



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 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Features     ):
				"""simple docstring"""







				__a	  =		np.inf

				def set_batch_size(_SCREAMING_SNAKE_CASE							:      FeatureType     ) -> None:
								nonlocal batch_size
								if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__     ):
												__a	  =		min(SCREAMING_SNAKE_CASE__					,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
								elif isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__     ):
												__a	  =		min(SCREAMING_SNAKE_CASE__					,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
								elif isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__     ) and feature.dtype == "binary":
												__a	  =		min(SCREAMING_SNAKE_CASE__					,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

				_visit(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__     )

				return None if batch_size is np.inf else batch_size






class  SCREAMING_SNAKE_CASE      (				_UpperCamelCase       ):





							def __init__( self		:		int   ,					__lowercase		:		NestedDataStructureLike[PathLike]   ,					__lowercase		:		Optional[NamedSplit] = None   ,					__lowercase		:		Optional[Features] = None   ,					__lowercase		:		str = None   ,					__lowercase		:		bool = False   ,					__lowercase		:		bool = False   ,					__lowercase		:		Optional[int] = None   ,					**__lowercase		:		Any   ,					):

											'''simple docstring'''
											super().__init__(
											    __lowercase   ,					split=__lowercase   ,					features=__lowercase   ,					cache_dir=__lowercase   ,					keep_in_memory=__lowercase   ,					streaming=__lowercase   ,					num_proc=__lowercase   ,					**__lowercase   ,					)
											__a	  =		path_or_paths if isinstance(__lowercase   ,					__lowercase						) else {self.split: path_or_paths}
											__a	  =		_PACKAGED_DATASETS_MODULES["parquet"][1]
											__a	  =		Parquet(
											    cache_dir=__lowercase   ,					data_files=__lowercase   ,					features=__lowercase   ,					hash=__lowercase   ,					**__lowercase   ,					)







							def     UpperCamelCase_       ( self		:		Optional[int]						):

											'''simple docstring'''
											# Build iterable dataset
											if self.streaming:
															__a	  =		self.builder.as_streaming_dataset(split=self.split						)
											# Build regular (map-style) dataset
											else:
															__a	  =		None
															__a	  =		None
															__a	  =		None
															__a	  =		None

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






class  SCREAMING_SNAKE_CASE      :





							def __init__( self		:		List[Any]   ,					__lowercase		:		Dataset   ,					__lowercase		:		Union[PathLike, BinaryIO]   ,					__lowercase		:		Optional[int] = None   ,					**__lowercase		:		Optional[Any]   ,					):

											'''simple docstring'''
											__a	  =		dataset
											__a	  =		path_or_buf
											__a	  =		batch_size or get_writer_batch_size(dataset.features						)
											__a	  =		parquet_writer_kwargs





							def     UpperCamelCase_       ( self		:		str						):

											'''simple docstring'''
											__a	  =		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:
																			__a	  =		self._write(file_obj=__lowercase   ,					batch_size=__lowercase   ,					**self.parquet_writer_kwargs						)
											else:
															__a	  =		self._write(file_obj=self.path_or_buf   ,					batch_size=__lowercase   ,					**self.parquet_writer_kwargs						)
											return written







							def     UpperCamelCase_       ( self		:		List[Any]   ,					__lowercase		:		BinaryIO   ,					__lowercase		:		int   ,					**__lowercase		:		Tuple						):

											'''simple docstring'''
											__a	  =		0
											__a	  =		parquet_writer_kwargs.pop("""path_or_buf"""   ,					__lowercase						)
											__a	  =		self.dataset.features.arrow_schema

											__a	  =		pq.ParquetWriter(__lowercase   ,					schema=__lowercase   ,					**__lowercase						)

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





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS 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."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
