code: 
  
  
  '''simple docstring'''
  
  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
  
  
  __lowercase:		Optional[int]							=     "none"
  __lowercase:		Union[str, Any]							=     "none"
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Optional[int]   )		->					List[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						value.split(_UpperCamelCase   )
     return len(_UpperCamelCase   ) > 1 and any((len(_UpperCamelCase   ) == 3 for x in split)   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int   )		->					Dict:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     if isinstance(_UpperCamelCase    ,		_UpperCamelCase   ):
        return value
     if isinstance(_UpperCamelCase    ,		_UpperCamelCase   ):
        return float(_UpperCamelCase   )
     if not isinstance(_UpperCamelCase    ,		_UpperCamelCase   ):
        raise ValueError("Argument value is not a string. Can't parse it as float"   )
     UpperCamelCase__ 						=						value
  
     try:
        # Example: 1,000.7
        if "." in sanitized and "," in sanitized:
           return float(sanitized.replace(","    ,		""   )   )
        # 1,000
        if "," in sanitized and _split_thousands(","    ,		_UpperCamelCase   ):
           return float(sanitized.replace(","    ,		""   )   )
        # 5,5556
        if "," in sanitized and sanitized.count(","   ) == 1 and not _split_thousands(","    ,		_UpperCamelCase   ):
           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(_UpperCamelCase   )
     except ValueError:
        # Avoid adding the sanitized value in the error message.
        raise ValueError("Unable to convert value to float"   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple   )		->					List[str]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     if answer is None:
        return None
     try:
        UpperCamelCase__ 						=						convert_to_float(_UpperCamelCase   )
        if isinstance(_UpperCamelCase    ,		_UpperCamelCase   ) and math.isnan(_UpperCamelCase   ):
           return None
        return value
     except ValueError:
        return answer.lower()
  
  
  __lowercase:		Any							=     {
      "text": lambda x: x,
      "real": convert_to_float,
  }
  
  
  
  
  
  
  
  class  UpperCAmelCase (       enum.Enum):
   _lowerCamelCase :					Dict    					=					0
   _lowerCamelCase :					Dict    					=					1
   _lowerCamelCase :					Any    					=					2
   _lowerCamelCase :					str    					=					3
   _lowerCamelCase :					str    					=					4
   _lowerCamelCase :					Optional[Any]    					=					5
  
  
  
  
  
  
  
  class  UpperCAmelCase (       enum.Enum):
   _lowerCamelCase :					int    					=					0
   _lowerCamelCase :					Tuple    					=					1
   _lowerCamelCase :					List[str]    					=					2
  
  
  
  
  
  
  
  @dataclasses.dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					Text
   _lowerCamelCase :					_Operator
   _lowerCamelCase :					Any
  
  
  
  
  
  
  
  __lowercase:		List[Any]							=     re.compile(r"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     return list(_TOKENIZER.findall(x.lower()   )   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Tuple   )		->					Dict:
  
  
  
  
  
     '''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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Any   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						table["types"]
     return _TYPE_CONVERTER[types[column]](_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     return isinstance(_UpperCamelCase    ,		_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				str   )		->					Any:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     for cond in conditions:
        UpperCamelCase__ 						=						row[cond.column]
  
        UpperCamelCase__ 						=						_parse_value(_UpperCamelCase    ,		cond.column    ,		cond.cmp_value   )
  
        if _is_string(_UpperCamelCase   ) and _is_string(_UpperCamelCase   ):
           UpperCamelCase__ 						=						_normalize_for_match(_UpperCamelCase   )
           UpperCamelCase__ 						=						_normalize_for_match(_UpperCamelCase   )
  
        if not isinstance(_UpperCamelCase    ,		type(_UpperCamelCase   )   ):
           raise ValueError("Type difference {} != {}".format(type(_UpperCamelCase   )    ,		type(_UpperCamelCase   )   )   )
  
        if not _compare(cond.operator    ,		_UpperCamelCase    ,		_UpperCamelCase   ):
           return False
     return True
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				int   )		->					Tuple:
  
  
  
  
  
     '''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(_UpperCamelCase   )   )
  
     # If we have just one answer, if float returns it or try a conversion.
     UpperCamelCase__ 						=						[table["rows"][i][j] for (i, j) in answer_coordinates]
     if len(_UpperCamelCase   ) == 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(_UpperCamelCase    ,		(int, float)   ) for v in values)   ):
        return None
  
     if aggregation_op == _Aggregation.SUM:
        return float(sum(_UpperCamelCase   )   )
     elif aggregation_op == _Aggregation.AVERAGE:
        return sum(_UpperCamelCase   ) / len(_UpperCamelCase   )
     else:
        raise ValueError(F'Unknown aggregation: {aggregation_op}'   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				int   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						sql_query["agg"]
     if aggregation_op_index >= 3:
        UpperCamelCase__ 						=						_Aggregation(_UpperCamelCase   )
     else:
        UpperCamelCase__ 						=						_Aggregation.NONE
  
     UpperCamelCase__ 						=						sql_query["sel"]
     UpperCamelCase__ 						=						[
         _Condition(_UpperCamelCase    ,		_Operator(_UpperCamelCase   )    ,		_UpperCamelCase   )
         for column, operator, cmp_value in zip(
             sql_query["conds"]["column_index"]    ,		sql_query["conds"]["operator_index"]    ,		sql_query["conds"]["condition"]   )
     ]
  
     UpperCamelCase__ 						=						[]
     for row in range(len(table["rows"]   )   ):
        if _respect_conditions(_UpperCamelCase    ,		table["rows"][row]    ,		_UpperCamelCase   ):
           indices.append((row, target_column)   )
  
     if not indices:
        return [], aggregation_op
  
     if len(_UpperCamelCase   ) == 1:
        return indices, aggregation_op
  
     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
        UpperCamelCase__ 						=						{2: min, 1: max}
        UpperCamelCase__ 						=						[(table["rows"][i][j], index) for index, (i, j) in enumerate(_UpperCamelCase   )]
        UpperCamelCase__ 						=						functools.reduce(operators[sql_query["agg"]]    ,		_UpperCamelCase   )
  
        UpperCamelCase__ 						=						[indices[reduced[1]]]
        return ret, _Aggregation.NONE
  
     return indices, aggregation_op
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Optional[Any]   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     if float_answer is not None:
        return [str(_UpperCamelCase   )]
     return [str(table["real_rows"][r][c]   ) for r, c in answer_coordinates]
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[Any]   )		->					List:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__ 						=						_get_answer_coordinates(_UpperCamelCase    ,		_UpperCamelCase   )
     UpperCamelCase__ 						=						_get_float_answer(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
     UpperCamelCase__ 						=						_get_answer_text(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
     # keep the original data the same with TaPas
     if len(_UpperCamelCase   ) == 0:
        UpperCamelCase__ 						=						[EMPTY_ANSWER]
     return answer_text
style_context: 
  
  
  '''simple docstring'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)