



import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


SCREAMING_SNAKE_CASE_:Tuple             =						NewType("""DataClass""", Any)
SCREAMING_SNAKE_CASE_:Tuple             =						NewType("""DataClassType""", Any)




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






    if isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise ArgumentTypeError(
            f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''		)




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






    A :       Tuple          =   {str(_lowerCAmelCase		): choice for choice in choices}
    return lambda _lowerCAmelCase		: str_to_choice.get(_lowerCAmelCase      ,     _lowerCAmelCase		)




def 	__UpperCamelCase	(  *,
    _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = dataclasses.MISSING      ,     _lowerCAmelCase = dataclasses.MISSING      ,     _lowerCAmelCase = None      ,     **_lowerCAmelCase      ,     )       ->  Optional[int]:



    """simple docstring"""






    if metadata is None:
        # Important, don't use as default param in function signature because dict is mutable and shared across function calls
        A :       Optional[int]          =   {}
    if aliases is not None:
        A :       Dict          =   aliases
    if help is not None:
        A :       int          =   help

    return dataclasses.field(metadata=_lowerCAmelCase      ,     default=_lowerCAmelCase      ,     default_factory=_lowerCAmelCase      ,     **_lowerCAmelCase		)






class SCREAMING_SNAKE_CASE__     (		A__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      42






    def __init__(       self,		lowerCamelCase__,		**lowerCamelCase__		):






        if "formatter_class" not in kwargs:
            A :       List[str]          =   ArgumentDefaultsHelpFormatter
        super().__init__(**_A		)
        if dataclasses.is_dataclass(_A		):
            A :       Tuple          =   [dataclass_types]
        A :       int          =   list(_A		)
        for dtype in self.dataclass_types:
            self._add_dataclass_arguments(_A		)






    @staticmethod
    def 			_lowerCAmelCase					(       lowerCamelCase__,		lowerCamelCase__		):
        A :       Optional[int]          =   f'''--{field.name}'''
        A :       Dict          =   field.metadata.copy()
        # field.metadata is not used at all by Data Classes,
        # it is provided as a third-party extension mechanism.
        if isinstance(field.type,		_A		):
            raise RuntimeError(
                """Unresolved type detected, which should have been done with the help of """
                """`typing.get_type_hints` method by default"""		)

        A :       Optional[Any]          =   kwargs.pop("""aliases""",		[]		)
        if isinstance(_A,		_A		):
            A :       str          =   [aliases]

        A :       str          =   getattr(field.type,		"""__origin__""",		field.type		)
        if origin_type is Union or (hasattr(_A,		"""UnionType"""		) and isinstance(_A,		types.UnionType		)):
            if str not in field.type.__args__ and (
                len(field.type.__args__		) != 2 or type(_A		) not in field.type.__args__
            ):
                raise ValueError(
                    """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
                    """ the argument parser only supports one type per argument."""
                    f''' Problem encountered in field \'{field.name}\'.'''		)
            if type(_A		) not in field.type.__args__:
                # filter `str` in Union
                A :       Any          =   field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                A :       Tuple          =   getattr(field.type,		"""__origin__""",		field.type		)
            elif bool not in field.type.__args__:
                # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                A :       int          =   (
                    field.type.__args__[0] if isinstance(_A,		field.type.__args__[1]		) else field.type.__args__[1]
                )
                A :       Dict          =   getattr(field.type,		"""__origin__""",		field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
        A :       Tuple          =   {}
        if origin_type is Literal or (isinstance(field.type,		_A		) and issubclass(field.type,		_A		)):
            if origin_type is Literal:
                A :       Tuple          =   field.type.__args__
            else:
                A :       Any          =   [x.value for x in field.type]

            A :       Tuple          =   make_choice_type_function(kwargs["""choices"""]		)

            if field.default is not dataclasses.MISSING:
                A :       Dict          =   field.default
            else:
                A :       Optional[Any]          =   True
        elif field.type is bool or field.type == Optional[bool]:
            # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
            # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
            A :       Optional[Any]          =   copy(_A		)

            # Hack because type=bool in argparse does not behave as we want.
            A :       int          =   string_to_bool
            if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                # Default value is False if we have no default when of type bool.
                A :       List[Any]          =   False if field.default is dataclasses.MISSING else field.default
                # This is the value that will get picked if we don't include --field_name in any way
                A :       Union[str, Any]          =   default
                # This tells argparse we accept 0 or 1 value after --field_name
                A :       str          =   "?"
                # This is the value that will get picked if we do --field_name (without value)
                A :       int          =   True
        elif isclass(_A		) and issubclass(_A,		_A		):
            A :       Union[str, Any]          =   field.type.__args__[0]
            A :       Optional[int]          =   "+"
            if field.default_factory is not dataclasses.MISSING:
                A :       Union[str, Any]          =   field.default_factory()
            elif field.default is dataclasses.MISSING:
                A :       str          =   True
        else:
            A :       Dict          =   field.type
            if field.default is not dataclasses.MISSING:
                A :       Dict          =   field.default
            elif field.default_factory is not dataclasses.MISSING:
                A :       Optional[Any]          =   field.default_factory()
            else:
                A :       List[str]          =   True
        parser.add_argument(_A,		*_A,		**_A		)

        # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
        # Order is important for arguments with the same destination!
        # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
        # here and we do not need those changes/additional keys.
        if field.default is True and (field.type is bool or field.type == Optional[bool]):
            A :       Dict          =   False
            parser.add_argument(f'''--no_{field.name}''',		action="""store_false""",		dest=field.name,		**_A		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):






        if hasattr(_A,		"""_argument_group_name"""		):
            A :       Union[str, Any]          =   self.add_argument_group(dtype._argument_group_name		)
        else:
            A :       Optional[int]          =   self

        try:
            A :       Dict[str, type]          =   get_type_hints(_A		)
        except NameError:
            raise RuntimeError(
                f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
                """removing line of `from __future__ import annotations` which opts in Postponed """
                """Evaluation of Annotations (PEP 563)"""		)
        except TypeError as ex:
            # Remove this block when we drop Python 3.9 support
            if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_A		):
                A :       Optional[Any]          =   ".".join(map(_A,		sys.version_info[:3]		)		)
                raise RuntimeError(
                    f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
                    """line of `from __future__ import annotations` which opts in union types as """
                    """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
                    """support Python versions that lower than 3.10, you need to use """
                    """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
                    """`X | None`."""		) from ex
            raise

        for field in dataclasses.fields(_A		):
            if not field.init:
                continue
            A :       int          =   type_hints[field.name]
            self._parse_dataclass_field(_A,		_A		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=None,		):






        if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
            A :       Tuple          =   []

            if args_filename:
                args_files.append(Path(_A		)		)
            elif look_for_args_file and len(sys.argv		):
                args_files.append(Path(sys.argv[0]		).with_suffix(""".args"""		)		)

            # args files specified via command line flag should overwrite default args files so we add them last
            if args_file_flag:
                # Create special parser just to extract the args_file_flag values
                A :       Optional[Any]          =   ArgumentParser()
                args_file_parser.add_argument(_A,		type=_A,		action="""append"""		)

                # Use only remaining args for further parsing (remove the args_file_flag)
                A :       Dict          =   args_file_parser.parse_known_args(args=_A		)
                A :       Union[str, Any]          =   vars(_A		).get(args_file_flag.lstrip("""-"""		),		_A		)

                if cmd_args_file_paths:
                    args_files.extend([Path(_A		) for p in cmd_args_file_paths]		)

            A :       Any          =   []
            for args_file in args_files:
                if args_file.exists():
                    file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
            A :       Union[str, Any]          =   file_args + args if args is not None else file_args + sys.argv[1:]
        A :       str          =   self.parse_known_args(args=_A		)
        A :       Tuple          =   []
        for dtype in self.dataclass_types:
            A :       List[Any]          =   {f.name for f in dataclasses.fields(_A		) if f.init}
            A :       Optional[Any]          =   {k: v for k, v in vars(_A		).items() if k in keys}
            for k in keys:
                delattr(_A,		_A		)
            A :       List[str]          =   dtype(**_A		)
            outputs.append(_A		)
        if len(namespace.__dict__		) > 0:
            # additional namespace.
            outputs.append(_A		)
        if return_remaining_strings:
            return (*outputs, remaining_args)
        else:
            if remaining_args:
                raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''		)

            return (*outputs,)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = False		):
        A :       str          =   set(args.keys()		)
        A :       Tuple          =   []
        for dtype in self.dataclass_types:
            A :       List[str]          =   {f.name for f in dataclasses.fields(_A		) if f.init}
            A :       Tuple          =   {k: v for k, v in args.items() if k in keys}
            unused_keys.difference_update(inputs.keys()		)
            A :       Dict          =   dtype(**_A		)
            outputs.append(_A		)
        if not allow_extra_keys and unused_keys:
            raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(_A		)}'''		)
        return tuple(_A		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = False		):






        with open(Path(_A		),		encoding="""utf-8"""		) as open_json_file:
            A :       Any          =   json.loads(open_json_file.read()		)
        A :       List[str]          =   self.parse_dict(_A,		allow_extra_keys=_A		)
        return tuple(_A		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = False		):
        A :       Optional[int]          =   self.parse_dict(yaml.safe_load(Path(_A		).read_text()		),		allow_extra_keys=_A		)
        return tuple(_A		)



import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch






class 		a__						(					unittest.TestCase					):

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    {
							    "task_specific_params": {
							        "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
							        "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
							        "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
							    }
							}
							SCREAMING_SNAKE_CASE_  :		Any				      =    {
							    "task_specific_params.summarization.length_penalty": 1.0,
							    "task_specific_params.summarization.max_length": 128,
							    "task_specific_params.summarization.min_length": 12,
							    "task_specific_params.summarization.num_beams": 4,
							    "task_specific_params.summarization_cnn.length_penalty": 2.0,
							    "task_specific_params.summarization_cnn.max_length": 142,
							    "task_specific_params.summarization_cnn.min_length": 56,
							    "task_specific_params.summarization_cnn.num_beams": 4,
							    "task_specific_params.summarization_xsum.length_penalty": 1.0,
							    "task_specific_params.summarization_xsum.max_length": 62,
							    "task_specific_params.summarization_xsum.min_length": 11,
							    "task_specific_params.summarization_xsum.num_beams": 6,
							}

							self.assertEqual(flatten_dict(_A    ),_A    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(transpose(_A    ),x.transpose()    )    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4,5    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),x.transpose((1, 2, 0)    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),transpose(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),transpose(_A,axes=(1, 2, 0)    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),transpose(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),transpose(_A,axes=(1, 2, 0)    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),np.asarray(transpose(_A    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),np.asarray(transpose(_A,axes=(1, 2, 0)    )    )    )    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),np.reshape(_A,(4, 3)    )    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(3,4,5    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),np.reshape(_A,(12, 5)    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),reshape(_A,(4, 3)    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),reshape(_A,(12, 5)    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),reshape(_A,(4, 3)    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),reshape(_A,(12, 5)    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		int				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),np.asarray(reshape(_A,(4, 3)    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),np.asarray(reshape(_A,(12, 5)    )    )    )    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(1,3,4    )
							self.assertTrue(np.allclose(squeeze(_A    ),np.squeeze(_A    )    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),np.squeeze(_A,axis=2    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),squeeze(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),squeeze(_A,axis=2    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),squeeze(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),squeeze(_A,axis=2    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),np.asarray(squeeze(_A    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),np.asarray(squeeze(_A,axis=2    )    )    )    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),np.expand_dims(_A,axis=1    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),expand_dims(_A,axis=1    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),expand_dims(_A,axis=1    ).numpy()    )    )






					@require_flax
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),np.asarray(expand_dims(_A,axis=1    )    )    )    )
