





from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     List[str]
						__magic_name__:     Optional[str]	 =    None
						# Automatically constructed
						__magic_name__:     ClassVar[str]	 =    "dict"
						__magic_name__:     ClassVar[Any]	 =    None
						__magic_name__:     str	 =    field(default="Translation"    ,			init=snake_case_    ,			repr=snake_case_   )






						def __call__( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							return pa.struct({lang: pa.string() for lang in sorted(self.languages		)}		)






						def UpperCAmelCase_			( self : Dict		)							->      Union["FeatureType", Dict[str, "FeatureType"]]:







							"""simple docstring"""


							from .features import Value

							return {k: Value('string'		) for k in sorted(self.languages		)}
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     Optional[List]	 =    None
						__magic_name__:     Optional[int]	 =    None
						__magic_name__:     Optional[str]	 =    None
						# Automatically constructed
						__magic_name__:     ClassVar[str]	 =    "dict"
						__magic_name__:     ClassVar[Any]	 =    None
						__magic_name__:     str	 =    field(default="TranslationVariableLanguages"    ,			init=snake_case_    ,			repr=snake_case_   )






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     int     =		sorted(set(self.languages		)		) if self.languages else None
							snake_case_						:     Optional[int]     =		len(self.languages		) if self.languages else None






						def __call__( self : int		)							->      List[Any]:







							"""simple docstring"""


							return pa.struct({'language': pa.list_(pa.string()		), 'translation': pa.list_(pa.string()		)}		)






						def UpperCAmelCase_			( self : Tuple						,				_A : int		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Dict     =		set(self.languages		)
							if self.languages and set(_A		) - lang_set:
								raise ValueError(
								    F"""Some languages in example ({', '.join(sorted(set(_A		) - lang_set		)		)}) are not in valid set ({', '.join(_A		)})."""		)

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							snake_case_						:     List[Any]     =		[]
							for lang, text in translation_dict.items():
								if isinstance(_A						,				_A		):
									translation_tuples.append((lang, text)		)
								else:
									translation_tuples.extend([(lang, el) for el in text]		)

        # Ensure translations are in ascending order by language code.
							snake_case_  ,snake_case_						:     Any     =		zip(*sorted(_A		)		)

							return {"language": languages, "translation": translations}






						def UpperCAmelCase_			( self : Dict		)							->      Union["FeatureType", Dict[str, "FeatureType"]]:







							"""simple docstring"""


							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('string'		)		),
							    "translation": Sequence(Value('string'		)		),
							}








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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_						:     Optional[int]     =		4
		snake_case_						:     List[str]     =		True
		# hparam_utils.py hparams
		snake_case_						:     Optional[Any]     =		0.664694
		snake_case_						:     Dict     =		0.207951
		snake_case_						:     Tuple     =		0.121194
		snake_case_						:     Dict     =		True
		snake_case_						:     int     =		True
		snake_case_						:     int     =		False
		snake_case_						:     str     =		0.0352513

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	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(__a				,	__a				,	__a				)

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

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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."""
						)
						_SCREAMING_SNAKE_CASE 		=						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,
						)

