
'''simple docstring'''
from typing import Callable, Optional, Union

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


A__   :		Union[str, Any] 		=logging.get_logger(__name__)

A__   :		Dict 		={
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''xlm-prophetnet'''
 lowerCamelCase				       =['''past_key_values''']
 lowerCamelCase				       ={
     '''num_attention_heads''': '''num_encoder_attention_heads''',
 }
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    Optional[float] = 0.1				,   lowerCamelCase			:    Optional[Union[str, Callable]] = "gelu"				,   lowerCamelCase			:    Optional[int] = 3_05_22				,   lowerCamelCase			:    Optional[int] = 10_24				,   lowerCamelCase			:    Optional[int] = 40_96				,   lowerCamelCase			:    Optional[int] = 12				,   lowerCamelCase			:    Optional[int] = 16				,   lowerCamelCase			:    Optional[int] = 40_96				,   lowerCamelCase			:    Optional[int] = 12				,   lowerCamelCase			:    Optional[int] = 16				,   lowerCamelCase			:    Optional[float] = 0.1				,   lowerCamelCase			:    Optional[float] = 0.1				,   lowerCamelCase			:    Optional[int] = 5_12				,   lowerCamelCase			:    Optional[float] = 0.02				,   lowerCamelCase			:    Optional[bool] = True				,   lowerCamelCase			:    Optional[bool] = True				,   lowerCamelCase			:    Optional[int] = 0				,   lowerCamelCase			:    Optional[int] = 2				,   lowerCamelCase			:    Optional[int] = 32				,   lowerCamelCase			:    Optional[int] = 1_28				,   lowerCamelCase			:    Optional[bool] = False				,   lowerCamelCase			:    Optional[float] = 0.0				,   lowerCamelCase			:    Optional[bool] = True				,   lowerCamelCase			:    Optional[int] = 0				,   lowerCamelCase			:    Optional[int] = 1				,   lowerCamelCase			:    Optional[int] = 2				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   __A		:     List[Any]												=		vocab_size
   __A		:     Union[str, Any]												=		hidden_size
   __A		:     int												=		encoder_ffn_dim
   __A		:     Optional[int]												=		num_encoder_layers
   __A		:     Any												=		num_encoder_attention_heads
   __A		:     Optional[int]												=		decoder_ffn_dim
   __A		:     Optional[Any]												=		num_decoder_layers
   __A		:     Any												=		num_decoder_attention_heads
   __A		:     List[str]												=		max_position_embeddings
   __A		:     Tuple												=		init_std  # Normal(0, this parameter)
   __A		:     str												=		activation_function

   # parameters for xlmprophetnet
   __A		:     Tuple												=		ngram
   __A		:     Dict												=		num_buckets
   __A		:     Optional[Any]												=		relative_max_distance
   __A		:     str												=		disable_ngram_loss
   __A		:     List[str]												=		eps

   # 3 Types of Dropout
   __A		:     int												=		attention_dropout
   __A		:     Union[str, Any]												=		activation_dropout
   __A		:     List[str]												=		dropout

   __A		:     List[str]												=		use_cache

   super().__init__(
       pad_token_id=lowerCamelCase				,   bos_token_id=lowerCamelCase				,   eos_token_id=lowerCamelCase				,   is_encoder_decoder=lowerCamelCase				,   add_cross_attention=lowerCamelCase				,   decoder_start_token_id=lowerCamelCase				,   **lowerCamelCase				,   )
 @property
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   return self.num_encoder_layers + self.num_decoder_layers






 @num_hidden_layers.setter
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    List[str]       ):



   """simple docstring"""





   raise NotImplementedError(
       """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
       """ `num_decoder_layers`."""       )





'''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)



