
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

A__   :		Tuple 		={
    '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  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''trocr'''
 lowerCamelCase				       =['''past_key_values''']
 lowerCamelCase				       ={
     '''num_attention_heads''': '''decoder_attention_heads''',
     '''hidden_size''': '''d_model''',
     '''num_hidden_layers''': '''decoder_layers''',
 }
 def __init__(   self			:    str				,   lowerCamelCase			:    Tuple=5_02_65				,   lowerCamelCase			:    Any=10_24				,   lowerCamelCase			:    List[str]=12				,   lowerCamelCase			:    Optional[Any]=16				,   lowerCamelCase			:    str=40_96				,   lowerCamelCase			:    Any="gelu"				,   lowerCamelCase			:    Optional[int]=5_12				,   lowerCamelCase			:    int=0.1				,   lowerCamelCase			:    Optional[Any]=0.0				,   lowerCamelCase			:    List[str]=0.0				,   lowerCamelCase			:    Optional[Any]=2				,   lowerCamelCase			:    List[str]=0.02				,   lowerCamelCase			:    Optional[Any]=0.0				,   lowerCamelCase			:    Optional[Any]=True				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    List[str]=True				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Dict=1				,   lowerCamelCase			:    List[Any]=0				,   lowerCamelCase			:    List[Any]=2				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   __A		:     Any												=		vocab_size
   __A		:     Optional[Any]												=		d_model
   __A		:     Dict												=		decoder_layers
   __A		:     Any												=		decoder_attention_heads
   __A		:     Optional[Any]												=		decoder_ffn_dim
   __A		:     str												=		activation_function
   __A		:     List[Any]												=		max_position_embeddings
   __A		:     Optional[Any]												=		dropout
   __A		:     Dict												=		attention_dropout
   __A		:     Union[str, Any]												=		activation_dropout
   __A		:     str												=		init_std
   __A		:     List[Any]												=		decoder_layerdrop
   __A		:     Dict												=		use_cache
   __A		:     List[Any]												=		scale_embedding
   __A		:     List[Any]												=		use_learned_position_embeddings
   __A		:     Any												=		layernorm_embedding

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





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



