

'''simple docstring'''





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


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


A_	= logging.get_logger(__name__)

A_	= {"tokenizer_file": "tokenizer.json"}

A_	= {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_           =					PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_           =					['input_ids', 'attention_mask']
		SCREAMING_SNAKE_CASE_           =					None
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_="<unk>"   ,			SCREAMING_SNAKE_CASE_="<s>"   ,			SCREAMING_SNAKE_CASE_="</s>"   ,			SCREAMING_SNAKE_CASE_="<pad>"   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Dict:






							'''simple docstring'''



							super().__init__(
							    SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			tokenizer_file=SCREAMING_SNAKE_CASE_   ,			unk_token=SCREAMING_SNAKE_CASE_   ,			bos_token=SCREAMING_SNAKE_CASE_   ,			eos_token=SCREAMING_SNAKE_CASE_   ,			pad_token=SCREAMING_SNAKE_CASE_   ,			add_prefix_space=SCREAMING_SNAKE_CASE_   ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)
							lowerCamelCase_  				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
							if pre_tok_state.get('add_prefix_space'   ,			SCREAMING_SNAKE_CASE_		) != add_prefix_space:
												lowerCamelCase_  				=      getattr(SCREAMING_SNAKE_CASE_   ,			pre_tok_state.pop('type'		)		)
												lowerCamelCase_  				=      add_prefix_space
												lowerCamelCase_  				=      pre_tok_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      add_prefix_space
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     BatchEncoding:






							'''simple docstring'''



							lowerCamelCase_  				=      kwargs.get('is_split_into_words'   ,			SCREAMING_SNAKE_CASE_		)
							if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
												    ' pretokenized inputs.'		)

							return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     BatchEncoding:






							'''simple docstring'''



							lowerCamelCase_  				=      kwargs.get('is_split_into_words'   ,			SCREAMING_SNAKE_CASE_		)

							if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
												    ' pretokenized inputs.'		)

							return super()._encode_plus(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     Tuple[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_   ,			name=SCREAMING_SNAKE_CASE_		)
							return tuple(SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     List[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      []
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_		) + [self.eos_token_id]		)

							if len(SCREAMING_SNAKE_CASE_		) > self.model_max_length:
												lowerCamelCase_  				=      input_ids[-self.model_max_length :]
							return input_ids






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,				UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, 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(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT 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						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




