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


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

A__   :		Union[str, Any] 		={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''transfo-xl'''
 lowerCamelCase				       =['''mems''']
 lowerCamelCase				       ={
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }
 def __init__(   self			:    Dict				,   lowerCamelCase			:    int=26_77_35				,   lowerCamelCase			:    List[str]=[2_00_00, 4_00_00, 20_00_00]				,   lowerCamelCase			:    Tuple=10_24				,   lowerCamelCase			:    Union[str, Any]=10_24				,   lowerCamelCase			:    Any=16				,   lowerCamelCase			:    str=64				,   lowerCamelCase			:    str=40_96				,   lowerCamelCase			:    Dict=4				,   lowerCamelCase			:    List[str]=False				,   lowerCamelCase			:    Optional[Any]=18				,   lowerCamelCase			:    Any=16_00				,   lowerCamelCase			:    Dict=10_00				,   lowerCamelCase			:    Dict=True				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    str=0				,   lowerCamelCase			:    Union[str, Any]=-1				,   lowerCamelCase			:    Optional[int]=True				,   lowerCamelCase			:    Optional[Any]=0.1				,   lowerCamelCase			:    Tuple=0.0				,   lowerCamelCase			:    Optional[Any]=True				,   lowerCamelCase			:    List[Any]="normal"				,   lowerCamelCase			:    Optional[int]=0.01				,   lowerCamelCase			:    Dict=0.01				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Dict=1e-5				,   lowerCamelCase			:    str=0				,   **lowerCamelCase			:    Any				,   ):



   """simple docstring"""





   __A		:     Any												=		vocab_size
   __A		:     Optional[int]												=		[]
   self.cutoffs.extend(lowerCamelCase       )
   if proj_share_all_but_first:
     __A		:     List[Any]												=		[False] + [True] * len(self.cutoffs       )
   else:
     __A		:     List[Any]												=		[False] + [False] * len(self.cutoffs       )
   __A		:     Union[str, Any]												=		d_model
   __A		:     Optional[Any]												=		d_embed
   __A		:     int												=		d_head
   __A		:     int												=		d_inner
   __A		:     Any												=		div_val
   __A		:     Optional[Any]												=		pre_lnorm
   __A		:     int												=		n_layer
   __A		:     Optional[int]												=		n_head
   __A		:     Optional[int]												=		mem_len
   __A		:     List[str]												=		same_length
   __A		:     int												=		attn_type
   __A		:     Any												=		clamp_len
   __A		:     Optional[Any]												=		sample_softmax
   __A		:     Tuple												=		adaptive
   __A		:     List[str]												=		dropout
   __A		:     str												=		dropatt
   __A		:     Optional[Any]												=		untie_r
   __A		:     Tuple												=		init
   __A		:     str												=		init_range
   __A		:     Any												=		proj_init_std
   __A		:     Any												=		init_std
   __A		:     int												=		layer_norm_epsilon
   super().__init__(eos_token_id=lowerCamelCase				,   **lowerCamelCase       )
 @property
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."       )
   return -1






 @max_position_embeddings.setter
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   raise NotImplementedError(
       f"The model {self.model_type} is one of the few models that has no sequence length limit."       )





'''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)



