


'''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



'''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"]					)