





import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __get__( self : Tuple						,				_A : Any						,				_A : str=None		)							->      Optional[int]:







							"""simple docstring"""


							if obj is None:
								return self
							if self.fget is None:
								raise AttributeError('unreadable attribute'		)
							snake_case_						:     Optional[Any]     =		'__cached_' + self.fget.__name__
							snake_case_						:     List[Any]     =		getattr(_A						,				_A						,				_A		)
							if cached is None:
								snake_case_						:     int     =		self.fget(_A		)
								setattr(_A						,				_A						,				_A		)
							return cached


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		val.lower()
	if val in {"y", "yes", "t", "true", "on", "1"}:
		return 1
	if val in {"n", "no", "f", "false", "off", "0"}:
		return 0
	raise ValueError(f"""invalid truth value {val!r}"""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if is_torch_fx_proxy(__a				):
		return True
	if is_torch_available():
		import torch

		if isinstance(__a				,	torch.Tensor				):
			return True
	if is_tf_available():
		import tensorflow as tf

		if isinstance(__a				,	tf.Tensor				):
			return True

	if is_flax_available():
		import jax.numpy as jnp
		from jax.core import Tracer

		if isinstance(__a				,	(jnp.ndarray, Tracer)				):
			return True

	return isinstance(__a				,	np.ndarray				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return isinstance(__a				,	np.ndarray				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return _is_numpy(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import torch

	return isinstance(__a				,	torch.Tensor				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_torch_available() else _is_torch(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import torch

	return isinstance(__a				,	torch.device				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_torch_available() else _is_torch_device(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import torch

	if isinstance(__a				,	__a				):
		if hasattr(__a				,	__a				):
			snake_case_						:     str     =		getattr(__a				,	__a				)
		else:
			return False
	return isinstance(__a				,	torch.dtype				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_torch_available() else _is_torch_dtype(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import tensorflow as tf

	return isinstance(__a				,	tf.Tensor				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_tf_available() else _is_tensorflow(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import tensorflow as tf

	# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
	if hasattr(__a				,	'is_symbolic_tensor'				):
		return tf.is_symbolic_tensor(__a				)
	return type(__a				) == tf.Tensor


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_tf_available() else _is_tf_symbolic_tensor(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	import jax.numpy as jnp  # noqa: F811

	return isinstance(__a				,	jnp.ndarray				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return False if not is_flax_available() else _is_jax(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if isinstance(__a				,	(dict, UserDict)				):
		return {k: to_py_obj(__a				) for k, v in obj.items()}
	elif isinstance(__a				,	(list, tuple)				):
		return [to_py_obj(__a				) for o in obj]
	elif is_tf_tensor(__a				):
		return obj.numpy().tolist()
	elif is_torch_tensor(__a				):
		return obj.detach().cpu().tolist()
	elif is_jax_tensor(__a				):
		return np.asarray(__a				).tolist()
	elif isinstance(__a				,	(np.ndarray, np.number)				):  # tolist also works on 0d np arrays
		return obj.tolist()
	else:
		return obj


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if isinstance(__a				,	(dict, UserDict)				):
		return {k: to_numpy(__a				) for k, v in obj.items()}
	elif isinstance(__a				,	(list, tuple)				):
		return np.array(__a				)
	elif is_tf_tensor(__a				):
		return obj.numpy()
	elif is_torch_tensor(__a				):
		return obj.detach().cpu().numpy()
	elif is_jax_tensor(__a				):
		return np.asarray(__a				)
	else:
		return obj
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		fields(self		)

							# Safety and consistency checks
							if not len(_A		):
								raise ValueError(F"""{self.__class__.__name__} has no fields."""		)
							if not all(field.default is None for field in class_fields[1:]		):
								raise ValueError(F"""{self.__class__.__name__} should not have more than one required field."""		)

							snake_case_						:     Any     =		getattr(self						,				class_fields[0].name		)
							snake_case_						:     Optional[int]     =		all(getattr(self						,				field.name		) is None for field in class_fields[1:]		)

							if other_fields_are_none and not is_tensor(_A		):
								if isinstance(_A						,				_A		):
									snake_case_						:     Optional[int]     =		first_field.items()
									snake_case_						:     Dict     =		True
								else:
									try:
										snake_case_						:     Optional[Any]     =		iter(_A		)
										snake_case_						:     Tuple     =		True
									except TypeError:
										snake_case_						:     Union[str, Any]     =		False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
								if first_field_iterator:
									for idx, element in enumerate(_A		):
										if (
										    not isinstance(_A						,				(list, tuple)		)
										    or not len(_A		) == 2
										    or not isinstance(element[0]						,				_A		)
										):
											if idx == 0:
												# If we do not have an iterator of key/values, set it as attribute
												snake_case_						:     Optional[Any]     =		first_field
											else:
												# If we have a mixed iterator, raise an error
												raise ValueError(
												    F"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""		)
											break
										setattr(self						,				element[0]						,				element[1]		)
										if element[1] is not None:
											snake_case_						:     Tuple     =		element[1]
								elif first_field is not None:
									snake_case_						:     List[Any]     =		first_field
							else:
								for field in class_fields:
									snake_case_						:     Union[str, Any]     =		getattr(self						,				field.name		)
									if v is not None:
										snake_case_						:     Dict     =		v






						def __delitem__( self : List[Any]						,				*_A : Any						,				**_A : Optional[Any]		)							->      Tuple:







							"""simple docstring"""


							raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""		)






						def UpperCAmelCase_			( self : List[str]						,				*_A : Optional[int]						,				**_A : Dict		)							->      Tuple:







							"""simple docstring"""


							raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""		)






						def UpperCAmelCase_			( self : List[Any]						,				*_A : int						,				**_A : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""		)






						def UpperCAmelCase_			( self : Dict						,				*_A : int						,				**_A : Tuple		)							->      Dict:







							"""simple docstring"""


							raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""		)






						def __getitem__( self : List[str]						,				_A : Dict		)							->      Tuple:







							"""simple docstring"""


							if isinstance(_A						,				_A		):
								snake_case_						:     Dict     =		dict(self.items()		)
								return inner_dict[k]
							else:
								return self.to_tuple()[k]






						def __setattr__( self : Any						,				_A : Optional[Any]						,				_A : str		)							->      Union[str, Any]:







							"""simple docstring"""


							if name in self.keys() and value is not None:
								# Don't call self.__setitem__ to avoid recursion errors
								super().__setitem__(_A						,				_A		)
							super().__setattr__(_A						,				_A		)






						def __setitem__( self : Optional[int]						,				_A : Dict						,				_A : int		)							->      Dict:







							"""simple docstring"""


							super().__setitem__(_A						,				_A		)
							# Don't call self.__setattr__ to avoid recursion errors
							super().__setattr__(_A						,				_A		)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Tuple[Any]:







							"""simple docstring"""


							return tuple(self[k] for k in self.keys()		)
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@classmethod
						def UpperCAmelCase_			( cls : List[str]						,				_A : Optional[int]		)							->      Any:







							"""simple docstring"""


							raise ValueError(
							    F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()		)}"""		)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Union[str, Any]	 =    "longest"
						__magic_name__:     Union[str, Any]	 =    "max_length"
						__magic_name__:     Tuple	 =    "do_not_pad"
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Dict	 =    "pt"
						__magic_name__:     int	 =    "tf"
						__magic_name__:     Any	 =    "np"
						__magic_name__:     int	 =    "jax"
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : Optional[Any]						,				_A : List[ContextManager]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		context_managers
							snake_case_						:     Optional[Any]     =		ExitStack()






						def __enter__( self : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							for context_manager in self.context_managers:
								self.stack.enter_context(_A		)






						def __exit__( self : Optional[int]						,				*_A : Union[str, Any]						,				**_A : List[str]		)							->      Optional[int]:







							"""simple docstring"""


							self.stack.__exit__(*_A						,				**_A		)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		infer_framework(__a				)
	if framework == "tf":
		snake_case_						:     List[str]     =		inspect.signature(model_class.call				)  # TensorFlow models
	elif framework == "pt":
		snake_case_						:     str     =		inspect.signature(model_class.forward				)  # PyTorch models
	else:
		snake_case_						:     Dict     =		inspect.signature(model_class.__call__				)  # Flax models

	for p in signature.parameters:
		if p == "return_loss" and signature.parameters[p].default is True:
			return True

	return False


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     str     =		model_class.__name__
	snake_case_						:     Optional[int]     =		infer_framework(__a				)
	if framework == "tf":
		snake_case_						:     Optional[Any]     =		inspect.signature(model_class.call				)  # TensorFlow models
	elif framework == "pt":
		snake_case_						:     Union[str, Any]     =		inspect.signature(model_class.forward				)  # PyTorch models
	else:
		snake_case_						:     Dict     =		inspect.signature(model_class.__call__				)  # Flax models

	if "QuestionAnswering" in model_name:
		return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
	else:
		return [p for p in signature.parameters if "label" in p]


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = ""				,	__a = "."				):

	def _flatten_dict(__a				,	__a=""				,	__a="."				):
		for k, v in d.items():
			snake_case_						:     Any     =		str(__a				) + delimiter + str(__a				) if parent_key else k
			if v and isinstance(__a				,	__a				):
				yield from flatten_dict(__a				,	__a				,	delimiter=__a				).items()
			else:
				yield key, v

	return dict(_flatten_dict(__a				,	__a				,	__a				)				)


@contextmanager
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = False				):
	if use_temp_dir:
		with tempfile.TemporaryDirectory() as tmp_dir:
			yield tmp_dir
	else:
		yield working_dir


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	if is_numpy_array(__a				):
		return np.transpose(__a				,	axes=__a				)
	elif is_torch_tensor(__a				):
		return array.T if axes is None else array.permute(*__a				)
	elif is_tf_tensor(__a				):
		import tensorflow as tf

		return tf.transpose(__a				,	perm=__a				)
	elif is_jax_tensor(__a				):
		return jnp.transpose(__a				,	axes=__a				)
	else:
		raise ValueError(f"""Type not supported for transpose: {type(__a				)}."""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	if is_numpy_array(__a				):
		return np.reshape(__a				,	__a				)
	elif is_torch_tensor(__a				):
		return array.reshape(*__a				)
	elif is_tf_tensor(__a				):
		import tensorflow as tf

		return tf.reshape(__a				,	__a				)
	elif is_jax_tensor(__a				):
		return jnp.reshape(__a				,	__a				)
	else:
		raise ValueError(f"""Type not supported for reshape: {type(__a				)}."""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	if is_numpy_array(__a				):
		return np.squeeze(__a				,	axis=__a				)
	elif is_torch_tensor(__a				):
		return array.squeeze() if axis is None else array.squeeze(dim=__a				)
	elif is_tf_tensor(__a				):
		import tensorflow as tf

		return tf.squeeze(__a				,	axis=__a				)
	elif is_jax_tensor(__a				):
		return jnp.squeeze(__a				,	axis=__a				)
	else:
		raise ValueError(f"""Type not supported for squeeze: {type(__a				)}."""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	if is_numpy_array(__a				):
		return np.expand_dims(__a				,	__a				)
	elif is_torch_tensor(__a				):
		return array.unsqueeze(dim=__a				)
	elif is_tf_tensor(__a				):
		import tensorflow as tf

		return tf.expand_dims(__a				,	axis=__a				)
	elif is_jax_tensor(__a				):
		return jnp.expand_dims(__a				,	axis=__a				)
	else:
		raise ValueError(f"""Type not supported for expand_dims: {type(__a				)}."""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if is_numpy_array(__a				):
		return np.size(__a				)
	elif is_torch_tensor(__a				):
		return array.numel()
	elif is_tf_tensor(__a				):
		import tensorflow as tf

		return tf.size(__a				)
	elif is_jax_tensor(__a				):
		return array.size
	else:
		raise ValueError(f"""Type not supported for expand_dims: {type(__a				)}."""				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	for key, value in auto_map.items():
		if isinstance(__a				,	(tuple, list)				):
			snake_case_						:     Any     =		[f"""{repo_id}--{v}""" if (v is not None and '--' not in v) else v for v in value]
		elif value is not None and "--" not in value:
			snake_case_						:     Dict     =		f"""{repo_id}--{value}"""

	return auto_map


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	for base_class in inspect.getmro(__a				):
		snake_case_						:     Tuple     =		base_class.__module__
		snake_case_						:     List[Any]     =		base_class.__name__
		if module.startswith('tensorflow'				) or module.startswith('keras'				) or name == "TFPreTrainedModel":
			return "tf"
		elif module.startswith('torch'				) or name == "PreTrainedModel":
			return "pt"
		elif module.startswith('flax'				) or module.startswith('jax'				) or name == "FlaxPreTrainedModel":
			return "flax"
	else:
		raise TypeError(f"""Could not infer framework from class {model_class}."""				)








# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_SCREAMING_SNAKE_CASE 		=						subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_SCREAMING_SNAKE_CASE 		=						(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

_SCREAMING_SNAKE_CASE 		=						"""|""".join(sys.argv[1:])
_SCREAMING_SNAKE_CASE 		=						re.compile(RF'''^({joined_dirs}).*?\.py$''')

_SCREAMING_SNAKE_CASE 		=						[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")

