
"""simple docstring"""


import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


__A       						=       set()
__A       						=       get_logger(__name__)



def     _lowerCamelCase(__UpperCamelCase = None       )				-> Optional[Any]:

	def decorator(__UpperCamelCase       ):
		global _emitted_deprecation_warnings

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=deprecated_class_or_function.__init__
			_lowerCAmelCase								=deprecated_class_or_function.__name__
		else:
			_lowerCAmelCase								=deprecated_class_or_function
			_lowerCAmelCase								=deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			_lowerCAmelCase								=name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

		_lowerCAmelCase								=(
		    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
		    if help_message
		    else """"""
		)

		@wraps(__UpperCamelCase       )
		def wrapper(*__UpperCamelCase    , **__UpperCamelCase       ):
			_lowerCAmelCase								=hash(__UpperCamelCase       )
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(__UpperCamelCase    , category=__UpperCamelCase    , stacklevel=2       )
				_emitted_deprecation_warnings.add(__UpperCamelCase       )
			return deprecated_function(*__UpperCamelCase    , **__UpperCamelCase       )

		_lowerCAmelCase								="""deprecated"""

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator







class 		lowerCamelCase__					(					enum.EnumMeta				):





			'''simple docstring'''



			def __getattribute__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=super().__getattribute__(__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj



			def __getitem__(							cls     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase								=super().__getitem__(__UpperCAmelCase  )
				if member._on_access:
					member._on_access()
				return member



			def __call__(							cls     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None     ,     *, __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=1  ) ->     Optional[Any]:
				_lowerCAmelCase								=super().__call__(__UpperCAmelCase     ,     __UpperCAmelCase     ,     module=__UpperCAmelCase     ,     qualname=__UpperCAmelCase     ,     type=__UpperCAmelCase     ,     start=__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj







class 		lowerCamelCase__					(					enum.Enum ,	metaclass=__magic_name__				):





			'''simple docstring'''



			def __new__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=object.__new__(cls  )
				_lowerCAmelCase								=value
				_lowerCAmelCase								=member.deprecate
				return member



			@property
			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				return ""



			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				_lowerCAmelCase								=f''' {self.help_message}''' if self.help_message else """"""
				warnings.warn(
				    f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
				    + help_message     ,     __UpperCAmelCase     ,     stacklevel=3     ,     )











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A          = logging.get_logger(__name__)

__A          = {}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt_decoder"

							def __init__(     self:      List[Any]						,   __A:      Dict=0						,   __A:      str=0      )			-> Optional[int]:
												super().__init__()
												_A									=       vocab_size
												_A									=       bos_token_id





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt"
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      Optional[Any]						,   __A:      Optional[Any]=["en", "de"]						,   __A:      List[str]=4_20_24						,   __A:      Any=4_20_24						,   __A:      Optional[int]="relu"						,   __A:      List[str]=10_24						,   __A:      str=2_00						,   __A:      Tuple=10_24						,   __A:      List[str]=40_96						,   __A:      List[Any]=12						,   __A:      Union[str, Any]=16						,   __A:      List[str]=0.0						,   __A:      int=40_96						,   __A:      Union[str, Any]=12						,   __A:      List[str]=16						,   __A:      Union[str, Any]=0.0						,   __A:      int=0.0						,   __A:      str=0.1						,   __A:      int=0.0						,   __A:      Union[str, Any]=0.02						,   __A:      Tuple=2						,   __A:      int=True						,   __A:      Dict=True						,   __A:      int=False						,   __A:      Any=5						,   __A:      Tuple=1.0						,   __A:      Optional[int]=False						,   __A:      List[Any]=True						,   __A:      Tuple=1						,   __A:      List[Any]=0						,   __A:      Any=2						,   __A:      Optional[Any]=2						,   **__A:      Optional[Any]						,   )			-> Tuple:
												_A									=       langs
												_A									=       src_vocab_size
												_A									=       tgt_vocab_size
												_A									=       d_model  # encoder_embed_dim and decoder_embed_dim

												_A									=       encoder_ffn_dim
												_A									=       _A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       max_position_embeddings
												_A									=       init_std  # Normal(0, this parameter)
												_A									=       activation_function

												_A									=       DecoderConfig(vocab_size=__A						,   bos_token_id=__A      )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												_A									=       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       dropout

												_A									=       use_cache
												super().__init__(
												    pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   decoder_start_token_id=__A						,   is_encoder_decoder=__A						,   tie_word_embeddings=__A						,   forced_eos_token_id=__A						,   max_length=__A						,   num_beams=__A						,   length_penalty=__A						,   early_stopping=__A						,   **__A						,   )



							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_A									=       copy.deepcopy(self.__dict__      )
												_A									=       self.decoder.to_dict()
												_A									=       self.__class__.model_type
												return output



