
"""simple docstring"""

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


UpperCamelCase	:				int  	= logging.get_logger(__name__)

UpperCamelCase	:				List[str]  	= {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class __lowerCAmelCase		(      UpperCAmelCase__			):
     lowercase						   =						"trocr"
     lowercase						   =						["past_key_values"]
     lowercase						   =						{
         "num_attention_heads": "decoder_attention_heads",
         "hidden_size": "d_model",
         "num_hidden_layers": "decoder_layers",
     }





     def __init__(	self	,     __UpperCAmelCase=5_0265	,     __UpperCAmelCase=1024	,     __UpperCAmelCase=12	,     __UpperCAmelCase=16	,     __UpperCAmelCase=4096	,     __UpperCAmelCase="gelu"	,     __UpperCAmelCase=512	,     __UpperCAmelCase=0.1	,     __UpperCAmelCase=0.0	,     __UpperCAmelCase=0.0	,     __UpperCAmelCase=2	,     __UpperCAmelCase=0.0_2	,     __UpperCAmelCase=0.0	,     __UpperCAmelCase=True	,     __UpperCAmelCase=False	,     __UpperCAmelCase=True	,     __UpperCAmelCase=True	,     __UpperCAmelCase=1	,     __UpperCAmelCase=0	,     __UpperCAmelCase=2	,     **__UpperCAmelCase	,     ):

      '''simple docstring'''




      __UpperCamelCase				  =       vocab_size
      __UpperCamelCase				  =       d_model
      __UpperCamelCase				  =       decoder_layers
      __UpperCamelCase				  =       decoder_attention_heads
      __UpperCamelCase				  =       decoder_ffn_dim
      __UpperCamelCase				  =       activation_function
      __UpperCamelCase				  =       max_position_embeddings
      __UpperCamelCase				  =       dropout
      __UpperCamelCase				  =       attention_dropout
      __UpperCamelCase				  =       activation_dropout
      __UpperCamelCase				  =       init_std
      __UpperCamelCase				  =       decoder_layerdrop
      __UpperCamelCase				  =       use_cache
      __UpperCamelCase				  =       scale_embedding
      __UpperCamelCase				  =       use_learned_position_embeddings
      __UpperCamelCase				  =       layernorm_embedding

      super().__init__(
          pad_token_id=__UpperCAmelCase	,     bos_token_id=__UpperCAmelCase	,     eos_token_id=__UpperCAmelCase	,     decoder_start_token_id=__UpperCAmelCase	,     **__UpperCAmelCase	,     )







'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


A__:    List[str]					    =       logging.get_logger(__name__)

A__:    Union[str, Any]					    =       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  int	       =							"data2vec-text"






      def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:Any=7_6_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Dict=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[str]="gelu"     ,						SCREAMING_SNAKE_CASE			:Any=0.1     ,						SCREAMING_SNAKE_CASE			:List[str]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:int=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Dict=1e-12     ,						SCREAMING_SNAKE_CASE			:int=1     ,						SCREAMING_SNAKE_CASE			:Dict=0     ,						SCREAMING_SNAKE_CASE			:List[Any]=2     ,						SCREAMING_SNAKE_CASE			:str="absolute"     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						**SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						)       ->					List[str]:




             '''simple docstring'''
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						bos_token_id=SCREAMING_SNAKE_CASE     ,						eos_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					=vocab_size
             _a    : Optional[Any]     					=hidden_size
             _a    : Any     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : Union[str, Any]     					=hidden_act
             _a    : Any     					=intermediate_size
             _a    : str     					=hidden_dropout_prob
             _a    : Optional[Any]     					=attention_probs_dropout_prob
             _a    : Optional[Any]     					=max_position_embeddings
             _a    : Union[str, Any]     					=type_vocab_size
             _a    : Tuple     					=initializer_range
             _a    : Optional[int]     					=layer_norm_eps
             _a    : Tuple     					=position_embedding_type
             _a    : int     					=use_cache
             _a    : List[str]     					=classifier_dropout







class A__       (						UpperCAmelCase__      ):






      @property
      def __UpperCAmelCase       ( self			:int	)       ->					Mapping[str, Mapping[int, str]]:




             '''simple docstring'''
             if self.task == "multiple-choice":
                    _a    : Tuple     					={0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    _a    : List[Any]     					={0: """batch""", 1: """sequence"""}
             return OrderedDict(
                 [
                     ("""input_ids""", dynamic_axis),
                     ("""attention_mask""", dynamic_axis),
                 ]	)


