac, nisi. Aenean magna nisl, mollis quis,
+    molestie eu, feugiat in, orci. In hac habitasse platea dictumst.
+    """
+    paragraphs = s.splitlines()
+    wrapped = ('\n'.join(textwrap.wrap(para)) for para in paragraphs)
+    return '\n\n'.join(wrapped)
+
+
+def unwrap(s):
+    r"""
+    Given a multi-line string, return an unwrapped version.
+
+    >>> wrapped = wrap(lorem_ipsum)
+    >>> wrapped.count('\n')
+    20
+    >>> unwrapped = unwrap(wrapped)
+    >>> unwrapped.count('\n')
+    1
+    >>> print(unwrapped)
+    Lorem ipsum dolor sit amet, consectetur adipiscing ...
+    Curabitur pretium tincidunt lacus. Nulla gravida orci ...
+
+    """
+    paragraphs = re.split(r'\n\n+', s)
+    cleaned = (para.replace('\n', ' ') for para in paragraphs)
+    return '\n'.join(cleaned)
+
+
+lorem_ipsum: str = (
+    files(__name__).joinpath('Lorem ipsum.txt').read_text(encoding='utf-8')
+)
+
+
+class Splitter:
+    """object that will split a string with the given arguments for each call
+
+    >>> s = Splitter(',')
+    >>> s('hello, world, this is your, master calling')
+    ['hello', ' world', ' this is your', ' master calling']
+    """
+
+    def __init__(self, *args):
+        self.args = args
+
+    def __call__(self, s):
+        return s.split(*self.args)
+
+
+def indent(string, prefix=' ' * 4):
+    """
+    >>> indent('foo')
+    '    foo'
+    """
+    return prefix + string
+
+
+class WordSet(tuple):
+    """
+    Given an identifier, return the words that identifier represents,
+    whether in camel case, underscore-separated, etc.
+
+    >>> WordSet.parse("camelCase")
+    ('camel', 'Case')
+
+    >>> WordSet.parse("under_sep")
+    ('under', 'sep')
+
+    Acronyms should be retained
+
+    >>> WordSet.parse("firstSNL")
+    ('first', 'SNL')
+
+    >>> WordSet.parse("you_and_I")
+    ('you', 'and', 'I')
+
+    >>> WordSet.parse("A simple test")
+    ('A', 'simple', 'test')
+
+    Multiple caps should not interfere with the first cap of another word.
+
+    >>> WordSet.parse("myABCClass")
+    ('my', 'ABC', 'Class')
+
+    The result is a WordSet, providing access to other forms.
+
+    >>> WordSet.parse("myABCClass").underscore_separated()
+    'my_ABC_Class'
+
+    >>> WordSet.parse('a-command').camel_case()
+    'ACommand'
+
+    >>> WordSet.parse('someIdentifier').lowered().space_separated()
+    'some identifier'
+
+    Slices of the result should return another WordSet.
+
+    >>> WordSet.parse('taken-out-of-context')[1:].underscore_separated()
+    'out_of_context'
+
+    >>> WordSet.from_class_name(WordSet()).lowered().space_separated()
+    'word set'
+
+    >>> example = WordSet.parse('figured it out')
+    >>> example.headless_camel_case()
+    'figuredItOut'
+    >>> example.dash_separated()
+    'figured-it-out'
+
+    """
+
+    _pattern = re.compile('([A-Z]?[a-z]+)|([A-Z]+(?![a-z]))')
+
+    def capitalized(self):
+        return WordSet(word.capitalize() for word in self)
+
+    def lowered(self):
+        return WordSet(word.lower() for word in self)
+
+    def camel_case(self):
+        return ''.join(self.capitalized())
+
+    def headless_camel_case(self):
+        words = iter(self)
+        first = next(words).lower()
+        new_words = itertools.chain((first,), WordSet(words).camel_case())
+        return ''.join(new_words)
+
+    def underscore_separated(self):
+        return '_'.join(self)
+
+    def dash_separated(self):
+        return '-'.join(self)
+
+    def space_separated(self):
+        return ' '.join(self)
+
+    def trim_right(self, item):
+        """
+        Remove the item from the end of the set.
+
+        >>> WordSet.parse('foo bar').trim_right('foo')
+        ('foo', 'bar')
+        >>> WordSet.parse('foo bar').trim_right('bar')
+        ('foo',)
+        >>> WordSet.parse('').trim_right('bar')
+        ()
+        """
+        return self[:-1] if self and self[-1] == item else self
+
+    def trim_left(self, item):
+        """
+        Remove the item from the beginning of the set.
+
+        >>> WordSet.parse('foo bar').trim_left('foo')
+        ('bar',)
+        >>> WordSet.parse('foo bar').trim_left('bar')
+        ('foo', 'bar')
+        >>> WordSet.parse('').trim_left('bar')
+        ()
+        """
+        return self[1:] if self and self[0] == item else self
+
+    def trim(self, item):
+        """
+        >>> WordSet.parse('foo bar').trim('foo')
+        ('bar',)
+        """
+        return self.trim_left(item).trim_right(item)
+
+    def __getitem__(self, item):
+        result = super().__getitem__(item)
+        if isinstance(item, slice):
+            result = WordSet(result)
+        return result
+
+    @classmethod
+    def parse(cls, identifier):
+        matches = cls._pattern.finditer(identifier)
+        return WordSet(match.group(0) for match in matches)
+
+    @classmethod
+    def from_class_name(cls, subject):
+        return cls.parse(subject.__class__.__name__)
+
+
+# for backward compatibility
+words = WordSet.parse
+
+
+def simple_html_strip(s):
+    r"""
+    Remove HTML from the string `s`.
+
+    >>> str(simple_html_strip(''))
+    ''
+
+    >>> print(simple_html_strip('A <bold>stormy</bold> day in paradise'))
+    A stormy day in paradise
+
+    >>> print(simple_html_strip('Somebody <!-- do not --> tell the truth.'))
+    Somebody  tell the truth.
+
+    >>> print(simple_html_strip('What about<br/>\nmultiple lines?'))
+    What about
+    multiple lines?
+    """
+    html_stripper = re.compile('(<!--.*?-->)|(<[^>]*>)|([^<]+)', re.DOTALL)
+    texts = (match.group(3) or '' for match in html_stripper.finditer(s))
+    return ''.join(texts)
+
+
+class SeparatedValues(str):
+    """
+    A string separated by a separator. Overrides __iter__ for getting
+    the values.
+
+    >>> list(SeparatedValues('a,b,c'))
+    ['a', 'b', 'c']
+
+    Whitespace is stripped and empty values are discarded.
+
+    >>> list(SeparatedValues(' a,   b   , c,  '))
+    ['a', 'b', 'c']
+    """
+
+    separator = ','
+
+    def __iter__(self):
+        parts = self.split(self.separator)
+        return filter(None, (part.strip() for part in parts))
+
+
+class Stripper:
+    r"""
+    Given a series of lines, find the common prefix and strip it from them.
+
+    >>> lines = [
+    ...     'abcdefg\n',
+    ...     'abc\n',
+    ...     'abcde\n',
+    ... ]
+    >>> res = Stripper.strip_prefix(lines)
+    >>> res.prefix
+    'abc'
+    >>> list(res.lines)
+    ['defg\n', '\n', 'de\n']
+
+    If no prefix is common, nothing should be stripped.
+
+    >>> lines = [
+    ...     'abcd\n',
+    ...     '1234\n',
+    ... ]
+    >>> res = Stripper.strip_prefix(lines)
+    >>> res.prefix = ''
+    >>> list(res.lines)
+    ['abcd\n', '1234\n']
+    """
+
+    def __init__(self, prefix, lines):
+        self.prefix = prefix
+        self.lines = map(self, lines)
+
+    @classmethod
+    def strip_prefix(cls, lines):
+        prefix_lines, lines = itertools.tee(lines)
+        prefix = functools.reduce(cls.common_prefix, prefix_lines)
+        return cls(prefix, lines)
+
+    def __call__(self, line):
+        if not self.prefix:
+            return line
+        null, prefix, rest = line.partition(self.prefix)
+        return rest
+
+    @staticmethod
+    def common_prefix(s1, s2):
+        """
+        Return the common prefix of two lines.
+        """
+        index = min(len(s1), len(s2))
+        while s1[:index] != s2[:index]:
+            index -= 1
+        return s1[:index]
+
+
+def remove_prefix(text, prefix):
+    """
+    Remove the prefix from the text if it exists.
+
+    >>> remove_prefix('underwhelming performance', 'underwhelming ')
+    'performance'
+
+    >>> remove_prefix('something special', 'sample')
+    'something special'
+    """
+    null, prefix, rest = text.rpartition(prefix)
+    return rest
+
+
+def remove_suffix(text, suffix):
+    """
+    Remove the suffix from the text if it exists.
+
+    >>> remove_suffix('name.git', '.git')
+    'name'
+
+    >>> remove_suffix('something special', 'sample')
+    'something special'
+    """
+    rest, suffix, null = text.partition(suffix)
+    return rest
+
+
+def normalize_newlines(text):
+    r"""
+    Replace alternate newlines with the canonical newline.
+
+    >>> normalize_newlines('Lorem Ipsum\u2029')
+    'Lorem Ipsum\n'
+    >>> normalize_newlines('Lorem Ipsum\r\n')
+    'Lorem Ipsum\n'
+    >>> normalize_newlines('Lorem Ipsum\x85')
+    'Lorem Ipsum\n'
+    """
+    newlines = ['\r\n', '\r', '\n', '\u0085', '\u2028', '\u2029']
+    pattern = '|'.join(newlines)
+    return re.sub(pattern, '\n', text)
+
+
+def _nonblank(str):
+    return str and not str.startswith('#')
+
+
+@functools.singledispatch
+def yield_lines(iterable):
+    r"""
+    Yield valid lines of a string or iterable.
+
+    >>> list(yield_lines(''))
+    []
+    >>> list(yield_lines(['foo', 'bar']))
+    ['foo', 'bar']
+    >>> list(yield_lines('foo\nbar'))
+    ['foo', 'bar']
+    >>> list(yield_lines('\nfoo\n#bar\nbaz #comment'))
+    ['foo', 'baz #comment']
+    >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n']))
+    ['foo', 'bar', 'baz', 'bing']
+    """
+    return itertools.chain.from_iterable(map(yield_lines, iterable))
+
+
+@yield_lines.register(str)
+def _(text):
+    return filter(_nonblank, map(str.strip, text.splitlines()))
+
+
+def drop_comment(line):
+    """
+    Drop comments.
+
+    >>> drop_comment('foo # bar')
+    'foo'
+
+    A hash without a space may be in a URL.
+
+    >>> drop_comment('http://example.com/foo#bar')
+    'http://example.com/foo#bar'
+    """
+    return line.partition(' #')[0]
+
+
+def join_continuation(lines):
+    r"""
+    Join lines continued by a trailing backslash.
+
+    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
+    ['foobar', 'baz']
+    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
+    ['foobar', 'baz']
+    >>> list(join_continuation(['foo \\', 'bar \\', 'baz']))
+    ['foobarbaz']
+
+    Not sure why, but...
+    The character preceding the backslash is also elided.
+
+    >>> list(join_continuation(['goo\\', 'dly']))
+    ['godly']
+
+    A terrible idea, but...
+    If no line is available to continue, suppress the lines.
+
+    >>> list(join_continuation(['foo', 'bar\\', 'baz\\']))
+    ['foo']
+    """
+    lines = iter(lines)
+    for item in lines:
+        while item.endswith('\\'):
+            try:
+                item = item[:-2].strip() + next(lines)
+            except StopIteration:
+                return
+        yield item
+
+
+def read_newlines(filename, limit=1024):
+    r"""
+    >>> tmp_path = getfixture('tmp_path')
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\n', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    '\n'
+    >>> _ = filename.write_text('foo\r\n', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    '\r\n'
+    >>> _ = filename.write_text('foo\r\nbar\nbing\r', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    ('\r', '\n', '\r\n')
+    """
+    with open(filename, encoding='utf-8') as fp:
+        fp.read(limit)
+    return fp.newlines

llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/layouts.py

@@ -0,0 +1,25 @@
+qwerty = "-=qwertyuiop[]asdfghjkl;'zxcvbnm,./_+QWERTYUIOP{}ASDFGHJKL:\"ZXCVBNM<>?"
+dvorak = "[]',.pyfgcrl/=aoeuidhtns-;qjkxbmwvz{}\"<>PYFGCRL?+AOEUIDHTNS_:QJKXBMWVZ"
+
+
+to_dvorak = str.maketrans(qwerty, dvorak)
+to_qwerty = str.maketrans(dvorak, qwerty)
+
+
+def translate(input, translation):
+    """
+    >>> translate('dvorak', to_dvorak)
+    'ekrpat'
+    >>> translate('qwerty', to_qwerty)
+    'x,dokt'
+    """
+    return input.translate(translation)
+
+
+def _translate_stream(stream, translation):
+    """
+    >>> import io
+    >>> _translate_stream(io.StringIO('foo'), to_dvorak)
+    urr
+    """
+    print(translate(stream.read(), translation))

llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/show-newlines.py

@@ -0,0 +1,33 @@
+import autocommand
+import inflect
+
+from more_itertools import always_iterable
+
+import jaraco.text
+
+
+def report_newlines(filename):
+    r"""
+    Report the newlines in the indicated file.
+
+    >>> tmp_path = getfixture('tmp_path')
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\nbar\n', newline='', encoding='utf-8')
+    >>> report_newlines(filename)
+    newline is '\n'
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\nbar\r\n', newline='', encoding='utf-8')
+    >>> report_newlines(filename)
+    newlines are ('\n', '\r\n')
+    """
+    newlines = jaraco.text.read_newlines(filename)
+    count = len(tuple(always_iterable(newlines)))
+    engine = inflect.engine()
+    print(
+        engine.plural_noun("newline", count),
+        engine.plural_verb("is", count),
+        repr(newlines),
+    )
+
+
+autocommand.autocommand(__name__)(report_newlines)

llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/strip-prefix.py

@@ -0,0 +1,21 @@
+import sys
+
+import autocommand
+
+from jaraco.text import Stripper
+
+
+def strip_prefix():
+    r"""
+    Strip any common prefix from stdin.
+
+    >>> import io, pytest
+    >>> getfixture('monkeypatch').setattr('sys.stdin', io.StringIO('abcdef\nabc123'))
+    >>> strip_prefix()
+    def
+    123
+    """
+    sys.stdout.writelines(Stripper.strip_prefix(sys.stdin).lines)
+
+
+autocommand.autocommand(__name__)(strip_prefix)

llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/to-dvorak.py

@@ -0,0 +1,6 @@
+import sys
+
+from . import layouts
+
+
+__name__ == '__main__' and layouts._translate_stream(sys.stdin, layouts.to_dvorak)

llava/lib/python3.10/site-packages/setuptools/_vendor/jaraco/text/to-qwerty.py

@@ -0,0 +1,6 @@
+import sys
+
+from . import layouts
+
+
+__name__ == '__main__' and layouts._translate_stream(sys.stdin, layouts.to_qwerty)

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_adaptive_avg_pool3d_backward_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _adaptive_avg_pool3d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self);
+TORCH_API at::Tensor & _adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_assert_async_native.h

@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void _assert_async_cpu(const at::Tensor & self);
+TORCH_API void _assert_async_cuda(const at::Tensor & self);
+TORCH_API void _assert_async_msg_cpu(const at::Tensor & self, c10::string_view assert_msg);
+TORCH_API void _assert_async_msg_cuda(const at::Tensor & self, c10::string_view assert_msg);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_coalesced_meta_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & _coalesced_(at::Tensor & self, bool coalesced);
+
+} // namespace meta
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_clamp_min_native.h

@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_scalar_kernel_slow(at::TensorList self, const at::Scalar & scalar);
+TORCH_API void _foreach_clamp_min_Scalar_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out);
+TORCH_API void foreach_tensor_clamp_min_scalar_kernel_slow_(at::TensorList self, const at::Scalar & scalar);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_scalar_kernel_cuda(at::TensorList self, const at::Scalar & scalar);
+TORCH_API void foreach_tensor_clamp_min_scalar_kernel_cuda_(at::TensorList self, const at::Scalar & scalar);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_list_kernel_slow(at::TensorList self, at::TensorList other);
+TORCH_API void _foreach_clamp_min_List_out(at::TensorList self, at::TensorList other, at::TensorList out);
+TORCH_API void foreach_tensor_clamp_min_list_kernel_slow_(at::TensorList self, at::TensorList other);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_list_kernel_cuda(at::TensorList self, at::TensorList other);
+TORCH_API void foreach_tensor_clamp_min_list_kernel_cuda_(at::TensorList self, at::TensorList other);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_scalarlist_kernel_slow(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API void _foreach_clamp_min_ScalarList_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out);
+TORCH_API void foreach_tensor_clamp_min_scalarlist_kernel_slow_(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_clamp_min_scalarlist_kernel_cuda(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API void foreach_tensor_clamp_min_scalarlist_kernel_cuda_(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_zero_cuda_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API void _foreach_zero_(at::TensorList self);
+
+} // namespace cuda
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_grid_sampler_2d_cpu_fallback_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor _grid_sampler_2d_cpu_fallback(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners);
+TORCH_API at::Tensor & _grid_sampler_2d_cpu_fallback_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners);
+TORCH_API at::Tensor & _grid_sampler_2d_cpu_fallback_outf(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_linalg_svd_cpu_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_svd(const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, ::std::optional<c10::string_view> driver=::std::nullopt);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, ::std::optional<c10::string_view> driver=::std::nullopt);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_outf(const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_sum_backward_native.h

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor _nested_sum_backward_cpu(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_resize_output_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor _resize_output(const at::Tensor & self, at::IntArrayRef size, at::Device device);
+TORCH_API at::Tensor _resize_output_symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device);
+TORCH_API const at::Tensor & _resize_output_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::Device device);
+TORCH_API const at::Tensor & _resize_output_outf(const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out);
+TORCH_API const at::Tensor & _resize_output_symint_out(const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device);
+TORCH_API const at::Tensor & _resize_output_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device, const at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_cudnn_attention.h

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_scaled_dot_product_cudnn_attention_ops.h>
+
+namespace at {
+
+
+// aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, ::std::optional<double> scale=::std::nullopt) {
+    return at::_ops::_scaled_dot_product_cudnn_attention::call(query, key, value, dropout_p, is_causal, return_debug_mask, scale);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_validate_sparse_bsc_tensor_args.h

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_validate_sparse_bsc_tensor_args_ops.h>
+
+namespace at {
+
+
+// aten::_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()
+inline void _validate_sparse_bsc_tensor_args(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) {
+    return at::_ops::_validate_sparse_bsc_tensor_args::call(ccol_indices, row_indices, values, size);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_validate_sparse_compressed_tensor_args_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API void _validate_sparse_compressed_tensor_args(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout);
+
+} // namespace compositeimplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/acos_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_acos : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/aminmax_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_aminmax : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/angle.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/angle_ops.h>
+
+namespace at {
+
+
+// aten::angle(Tensor self) -> Tensor
+inline at::Tensor angle(const at::Tensor & self) {
+    return at::_ops::angle::call(self);
+}
+
+// aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & angle_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::angle_out::call(self, out);
+}
+// aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & angle_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::angle_out::call(self, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/any_native.h

@@ -0,0 +1,34 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/any_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_any_out : public at::meta::structured_any_dim {
+void impl(const at::Tensor & self, int64_t dim, bool keepdim, const at::Tensor & out);
+};
+TORCH_API at::Tensor any_dims_default(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & any_dims_out_default(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out);
+struct TORCH_API structured_any_dims_out : public at::meta::structured_any_dims {
+void impl(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, const at::Tensor & out);
+};
+TORCH_API at::Tensor any(const at::Tensor & self, at::Dimname dim, bool keepdim=false);
+TORCH_API at::Tensor & any_out(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out);
+struct TORCH_API structured_any_all_out : public at::meta::structured_any {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+TORCH_API at::Tensor any_sparse(const at::Tensor & self);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/arctan_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor arctan(const at::Tensor & self);
+TORCH_API at::Tensor & arctan_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & arctan_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & arctan_(at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/avg_pool2d_backward_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_avg_pool2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/batch_norm_backward_cpu_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, bool update, double eps, ::std::array<bool,3> output_mask, const at::Tensor & reserve);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/bernoulli_meta_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & bernoulli_(at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & bernoulli_(at::Tensor & self, double p=0.5, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace meta
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/block_diag_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor block_diag(at::TensorList tensors);
+TORCH_API at::Tensor & block_diag_out(at::TensorList tensors, at::Tensor & out);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_affine_grid_generator_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & cudnn_affine_grid_generator_out(at::Tensor & out, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W);
+TORCH_API at::Tensor & cudnn_affine_grid_generator_outf(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/cumulative_trapezoid.h

@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/cumulative_trapezoid_ops.h>
+
+namespace at {
+
+
+// aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
+inline at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
+    return at::_ops::cumulative_trapezoid_x::call(y, x, dim);
+}
+
+// aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor
+inline at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) {
+    return at::_ops::cumulative_trapezoid_dx::call(y, dx, dim);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/det_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API det {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::det")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "det(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/diag_embed_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & diag_embed_out(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out);
+TORCH_API at::Tensor diag_embed(const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal.h

@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/diagonal_ops.h>
+
+namespace at {
+
+
+// aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)
+inline at::Tensor diagonal(const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
+    return at::_ops::diagonal::call(self, offset, dim1, dim2);
+}
+
+// aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)
+inline at::Tensor diagonal(const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset=0) {
+    return at::_ops::diagonal_Dimname::call(self, outdim, dim1, dim2, offset);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/equal_cpu_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API bool equal(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/exp2_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_exp2 : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/fake_quantize_per_channel_affine_cachemask_backward_native.h

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor fake_quantize_per_channel_affine_cachemask_backward(const at::Tensor & grad, const at::Tensor & mask);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/fbgemm_linear_int8_weight_fp32_activation_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor fbgemm_linear_int8_weight_fp32_activation(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias);
+
+} // namespace compositeimplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/frac_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_frac : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/gcd.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/gcd_ops.h>
+
+namespace at {
+
+
+// aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & gcd_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::gcd_out::call(self, other, out);
+}
+// aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & gcd_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::gcd_out::call(self, other, out);
+}
+
+// aten::gcd(Tensor self, Tensor other) -> Tensor
+inline at::Tensor gcd(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::gcd::call(self, other);
+}
+
+// aten::gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!)
+inline at::Tensor & gcd_(at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::gcd_::call(self, other);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/gelu_backward_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate="none");
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/hardsigmoid_backward_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/isneginf_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API isneginf {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::isneginf")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "isneginf(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API isneginf_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::isneginf")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_ldl_factor_ex_cuda_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian=false, bool check_errors=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_out(at::Tensor & LD, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool hermitian=false, bool check_errors=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_outf(const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info);
+
+} // namespace cuda
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_matrix_exp_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API linalg_matrix_exp {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_matrix_exp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_matrix_exp(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API linalg_matrix_exp_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_matrix_exp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops