






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


__lowerCamelCase :      str										=				logging.get_logger(__name__)

__lowerCamelCase :      int										=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :      Optional[int]										=				{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :      List[str]										=				{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





     """simple docstring"""






     a_     =		VOCAB_FILES_NAMES
     a_     =		PRETRAINED_VOCAB_FILES_MAP
     a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a_     =		["input_ids", "attention_mask"]
     a_     =		GPTaTokenizer



     def __init__( self					:  Optional[int]							,		__A					:  Any=None							,		__A					:  List[str]=None							,		__A					:  List[Any]=None							,		__A					:  str="<|endoftext|>"							,		__A					:  Tuple="<|endoftext|>"							,		__A					:  Any="<|endoftext|>"							,		__A					:  Tuple=False							,		**__A					:  Tuple							,		):
      super().__init__(
          __A							,		__A							,		tokenizer_file=__A							,		unk_token=__A							,		bos_token=__A							,		eos_token=__A							,		add_prefix_space=__A							,		**__A							,		)

      snake_case__			:				Any       =  kwargs.pop("add_bos_token"							,		__A	)

      snake_case__			:				Optional[int]       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get("add_prefix_space"							,		__A	) != add_prefix_space:
       snake_case__			:				Optional[Any]       =  getattr(__A							,		pre_tok_state.pop("type"	)	)
       snake_case__			:				str       =  add_prefix_space
       snake_case__			:				List[Any]       =  pre_tok_class(**__A	)

      snake_case__			:				List[Any]       =  add_prefix_space



     def 	_lowercase					( self					:  Optional[int]							,		*__A					:  int							,		**__A					:  Optional[Any]	):
      snake_case__			:				Tuple       =  kwargs.get("is_split_into_words"							,		__A	)
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__A							,		**__A	)



     def 	_lowercase					( self					:  List[Any]							,		*__A					:  List[Any]							,		**__A					:  Tuple	):
      snake_case__			:				int       =  kwargs.get("is_split_into_words"							,		__A	)

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__A							,		**__A	)



     def 	_lowercase					( self					:  List[Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
      snake_case__			:				Union[str, Any]       =  self._tokenizer.model.save(__A							,		name=__A	)
      return tuple(__A	)






     def 	_lowercase					( self					:  Tuple							,		__A					:  "Conversation"	):
      snake_case__			:				Tuple       =  []
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__A							,		add_special_tokens=__A	) + [self.eos_token_id]	)

      if len(__A	) > self.model_max_length:
       snake_case__			:				List[Any]       =  input_ids[-self.model_max_length :]
      return input_ids











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[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(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT 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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



