


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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__UpperCAmelCase     							=						{
    'gpt-neox-20b': 2048,
}



class   lowerCamelCase				(A__      ):







		'''simple docstring'''



		_snake_case   :   Optional[int]							=  VOCAB_FILES_NAMES
		_snake_case   :   Optional[Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Optional[Any]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   List[Any]							=  ['''input_ids''', '''attention_mask''']



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="<|endoftext|>"    ,		_UpperCamelCase="<|endoftext|>"    ,		_UpperCamelCase="<|endoftext|>"    ,		_UpperCamelCase=False    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    __snake_case    ,		__snake_case    ,		tokenizer_file=__snake_case    ,		unk_token=__snake_case    ,		bos_token=__snake_case    ,		eos_token=__snake_case    ,		add_prefix_space=__snake_case    ,		**__snake_case    ,		)

							UpperCAmelCase_   :				Union[str, Any]						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
							if pre_tok_state.get('add_prefix_space'    ,		__snake_case       ) != add_prefix_space:
												UpperCAmelCase_   :				Tuple						=						getattr(__snake_case    ,		pre_tok_state.pop('type'       )       )
												UpperCAmelCase_   :				str						=						add_prefix_space
												UpperCAmelCase_   :				Optional[Any]						=						pre_tok_class(**__snake_case       )

							UpperCAmelCase_   :				int						=						add_prefix_space



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							UpperCAmelCase_   :				Tuple						=						self._tokenizer.model.save(__snake_case    ,		name=__snake_case       )
							return tuple(__snake_case       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[int]:
							UpperCAmelCase_   :				int						=						[]
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(__snake_case    ,		add_special_tokens=__snake_case       ) + [self.eos_token_id]       )

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














import argparse

import torch

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


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

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

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


if __name__ == "__main__":
		__UpperCAmelCase     							=						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.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






