


import copy

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


UpperCamelCase			:					str         =							logging.get_logger(__name__)

UpperCamelCase			:					List[Any]         =							{}

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       int								=			"fsmt_decoder"
					def __init__(						self       :						Union[str, Any]	,      _lowerCAmelCase       :						str=0	,      _lowerCAmelCase       :						Dict=0  )							->					List[str]:



											'''simple docstring'''





											super().__init__()
											__magic_name__					=      vocab_size
											__magic_name__					=      bos_token_id







class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Tuple								=			"fsmt"
					SCREAMING_SNAKE_CASE_		:       str								=			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
					def __init__(						self       :						List[str]	,      _lowerCAmelCase       :						Optional[int]=["en", "de"]	,      _lowerCAmelCase       :						List[str]=4_2_0_2_4	,      _lowerCAmelCase       :						Union[str, Any]=4_2_0_2_4	,      _lowerCAmelCase       :						List[Any]="relu"	,      _lowerCAmelCase       :						Optional[int]=1_0_2_4	,      _lowerCAmelCase       :						List[Any]=2_0_0	,      _lowerCAmelCase       :						int=1_0_2_4	,      _lowerCAmelCase       :						Optional[Any]=4_0_9_6	,      _lowerCAmelCase       :						Optional[int]=1_2	,      _lowerCAmelCase       :						List[Any]=1_6	,      _lowerCAmelCase       :						Optional[Any]=0.0	,      _lowerCAmelCase       :						Dict=4_0_9_6	,      _lowerCAmelCase       :						int=1_2	,      _lowerCAmelCase       :						Dict=1_6	,      _lowerCAmelCase       :						Dict=0.0	,      _lowerCAmelCase       :						Union[str, Any]=0.0	,      _lowerCAmelCase       :						Dict=0.1	,      _lowerCAmelCase       :						Optional[int]=0.0	,      _lowerCAmelCase       :						Optional[int]=0.02	,      _lowerCAmelCase       :						Tuple=2	,      _lowerCAmelCase       :						str=True	,      _lowerCAmelCase       :						Optional[Any]=True	,      _lowerCAmelCase       :						int=False	,      _lowerCAmelCase       :						str=5	,      _lowerCAmelCase       :						Tuple=1.0	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						str=1	,      _lowerCAmelCase       :						str=0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						int=2	,      **_lowerCAmelCase       :						Dict	,      )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      langs
											__magic_name__					=      src_vocab_size
											__magic_name__					=      tgt_vocab_size
											__magic_name__					=      d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

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







					def 	a			(						self       :						Dict  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      copy.deepcopy(self.__dict__  )
											__magic_name__					=      self.decoder.to_dict()
											__magic_name__					=      self.__class__.model_type
											return output





from ..utils import DummyObject, requires_backends

class      __snake_case				(   metaclass=_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			["torch", "transformers", "k_diffusion"]
					def __init__(						self       :						Tuple	,      *_lowerCAmelCase       :						Union[str, Any]	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Dict:



											'''simple docstring'''





											requires_backends(self	,      ['torch', 'transformers', 'k_diffusion']  )
					@classmethod
					def 	a			(						cls       :						Optional[int]	,      *_lowerCAmelCase       :						Any	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )







					@classmethod
					def 	a			(						cls       :						Union[str, Any]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						str  )							->					Tuple:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )

