


'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 							__magic_name__	(       tf.keras.layers.Layer ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None		):


          '''simple docstring'''





          super().__init__()
          __A	:       List[str]      =							pad_token_id
          __A	:       Optional[int]      =							max_length
          __A	:       Dict      =							vocab
          __A	:       Optional[Any]      =							merges
          __A	:       Optional[int]      =							BytePairTokenizer(lowerCamelCase			,     lowerCamelCase			,     sequence_length=lowerCamelCase		)


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     *lowerCamelCase			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[str]      =							[" ".join(lowerCamelCase		) for m in tokenizer.bpe_ranks.keys()]
          __A	:       int      =							tokenizer.get_vocab()
          return cls(lowerCamelCase			,     lowerCamelCase			,     *lowerCamelCase			,     **lowerCamelCase		)


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     *lowerCamelCase			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       str      =							GPTaTokenizer.from_pretrained(lowerCamelCase			,     *lowerCamelCase			,     **lowerCamelCase		)
          return cls.from_tokenizer(lowerCamelCase			,     *lowerCamelCase			,     **lowerCamelCase		)


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase		):


          '''simple docstring'''





          return cls(**lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return {
              "vocab": self.vocab,
              "merges": self.merges,
              "max_length": self.max_length,
              "pad_token_id": self.pad_token_id,
          }


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A	:       Any      =							self.tf_tokenizer(lowerCamelCase		)
          __A	:       Any      =							tf.ones_like(lowerCamelCase		)

          if self.pad_token_id is not None:
               # pad the tokens up to max length
               __A	:       Any      =							max_length if max_length is not None else self.max_length

               if max_length is not None:
                    __A  ,__A	:       int      =							pad_model_inputs(
                        lowerCamelCase			,     max_seq_length=lowerCamelCase			,     pad_value=self.pad_token_id		)

          return {"attention_mask": attention_mask, "input_ids": input_ids}





'''simple docstring'''


import argparse

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


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

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

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

