
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					np.inf

      def set_batch_size(UpperCamelCase__		) -> None:
            nonlocal batch_size
            if isinstance(_UpperCamelCase			,		_UpperCamelCase		):
                  UpperCamelCase__     : Any											=					min(_UpperCamelCase			,		config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
            elif isinstance(_UpperCamelCase			,		_UpperCamelCase		):
                  UpperCamelCase__     : Any											=					min(_UpperCamelCase			,		config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
            elif isinstance(_UpperCamelCase			,		_UpperCamelCase		) and feature.dtype == "binary":
                  UpperCamelCase__     : Any											=					min(_UpperCamelCase			,		config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

      _visit(_UpperCamelCase			,		_UpperCamelCase		)

      return None if batch_size is np.inf else batch_size




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Optional[int]:




           """simple docstring"""







           super().__init__(
               snake_case__    ,    split=snake_case__    ,    features=snake_case__    ,    cache_dir=snake_case__    ,    keep_in_memory=snake_case__    ,    streaming=snake_case__    ,    num_proc=snake_case__    ,    **snake_case__    ,    )
           UpperCamelCase__     : Dict											=					path_or_paths if isinstance(snake_case__    ,    snake_case__	) else {self.split: path_or_paths}
           UpperCamelCase__     : Any											=					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
           UpperCamelCase__     : Dict											=					Parquet(
               cache_dir=snake_case__    ,    data_files=snake_case__    ,    features=snake_case__    ,    hash=snake_case__    ,    **snake_case__    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           if self.streaming:
                 UpperCamelCase__     : Union[str, Any]											=					self.builder.as_streaming_dataset(split=self.split	)
           # Build regular (map-style) dataset
           else:
                 UpperCamelCase__     : Any											=					None
                 UpperCamelCase__     : int											=					None
                 UpperCamelCase__     : List[str]											=					None
                 UpperCamelCase__     : Optional[Any]											=					None

                 self.builder.download_and_prepare(
                     download_config=snake_case__    ,    download_mode=snake_case__    ,    verification_mode=snake_case__    ,    base_path=snake_case__    ,    num_proc=self.num_proc    ,    )
                 UpperCamelCase__     : Optional[int]											=					self.builder.as_dataset(
                     split=self.split    ,    verification_mode=snake_case__    ,    in_memory=self.keep_in_memory	)
           return dataset




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : int											=					dataset
           UpperCamelCase__     : List[Any]											=					path_or_buf
           UpperCamelCase__     : str											=					batch_size or get_writer_batch_size(dataset.features	)
           UpperCamelCase__     : Optional[Any]											=					parquet_writer_kwargs






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

           if isinstance(self.path_or_buf    ,    (str, bytes, os.PathLike)	):
                 with open(self.path_or_buf    ,    '''wb+'''	) as buffer:
                       UpperCamelCase__     : List[Any]											=					self._write(file_obj=snake_case__    ,    batch_size=snake_case__    ,    **self.parquet_writer_kwargs	)
           else:
                 UpperCamelCase__     : int											=					self._write(file_obj=self.path_or_buf    ,    batch_size=snake_case__    ,    **self.parquet_writer_kwargs	)
           return written






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					0
           UpperCamelCase__     : Dict											=					parquet_writer_kwargs.pop('''path_or_buf'''    ,    snake_case__	)
           UpperCamelCase__     : List[Any]											=					self.dataset.features.arrow_schema

           UpperCamelCase__     : Optional[Any]											=					pq.ParquetWriter(snake_case__    ,    schema=snake_case__    ,    **snake_case__	)

           for offset in logging.tqdm(
               range(0    ,    len(self.dataset	)    ,    snake_case__	)    ,    unit='''ba'''    ,    disable=not logging.is_progress_bar_enabled()    ,    desc='''Creating parquet from Arrow format'''    ,    ):
                 UpperCamelCase__     : int											=					query_table(
                     table=self.dataset._data    ,    key=slice(snake_case__    ,    offset + batch_size	)    ,    indices=self.dataset._indices if self.dataset._indices is not None else None    ,    )
                 writer.write_table(snake_case__	)
                 written += batch.nbytes
           writer.close()
           return written





import argparse

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


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      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_     :    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_     :    Any     		=   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
      )

