

'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  NestedDataStructureLike[PathLike]      ,lowercase__     :  Optional[NamedSplit] = None      ,lowercase__     :  Optional[Features] = None      ,lowercase__     :  str = None      ,lowercase__     :  bool = False      ,lowercase__     :  bool = False      ,lowercase__     :  Optional[str] = None      ,lowercase__     :  Optional[int] = None      ,**lowercase__     :  Optional[int]      ,):
   super().__init__(
       lowercase__      ,split=lowercase__      ,features=lowercase__      ,cache_dir=lowercase__      ,keep_in_memory=lowercase__      ,streaming=lowercase__      ,num_proc=lowercase__      ,**lowercase__      ,)
   __lowercase													=				field
   __lowercase													=				path_or_paths if isinstance(lowercase__      ,lowercase__    ) else {self.split: path_or_paths}
   __lowercase													=				Json(
       cache_dir=lowercase__      ,data_files=lowercase__      ,features=lowercase__      ,field=lowercase__      ,**lowercase__      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   # Build iterable dataset
   if self.streaming:
    __lowercase													=				self.builder.as_streaming_dataset(split=self.split    )
   # Build regular (map-style) dataset
   else:
    __lowercase													=				None
    __lowercase													=				None
    __lowercase													=				None
    __lowercase													=				None

    self.builder.download_and_prepare(
        download_config=lowercase__      ,download_mode=lowercase__      ,verification_mode=lowercase__      ,base_path=lowercase__      ,num_proc=self.num_proc      ,)
    __lowercase													=				self.builder.as_dataset(
        split=self.split      ,verification_mode=lowercase__      ,in_memory=self.keep_in_memory    )
   return dataset






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  str      ,lowercase__     :  Dataset      ,lowercase__     :  Union[PathLike, BinaryIO]      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  Optional[int] = None      ,**lowercase__     :  Any      ,):
   if num_proc is not None and num_proc <= 0:
    raise ValueError(F"num_proc {num_proc} must be an integer > 0."    )

   __lowercase													=				dataset
   __lowercase													=				path_or_buf
   __lowercase													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
   __lowercase													=				num_proc
   __lowercase													=				'''utf-8'''
   __lowercase													=				to_json_kwargs






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.to_json_kwargs.pop('''path_or_buf'''      ,lowercase__    )
   __lowercase													=				self.to_json_kwargs.pop('''orient'''      ,'''records'''    )
   __lowercase													=				self.to_json_kwargs.pop('''lines'''      ,True if orient == '''records''' else False    )
   __lowercase													=				self.to_json_kwargs.pop('''index'''      ,False if orient in ['''split''', '''table'''] else True    )
   __lowercase													=				self.to_json_kwargs.pop('''compression'''      ,lowercase__    )

   if compression not in [None, "infer", "gzip", "bz2", "xz"]:
    raise NotImplementedError(F"`datasets` currently does not support {compression} compression"    )

   if isinstance(self.path_or_buf      ,(str, bytes, os.PathLike)    ):
    with fsspec.open(self.path_or_buf      ,'''wb'''      ,compression=lowercase__    ) as buffer:
     __lowercase													=				self._write(file_obj=lowercase__      ,orient=lowercase__      ,lines=lowercase__      ,index=lowercase__      ,**self.to_json_kwargs    )
   else:
    if compression:
     raise NotImplementedError(
         F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
         ''' was passed. Please provide a local path instead.'''    )
    __lowercase													=				self._write(
        file_obj=self.path_or_buf      ,orient=lowercase__      ,lines=lowercase__      ,index=lowercase__      ,**self.to_json_kwargs    )
   return written






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  str    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				args

   __lowercase													=				query_table(
       table=self.dataset.data      ,key=slice(lowercase__      ,offset + self.batch_size    )      ,indices=self.dataset._indices      ,)
   __lowercase													=				batch.to_pandas().to_json(
       path_or_buf=lowercase__      ,orient=lowercase__      ,lines=lowercase__      ,index=lowercase__      ,**lowercase__    )
   if not json_str.endswith('''\n'''    ):
    json_str += "\n"
   return json_str.encode(self.encoding    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  BinaryIO      ,lowercase__     :  int      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,**lowercase__     :  Any      ,):
   __lowercase													=				0

   if self.num_proc is None or self.num_proc == 1:
    for offset in logging.tqdm(
        range(0      ,len(self.dataset    )      ,self.batch_size    )      ,unit='''ba'''      ,disable=not logging.is_progress_bar_enabled()      ,desc='''Creating json from Arrow format'''      ,):
     __lowercase													=				self._batch_json((offset, orient, lines, index, to_json_kwargs)    )
     written += file_obj.write(lowercase__    )
   else:
    __lowercase						,			__lowercase													=				len(self.dataset    ), self.batch_size
    with multiprocessing.Pool(self.num_proc    ) as pool:
     for json_str in logging.tqdm(
         pool.imap(
             self._batch_json      ,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0      ,lowercase__      ,lowercase__    )]      ,)      ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size      ,unit='''ba'''      ,disable=not logging.is_progress_bar_enabled()      ,desc='''Creating json from Arrow format'''      ,):
      written += file_obj.write(lowercase__    )

   return written




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

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


if __name__ == "__main__":
 lowerCAmelCase__      		=			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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

