

'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				List[str]    =				'''plbart'''
      SCREAMING_SNAKE_CASE__ :				Tuple    =				['''past_key_values''']
      SCREAMING_SNAKE_CASE__ :				Dict    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Union[str, Any]=50005     ,					snake_case :		Dict=1024     ,					snake_case :		Optional[int]=6     ,					snake_case :		Optional[int]=3072     ,					snake_case :		Any=12     ,					snake_case :		Optional[int]=6     ,					snake_case :		Union[str, Any]=3072     ,					snake_case :		Tuple=12     ,					snake_case :		Optional[Any]=0.0     ,					snake_case :		Tuple=0.0     ,					snake_case :		Dict=True     ,					snake_case :		Dict=True     ,					snake_case :		int="gelu"     ,					snake_case :		Union[str, Any]=768     ,					snake_case :		Tuple=0.1     ,					snake_case :		Optional[int]=0.1     ,					snake_case :		Dict=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		Optional[Any]=0.0     ,					snake_case :		int=True     ,					snake_case :		int=1     ,					snake_case :		str=0     ,					snake_case :		str=2     ,					snake_case :		int=2     ,					**snake_case :		Optional[Any]     ,					):



          """simple docstring"""





          _snake_case : str												=		vocab_size
          _snake_case : Tuple												=		max_position_embeddings
          _snake_case : Any												=		d_model
          _snake_case : int												=		encoder_ffn_dim
          _snake_case : Optional[Any]												=		encoder_layers
          _snake_case : Optional[int]												=		encoder_attention_heads
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Dict												=		decoder_layers
          _snake_case : Optional[int]												=		decoder_attention_heads
          _snake_case : Optional[Any]												=		dropout
          _snake_case : Optional[int]												=		attention_dropout
          _snake_case : int												=		activation_dropout
          _snake_case : Dict												=		activation_function
          _snake_case : Optional[Any]												=		init_std
          _snake_case : Optional[Any]												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : int												=		classifier_dropout
          _snake_case : List[Any]												=		use_cache
          _snake_case : Tuple												=		encoder_layers
          _snake_case : Optional[int]												=		scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					forced_eos_token_id=snake_case     ,					**snake_case     ,					)



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      @property
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          return OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'sequence'}),
              ]   )




      @property
      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          if self.use_past:
              return OrderedDict(
                  [
                      ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                      ('past_keys', {0: 'batch', 2: 'sequence'}),
                      ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ]   )
          else:
              return OrderedDict(
                  [
                      ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                      ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ]   )






'''simple docstring'''



import copy

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


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

          _snake_case : Optional[Any]												=		encoder_ffn_dim
          _snake_case : Dict												=		encoder_layers
          _snake_case : List[Any]												=		encoder_attention_heads
          _snake_case : Any												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Union[str, Any]												=		decoder_layers
          _snake_case : Tuple												=		decoder_attention_heads
          _snake_case : str												=		max_position_embeddings
          _snake_case : Dict												=		init_std  # Normal(0, this parameter)
          _snake_case : List[str]												=		activation_function

          _snake_case : Any												=		DecoderConfig(vocab_size=snake_case     ,					bos_token_id=snake_case   )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



