code: 
  
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  SCREAMING_SNAKE_CASE : Any          =    'none'
  SCREAMING_SNAKE_CASE : List[str]          =    'none'
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Optional[Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Any			=	value.split(SCREAMING_SNAKE_CASE_      )
  							return len(SCREAMING_SNAKE_CASE_      ) > 1 and any((len(SCREAMING_SNAKE_CASE_      ) == 3 for x in split)      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  														return value
  							if isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  														return float(SCREAMING_SNAKE_CASE_      )
  							if not isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  														raise ValueError("""Argument value is not a string. Can\'t parse it as float"""      )
  							a_       :   Tuple			=	value
  
  							try:
  														# Example: 1,000.7
  														if "." in sanitized and "," in sanitized:
  																					return float(sanitized.replace(""","""	,	""""""      )      )
  														# 1,000
  														if "," in sanitized and _split_thousands(""","""	,	SCREAMING_SNAKE_CASE_      ):
  																					return float(sanitized.replace(""","""	,	""""""      )      )
  														# 5,5556
  														if "," in sanitized and sanitized.count(""","""      ) == 1 and not _split_thousands(""","""	,	SCREAMING_SNAKE_CASE_      ):
  																					return float(sanitized.replace(""","""	,	"""."""      )      )
  														# 0.0.0.1
  														if sanitized.count("""."""      ) > 1:
  																					return float(sanitized.replace("""."""	,	""""""      )      )
  														# 0,0,0,1
  														if sanitized.count(""","""      ) > 1:
  																					return float(sanitized.replace(""","""	,	""""""      )      )
  														return float(SCREAMING_SNAKE_CASE_      )
  							except ValueError:
  														# Avoid adding the sanitized value in the error message.
  														raise ValueError("""Unable to convert value to float"""      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if answer is None:
  														return None
  							try:
  														a_       :   Tuple			=	convert_to_float(SCREAMING_SNAKE_CASE_      )
  														if isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ) and math.isnan(SCREAMING_SNAKE_CASE_      ):
  																					return None
  														return value
  							except ValueError:
  														return answer.lower()
  
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      'text': lambda x: x,
      'real': convert_to_float,
  }
  
  
  
  
  
  class 					snake_case__  ( enum.Enum       ):
  
  	'''simple docstring'''
  
  
  
  
  
  	UpperCAmelCase   :							int         = 0
  	UpperCAmelCase   :							Dict         = 1
  	UpperCAmelCase   :							Dict         = 2
  	UpperCAmelCase   :							str         = 3
  	UpperCAmelCase   :							Optional[Any]         = 4
  	UpperCAmelCase   :							List[str]         = 5
  
  
  
  
  
  class 					snake_case__  ( enum.Enum       ):
  
  	'''simple docstring'''
  
  
  
  
  
  	UpperCAmelCase   :							Dict         = 0
  	UpperCAmelCase   :							Union[str, Any]         = 1
  	UpperCAmelCase   :							Tuple         = 2
  
  
  
  
  
  @dataclasses.dataclass
  class 					snake_case__  :
  
  	'''simple docstring'''
  
  
  
  
  
  	UpperCAmelCase   :							Text
  	UpperCAmelCase   :							_Operator
  	UpperCAmelCase   :							Any
  
  
  
  
  
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							return list(_TOKENIZER.findall(x.lower()      )      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: List[str]	,	SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if operator == _Operator.EQUALS:
  														return src == tgt
  							elif operator == _Operator.GREATER:
  														return src > tgt
  							elif operator == _Operator.LESSER:
  														return src < tgt
  							raise ValueError(F"""Unknown operator: {operator}"""      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: Dict      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[str]			=	table["""types"""]
  							return _TYPE_CONVERTER[types[column]](SCREAMING_SNAKE_CASE_      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							return isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Dict      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							for cond in conditions:
  														a_       :   Any			=	row[cond.column]
  
  														a_       :   Union[str, Any]			=	_parse_value(SCREAMING_SNAKE_CASE_	,	cond.column	,	cond.cmp_value      )
  
  														if _is_string(SCREAMING_SNAKE_CASE_      ) and _is_string(SCREAMING_SNAKE_CASE_      ):
  																					a_       :   List[str]			=	_normalize_for_match(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Any			=	_normalize_for_match(SCREAMING_SNAKE_CASE_      )
  
  														if not isinstance(SCREAMING_SNAKE_CASE_	,	type(SCREAMING_SNAKE_CASE_      )      ):
  																					raise ValueError("""Type difference {} != {}""".format(type(SCREAMING_SNAKE_CASE_      )	,	type(SCREAMING_SNAKE_CASE_      )      )      )
  
  														if not _compare(cond.operator	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  																					return False
  							return True
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if not answer_coordinates:
  														if aggregation_op == _Aggregation.COUNT:
  																					return 0.0
  														else:
  																					return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
  							if aggregation_op == _Aggregation.COUNT:
  														return float(len(SCREAMING_SNAKE_CASE_      )      )
  
  							# If we have just one answer, if float returns it or try a conversion.
  							a_       :   int			=	[table["""rows"""][i][j] for (i, j) in answer_coordinates]
  							if len(SCREAMING_SNAKE_CASE_      ) == 1:
  														try:
  																					return convert_to_float(values[0]      )
  														except ValueError as e:
  																					if aggregation_op != _Aggregation.NONE:
  																												raise e
  
  							if aggregation_op == _Aggregation.NONE:
  														return None
  
  							# Other aggregation only support numeric values. Bail out if we have strings.
  							if not all((isinstance(SCREAMING_SNAKE_CASE_	,	(int, float)      ) for v in values)      ):
  														return None
  
  							if aggregation_op == _Aggregation.SUM:
  														return float(sum(SCREAMING_SNAKE_CASE_      )      )
  							elif aggregation_op == _Aggregation.AVERAGE:
  														return sum(SCREAMING_SNAKE_CASE_      ) / len(SCREAMING_SNAKE_CASE_      )
  							else:
  														raise ValueError(F"""Unknown aggregation: {aggregation_op}"""      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[str]	,	SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   str			=	sql_query["""agg"""]
  							if aggregation_op_index >= 3:
  														a_       :   Any			=	_Aggregation(SCREAMING_SNAKE_CASE_      )
  							else:
  														a_       :   Any			=	_Aggregation.NONE
  
  							a_       :   Dict			=	sql_query["""sel"""]
  							a_       :   Optional[Any]			=	[
  							    _Condition(SCREAMING_SNAKE_CASE_	,	_Operator(SCREAMING_SNAKE_CASE_      )	,	SCREAMING_SNAKE_CASE_      )
  							    for column, operator, cmp_value in zip(
  							        sql_query["""conds"""]["""column_index"""]	,	sql_query["""conds"""]["""operator_index"""]	,	sql_query["""conds"""]["""condition"""]      )
  							]
  
  							a_       :   Optional[Any]			=	[]
  							for row in range(len(table["""rows"""]      )      ):
  														if _respect_conditions(SCREAMING_SNAKE_CASE_	,	table["""rows"""][row]	,	SCREAMING_SNAKE_CASE_      ):
  																					indices.append((row, target_column)      )
  
  							if not indices:
  														return [], aggregation_op
  
  							if len(SCREAMING_SNAKE_CASE_      ) == 1:
  														return indices, aggregation_op
  
  							# Parsing of MIN/MAX.
  							if aggregation_op_index in (1, 2):
  														a_       :   int			=	{2: min, 1: max}
  														a_       :   List[Any]			=	[(table["""rows"""][i][j], index) for index, (i, j) in enumerate(SCREAMING_SNAKE_CASE_      )]
  														a_       :   str			=	functools.reduce(operators[sql_query["""agg"""]]	,	SCREAMING_SNAKE_CASE_      )
  
  														a_       :   Optional[Any]			=	[indices[reduced[1]]]
  														return ret, _Aggregation.NONE
  
  							return indices, aggregation_op
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: int      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if float_answer is not None:
  														return [str(SCREAMING_SNAKE_CASE_      )]
  							return [str(table["""real_rows"""][r][c]      ) for r, c in answer_coordinates]
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: Optional[Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_		,				a_       :   List[Any]			=	_get_answer_coordinates(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							a_       :   Any			=	_get_float_answer(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							a_       :   Union[str, Any]			=	_get_answer_text(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							# keep the original data the same with TaPas
  							if len(SCREAMING_SNAKE_CASE_      ) == 0:
  														a_       :   List[str]			=	[EMPTY_ANSWER]
  							return answer_text
  
  
  
style_context: 
  
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  SCREAMING_SNAKE_CASE : str          =    "1.5.0"
  
  SCREAMING_SNAKE_CASE : Dict          =    ["vision-encoder-decoder"]
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if is_optimum_available():
  														from optimum.version import __version__ as optimum_version
  
  														a_       :   List[Any]			=	version.parse(SCREAMING_SNAKE_CASE_      )
  														if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE_      ):
  																					raise RuntimeError(
  																					    F"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
  																					    """can upgrade optimum by running: pip install -U optimum[exporters]"""      )
  							else:
  														raise RuntimeError(
  														    """transformers.onnx requires optimum to run, you can install the library by running: pip install """
  														    """optimum[exporters]"""      )
  							a_       :   Tuple			=	[
  							    sys.executable,
  							    """-m""",
  							    """optimum.exporters.onnx""",
  							    F"""--model {args.model}""",
  							    F"""--task {args.feature}""",
  							    F"""--framework {args.framework}""" if args.framework is not None else """""",
  							    F"""{args.output}""",
  							]
  							a_       :   List[str]			=	subprocess.Popen(""" """.join(SCREAMING_SNAKE_CASE_      )	,	stdout=subprocess.PIPE	,	shell=SCREAMING_SNAKE_CASE_      )
  							proc.wait()
  
  							logger.info(
  							    """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
  							    """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
  							    """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Any			=	args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""      )
  							if not args.output.parent.exists():
  														args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE_      )
  
  							# Allocate the model
  							a_       :   int			=	FeaturesManager.get_model_from_feature(
  							    args.feature	,	args.model	,	framework=args.framework	,	cache_dir=args.cache_dir      )
  
  							a_		,				a_       :   str			=	FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE_	,	feature=args.feature      )
  							a_       :   Dict			=	model_onnx_config(model.config      )
  
  							if model_kind in ENCODER_DECODER_MODELS:
  														a_       :   Optional[Any]			=	model.get_encoder()
  														a_       :   Dict			=	model.get_decoder()
  
  														a_       :   List[Any]			=	onnx_config.get_encoder_config(encoder_model.config      )
  														a_       :   List[str]			=	onnx_config.get_decoder_config(
  														    encoder_model.config	,	decoder_model.config	,	feature=args.feature      )
  
  														if args.opset is None:
  																					a_       :   str			=	max(encoder_onnx_config.default_onnx_opset	,	decoder_onnx_config.default_onnx_opset      )
  
  														if args.opset < min(encoder_onnx_config.default_onnx_opset	,	decoder_onnx_config.default_onnx_opset      ):
  																					raise ValueError(
  																					    F"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
  																					    F""" {min(encoder_onnx_config.default_onnx_opset	,	decoder_onnx_config.default_onnx_opset      )} is required."""      )
  
  														a_       :   List[Any]			=	AutoFeatureExtractor.from_pretrained(args.model      )
  
  														a_		,				a_       :   List[str]			=	export(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.opset	,	args.output.parent.joinpath("""encoder_model.onnx"""      )	,	)
  
  														validate_model_outputs(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.output.parent.joinpath("""encoder_model.onnx"""      )	,	SCREAMING_SNAKE_CASE_	,	args.atol if args.atol else encoder_onnx_config.atol_for_validation	,	)
  
  														a_       :   Union[str, Any]			=	AutoTokenizer.from_pretrained(args.model      )
  
  														a_		,				a_       :   List[Any]			=	export(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.opset	,	args.output.parent.joinpath("""decoder_model.onnx"""      )	,	)
  
  														validate_model_outputs(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.output.parent.joinpath("""decoder_model.onnx"""      )	,	SCREAMING_SNAKE_CASE_	,	args.atol if args.atol else decoder_onnx_config.atol_for_validation	,	)
  														logger.info(
  														    F"""All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'      ).as_posix()},"""
  														    F""" {args.output.parent.joinpath('decoder_model.onnx'      ).as_posix()}"""      )
  
  							else:
  														# Instantiate the appropriate preprocessor
  														if args.preprocessor == "auto":
  																					a_       :   List[str]			=	get_preprocessor(args.model      )
  														elif args.preprocessor == "tokenizer":
  																					a_       :   Optional[int]			=	AutoTokenizer.from_pretrained(args.model      )
  														elif args.preprocessor == "image_processor":
  																					a_       :   List[str]			=	AutoImageProcessor.from_pretrained(args.model      )
  														elif args.preprocessor == "feature_extractor":
  																					a_       :   Optional[Any]			=	AutoFeatureExtractor.from_pretrained(args.model      )
  														elif args.preprocessor == "processor":
  																					a_       :   Union[str, Any]			=	AutoProcessor.from_pretrained(args.model      )
  														else:
  																					raise ValueError(F"""Unknown preprocessor type '{args.preprocessor}'"""      )
  
  														# Ensure the requested opset is sufficient
  														if args.opset is None:
  																					a_       :   List[str]			=	onnx_config.default_onnx_opset
  
  														if args.opset < onnx_config.default_onnx_opset:
  																					raise ValueError(
  																					    F"""Opset {args.opset} is not sufficient to export {model_kind}. """
  																					    F"""At least  {onnx_config.default_onnx_opset} is required."""      )
  
  														a_		,				a_       :   Optional[Any]			=	export(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.opset	,	args.output	,	)
  
  														if args.atol is None:
  																					a_       :   Any			=	onnx_config.atol_for_validation
  
  														validate_model_outputs(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	args.output	,	SCREAMING_SNAKE_CASE_	,	args.atol      )
  														logger.info(F"""All good, model saved at: {args.output.as_posix()}"""      )
  														warnings.warn(
  														    """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
  														    """ using optimum.exporters.onnx in future. You can find more information here:"""
  														    """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""	,	SCREAMING_SNAKE_CASE_	,	)
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	ArgumentParser("""Hugging Face Transformers ONNX exporter"""      )
  							parser.add_argument(
  							    """-m"""	,	"""--model"""	,	type=SCREAMING_SNAKE_CASE_	,	required=SCREAMING_SNAKE_CASE_	,	help="""Model ID on huggingface.co or path on disk to load model from."""      )
  							parser.add_argument(
  							    """--feature"""	,	default="""default"""	,	help="""The type of features to export the model with."""	,	)
  							parser.add_argument("""--opset"""	,	type=SCREAMING_SNAKE_CASE_	,	default=SCREAMING_SNAKE_CASE_	,	help="""ONNX opset version to export the model with."""      )
  							parser.add_argument(
  							    """--atol"""	,	type=SCREAMING_SNAKE_CASE_	,	default=SCREAMING_SNAKE_CASE_	,	help="""Absolute difference tolerance when validating the model."""      )
  							parser.add_argument(
  							    """--framework"""	,	type=SCREAMING_SNAKE_CASE_	,	choices=["""pt""", """tf"""]	,	default=SCREAMING_SNAKE_CASE_	,	help=(
  							        """The framework to use for the ONNX export."""
  							        """ If not provided, will attempt to use the local checkpoint's original framework"""
  							        """ or what is available in the environment."""
  							    )	,	)
  							parser.add_argument("""output"""	,	type=SCREAMING_SNAKE_CASE_	,	help="""Path indicating where to store generated ONNX model."""      )
  							parser.add_argument("""--cache_dir"""	,	type=SCREAMING_SNAKE_CASE_	,	default=SCREAMING_SNAKE_CASE_	,	help="""Path indicating where to store cache."""      )
  							parser.add_argument(
  							    """--preprocessor"""	,	type=SCREAMING_SNAKE_CASE_	,	choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]	,	default="""auto"""	,	help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""	,	)
  							parser.add_argument(
  							    """--export_with_transformers"""	,	action="""store_true"""	,	help=(
  							        """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
  							        """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
  							        """recommended."""
  							    )	,	)
  
  							a_       :   Tuple			=	parser.parse_args()
  							if args.export_with_transformers or not is_optimum_available():
  														export_with_transformers(SCREAMING_SNAKE_CASE_      )
  							else:
  														export_with_optimum(SCREAMING_SNAKE_CASE_      )
  
  
  if __name__ == "__main__":
  	SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
  	logger.setLevel(logging.INFO)
  	main()
  
  
  