






import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				# Initialise PyTorch model
				lowercase__    =			BigBirdConfig.from_json_file(lowerCamelCase_				)
				print(F"""Building PyTorch model from configuration: {config}"""				)

				if is_trivia_qa:
								lowercase__    =			BigBirdForQuestionAnswering(lowerCamelCase_				)
				else:
								lowercase__    =			BigBirdForPreTraining(lowerCamelCase_				)

				# Load weights from tf checkpoint
				load_tf_weights_in_big_bird(lowerCamelCase_		,   lowerCamelCase_		,   is_trivia_qa=lowerCamelCase_				)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
				model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							A__	:			Tuple			   = 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(
							    '--big_bird_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.'
							)
							parser.add_argument(
							    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
							)
							A__	:			Optional[int]			   = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
							)












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


A__	:			List[Any]			   = logging.get_logger(__name__)

A__	:			str			   = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__	:			int			   = {
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

A__	:			Optional[Any]			   = {
    'distilbert-base-uncased': 5_12,
    'distilbert-base-uncased-distilled-squad': 5_12,
    'distilbert-base-cased': 5_12,
    'distilbert-base-cased-distilled-squad': 5_12,
    'distilbert-base-german-cased': 5_12,
    'distilbert-base-multilingual-cased': 5_12,
}


A__	:			List[str]			   = {
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       VOCAB_FILES_NAMES
				lowercase__											=       PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=       PRETRAINED_INIT_CONFIGURATION
				lowercase__											=       ["""input_ids""", """attention_mask"""]
				lowercase__											=       DistilBertTokenizer




				def __init__(     self						:    List[Any],  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Dict=None,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Optional[int]="[UNK]",  lowerCamelCase						:    Optional[Any]="[SEP]",  lowerCamelCase						:    List[Any]="[PAD]",  lowerCamelCase						:    Any="[CLS]",  lowerCamelCase						:    Union[str, Any]="[MASK]",  lowerCamelCase						:    str=True,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Union[str, Any],  ):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,  tokenizer_file=lowerCamelCase,  do_lower_case=lowerCamelCase,  unk_token=lowerCamelCase,  sep_token=lowerCamelCase,  pad_token=lowerCamelCase,  cls_token=lowerCamelCase,  mask_token=lowerCamelCase,  tokenize_chinese_chars=lowerCamelCase,  strip_accents=lowerCamelCase,  **lowerCamelCase,  )

								lowercase__    =			json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
								if (
								    normalizer_state.get('''lowercase''',  lowerCamelCase  ) != do_lower_case
								    or normalizer_state.get('''strip_accents''',  lowerCamelCase  ) != strip_accents
								    or normalizer_state.get('''handle_chinese_chars''',  lowerCamelCase  ) != tokenize_chinese_chars
								):
												lowercase__    =			getattr(lowerCamelCase,  normalizer_state.pop('''type'''  )  )
												lowercase__    =			do_lower_case
												lowercase__    =			strip_accents
												lowercase__    =			tokenize_chinese_chars
												lowercase__    =			normalizer_class(**lowerCamelCase  )

								lowercase__    =			do_lower_case




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								lowercase__    =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''simple docstring'''
								lowercase__    =			[self.sep_token_id]
								lowercase__    =			[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep  ) * [0]
								return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

								'''simple docstring'''
								lowercase__    =			self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase  )
								return tuple(lowerCamelCase  )




