

'''simple docstring'''





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCamelCase_    						=       logging.get_logger(__name__)


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

UpperCamelCase_    						=       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase_    						=       {
    """allenai/led-base-16384""": 16384,
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    VOCAB_FILES_NAMES
				lowerCamelCase_		       =    PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_		       =    LEDTokenizer
				lowerCamelCase_		       =    ['input_ids', 'attention_mask']







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		Optional[Any]=None		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Dict="replace"		,					UpperCAmelCase__     :		Any="<s>"		,					UpperCAmelCase__     :		Any="</s>"		,					UpperCAmelCase__     :		Union[str, Any]="</s>"		,					UpperCAmelCase__     :		Dict="<s>"		,					UpperCAmelCase__     :		List[str]="<unk>"		,					UpperCAmelCase__     :		List[Any]="<pad>"		,					UpperCAmelCase__     :		Tuple="<mask>"		,					UpperCAmelCase__     :		int=False		,					UpperCAmelCase__     :		int=True		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										super().__init__(
										    UpperCAmelCase__		,					UpperCAmelCase__		,					tokenizer_file=UpperCAmelCase__		,					errors=UpperCAmelCase__		,					bos_token=UpperCAmelCase__		,					eos_token=UpperCAmelCase__		,					sep_token=UpperCAmelCase__		,					cls_token=UpperCAmelCase__		,					unk_token=UpperCAmelCase__		,					pad_token=UpperCAmelCase__		,					mask_token=UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__		,					trim_offsets=UpperCAmelCase__		,					**UpperCAmelCase__		,					)

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

										lowercase :							str    		=add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										lowercase :							Tuple    		='''post_processor'''
										lowercase :							Union[str, Any]    		=getattr(self.backend_tokenizer		,					UpperCAmelCase__		,					UpperCAmelCase__   )
										if tokenizer_component_instance:
																lowercase :							List[str]    		=json.loads(tokenizer_component_instance.__getstate__()   )

																# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																if "sep" in state:
																						lowercase :							int    		=tuple(state['''sep''']   )
																if "cls" in state:
																						lowercase :							Tuple    		=tuple(state['''cls''']   )

																lowercase :							Union[str, Any]    		=False

																if state.get('''add_prefix_space'''		,					UpperCAmelCase__   ) != add_prefix_space:
																						lowercase :							List[Any]    		=add_prefix_space
																						lowercase :							List[str]    		=True

																if state.get('''trim_offsets'''		,					UpperCAmelCase__   ) != trim_offsets:
																						lowercase :							Tuple    		=trim_offsets
																						lowercase :							Optional[int]    		=True

																if changes_to_apply:
																						lowercase :							Any    		=getattr(UpperCAmelCase__		,					state.pop('''type'''   )   )
																						lowercase :							str    		=component_class(**UpperCAmelCase__   )
																						setattr(self.backend_tokenizer		,					UpperCAmelCase__		,					UpperCAmelCase__   )







				@property
				# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										if self._mask_token is None:
																if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''   )
																return None
										return str(self._mask_token   )







				@mask_token.setter
				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=AddedToken(UpperCAmelCase__		,					lstrip=UpperCAmelCase__		,					rstrip=UpperCAmelCase__   ) if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ) else value
										lowercase :							Tuple    		=value







				def  lowerCamelCase_	(       self     :		int		,					*UpperCAmelCase__     :		List[str]		,					**UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=kwargs.get('''is_split_into_words'''		,					UpperCAmelCase__   )

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

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







				def  lowerCamelCase_	(       self     :		Optional[int]		,					*UpperCAmelCase__     :		Any		,					**UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=kwargs.get('''is_split_into_words'''		,					UpperCAmelCase__   )

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

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







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Optional[str] = None   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self._tokenizer.model.save(UpperCAmelCase__		,					name=UpperCAmelCase__   )
										return tuple(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Tuple=None   ):
										'''simple docstring'''
										lowercase :							str    		=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''
										lowercase :							List[str]    		=[self.sep_token_id]
										lowercase :							List[str]    		=[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 + sep + token_ids_a + sep   ) * [0]







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Union[Dict[str, EncodedInput], BatchEncoding]		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					):
										'''simple docstring'''
										lowercase :							Optional[int]    		=super()._pad(
										    encoded_inputs=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding_strategy=UpperCAmelCase__		,					pad_to_multiple_of=UpperCAmelCase__		,					return_attention_mask=UpperCAmelCase__		,					)

										# Load from model defaults
										if return_attention_mask is None:
																lowercase :							int    		='''attention_mask''' in self.model_input_names

										if return_attention_mask and "global_attention_mask" in encoded_inputs:
																lowercase :							int    		=encoded_inputs[self.model_input_names[0]]
																# `global_attention_mask` need to have the same length as other (sequential) inputs.
																lowercase :							Optional[int]    		=len(encoded_inputs['''global_attention_mask''']   ) != len(UpperCAmelCase__   )

																if needs_to_be_padded:
																						lowercase :							List[Any]    		=len(UpperCAmelCase__   ) - len(encoded_inputs['''global_attention_mask''']   )

																						if self.padding_side == "right":
																												# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																												lowercase :							Dict    		=(
																												    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																												)
																						elif self.padding_side == "left":
																												lowercase :							Union[str, Any]    		=[-1] * difference + encoded_inputs[
																												    '''global_attention_mask'''
																												]
																						else:
																												raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

										return encoded_inputs












import argparse

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


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

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

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


if __name__ == "__main__":
       a__: Optional[int]							   =						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__: Tuple							   =						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
       )





