
'''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


SCREAMING_SNAKE_CASE    =					"""none"""
SCREAMING_SNAKE_CASE    =					"""none"""







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				int												=       value.split(lowercase__		)
  return len(lowercase__		) > 1 and any((len(lowercase__		) == 3 for x in split)		)







def snake_case_    (				lowercase__		):
  if isinstance(lowercase__      ,					lowercase__		):
    return value
  if isinstance(lowercase__      ,					lowercase__		):
    return float(lowercase__		)
  if not isinstance(lowercase__      ,					lowercase__		):
    raise ValueError("Argument value is not a string. Can't parse it as float"		)
  UpperCAmelCase__			:				Any												=       value

  try:
    # Example: 1,000.7
    if "." in sanitized and "," in sanitized:
      return float(sanitized.replace(","      ,					""		)		)
    # 1,000
    if "," in sanitized and _split_thousands(","      ,					lowercase__		):
      return float(sanitized.replace(","      ,					""		)		)
    # 5,5556
    if "," in sanitized and sanitized.count(","		) == 1 and not _split_thousands(","      ,					lowercase__		):
      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(lowercase__		)
  except ValueError:
    # Avoid adding the sanitized value in the error message.
    raise ValueError("Unable to convert value to float"		)







def snake_case_    (				lowercase__		):
  if answer is None:
    return None
  try:
    UpperCAmelCase__			:				Dict												=       convert_to_float(lowercase__		)
    if isinstance(lowercase__      ,					lowercase__		) and math.isnan(lowercase__		):
      return None
    return value
  except ValueError:
    return answer.lower()


SCREAMING_SNAKE_CASE    =					{
    """text""": lambda x: x,
    """real""": convert_to_float,
}






class        UpperCAmelCase_       (						enum.Enum							):

 '''simple docstring'''
 lowercase_				:						Optional[Any]				=       0
 lowercase_				:						Optional[int]				=       1
 lowercase_				:						str				=       2
 lowercase_				:						List[Any]				=       3
 lowercase_				:						Dict				=       4
 lowercase_				:						List[str]				=       5






class        UpperCAmelCase_       (						enum.Enum							):

 '''simple docstring'''
 lowercase_				:						Optional[Any]				=       0
 lowercase_				:						int				=       1
 lowercase_				:						List[str]				=       2






@dataclasses.dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 lowercase_				:						Text
 lowercase_				:						_Operator
 lowercase_				:						Any



SCREAMING_SNAKE_CASE    =					re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)







def snake_case_    (				lowercase__		):
  return list(_TOKENIZER.findall(x.lower()		)		)







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  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 snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				List[str]												=       table["types"]
  return _TYPE_CONVERTER[types[column]](lowercase__		)







def snake_case_    (				lowercase__		):
  return isinstance(lowercase__      ,					lowercase__		)







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  for cond in conditions:
    UpperCAmelCase__			:				Optional[int]												=       row[cond.column]

    UpperCAmelCase__			:				Any												=       _parse_value(lowercase__      ,					cond.column      ,					cond.cmp_value		)

    if _is_string(lowercase__		) and _is_string(lowercase__		):
      UpperCAmelCase__			:				int												=       _normalize_for_match(lowercase__		)
      UpperCAmelCase__			:				int												=       _normalize_for_match(lowercase__		)

    if not isinstance(lowercase__      ,					type(lowercase__		)		):
      raise ValueError("Type difference {} != {}".format(type(lowercase__		)      ,					type(lowercase__		)		)		)

    if not _compare(cond.operator      ,					lowercase__      ,					lowercase__		):
      return False
  return True







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  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(lowercase__		)		)

  # If we have just one answer, if float returns it or try a conversion.
  UpperCAmelCase__			:				List[str]												=       [table["rows"][i][j] for (i, j) in answer_coordinates]
  if len(lowercase__		) == 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(lowercase__      ,					(int, float)		) for v in values)		):
    return None

  if aggregation_op == _Aggregation.SUM:
    return float(sum(lowercase__		)		)
  elif aggregation_op == _Aggregation.AVERAGE:
    return sum(lowercase__		) / len(lowercase__		)
  else:
    raise ValueError(F"""Unknown aggregation: {aggregation_op}"""		)







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Union[str, Any]												=       sql_query["agg"]
  if aggregation_op_index >= 3:
    UpperCAmelCase__			:				Optional[int]												=       _Aggregation(lowercase__		)
  else:
    UpperCAmelCase__			:				List[Any]												=       _Aggregation.NONE

  UpperCAmelCase__			:				Dict												=       sql_query["sel"]
  UpperCAmelCase__			:				Dict												=       [
      _Condition(lowercase__      ,					_Operator(lowercase__		)      ,					lowercase__		)
      for column, operator, cmp_value in zip(
          sql_query["conds"]["column_index"]      ,					sql_query["conds"]["operator_index"]      ,					sql_query["conds"]["condition"]		)
  ]

  UpperCAmelCase__			:				Optional[Any]												=       []
  for row in range(len(table["rows"]		)		):
    if _respect_conditions(lowercase__      ,					table["rows"][row]      ,					lowercase__		):
      indices.append((row, target_column)		)

  if not indices:
    return [], aggregation_op

  if len(lowercase__		) == 1:
    return indices, aggregation_op

  # Parsing of MIN/MAX.
  if aggregation_op_index in (1, 2):
    UpperCAmelCase__			:				List[Any]												=       {2: min, 1: max}
    UpperCAmelCase__			:				List[str]												=       [(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase__		)]
    UpperCAmelCase__			:				Optional[int]												=       functools.reduce(operators[sql_query["agg"]]      ,					lowercase__		)

    UpperCAmelCase__			:				List[Any]												=       [indices[reduced[1]]]
    return ret, _Aggregation.NONE

  return indices, aggregation_op







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  if float_answer is not None:
    return [str(lowercase__		)]
  return [str(table["real_rows"][r][c]		) for r, c in answer_coordinates]







def snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Optional[Any]												=       _get_answer_coordinates(lowercase__      ,					lowercase__		)
  UpperCAmelCase__			:				int												=       _get_float_answer(lowercase__      ,					lowercase__      ,					lowercase__		)
  UpperCAmelCase__			:				Optional[Any]												=       _get_answer_text(lowercase__      ,					lowercase__      ,					lowercase__		)
  # keep the original data the same with TaPas
  if len(lowercase__		) == 0:
    UpperCAmelCase__			:				Any												=       [EMPTY_ANSWER]
  return answer_text





'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import VideoMAEImageProcessor






class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def __init__(				self		:     str ,							snake_case__		:     Optional[int] ,							snake_case__		:     Union[str, Any]=7 ,							snake_case__		:     Any=3 ,							snake_case__		:     Tuple=10 ,							snake_case__		:     Dict=18 ,							snake_case__		:     Dict=30 ,							snake_case__		:     Tuple=4_00 ,							snake_case__		:     str=True ,							snake_case__		:     Optional[Any]=None ,							snake_case__		:     Union[str, Any]=True ,							snake_case__		:     List[Any]=[0.5, 0.5, 0.5] ,							snake_case__		:     Dict=[0.5, 0.5, 0.5] ,							snake_case__		:     Tuple=None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       size if size is not None else {"shortest_edge": 18}
   UpperCAmelCase__			:				Optional[int]												=       crop_size if crop_size is not None else {"height": 18, "width": 18}

   UpperCAmelCase__			:				List[Any]												=       parent
   UpperCAmelCase__			:				Optional[Any]												=       batch_size
   UpperCAmelCase__			:				int												=       num_channels
   UpperCAmelCase__			:				str												=       num_frames
   UpperCAmelCase__			:				List[Any]												=       image_size
   UpperCAmelCase__			:				Optional[int]												=       min_resolution
   UpperCAmelCase__			:				int												=       max_resolution
   UpperCAmelCase__			:				Optional[int]												=       do_resize
   UpperCAmelCase__			:				List[str]												=       size
   UpperCAmelCase__			:				Optional[int]												=       do_normalize
   UpperCAmelCase__			:				int												=       image_mean
   UpperCAmelCase__			:				Any												=       image_std
   UpperCAmelCase__			:				Optional[int]												=       crop_size







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
       "crop_size": self.crop_size,
   }






@require_torch
@require_vision
class        UpperCAmelCase_       (						A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						Any				=       VideoMAEImageProcessor if is_vision_available() else None







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       VideoMAEImageProcessingTester(self   )







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       self.image_processing_class(**self.image_processor_dict   )
   self.assertTrue(hasattr(snake_case__ ,							"image_mean"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"image_std"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_normalize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_resize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_center_crop"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"size"   )   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.image_processing_class.from_dict(self.image_processor_dict   )
   self.assertEqual(image_processor.size ,							{"shortest_edge": 18}   )
   self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}   )

   UpperCAmelCase__			:				Union[str, Any]												=       self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84   )
   self.assertEqual(image_processor.size ,							{"shortest_edge": 42}   )
   self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}   )







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       self.image_processing_class(**self.image_processor_dict   )
   # create random PIL videos
   UpperCAmelCase__			:				Optional[int]												=       prepare_video_inputs(self.image_processor_tester ,							equal_resolution=snake_case__   )
   for video in video_inputs:
     self.assertIsInstance(snake_case__ ,							snake_case__   )
     self.assertIsInstance(video[0] ,							Image.Image   )

   # Test not batched input
   UpperCAmelCase__			:				Dict												=       image_processing(video_inputs[0] ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           1,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)

   # Test batched
   UpperCAmelCase__			:				Tuple												=       image_processing(snake_case__ ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       self.image_processing_class(**self.image_processor_dict   )
   # create random numpy tensors
   UpperCAmelCase__			:				List[Any]												=       prepare_video_inputs(self.image_processor_tester ,							equal_resolution=snake_case__ ,							numpify=snake_case__   )
   for video in video_inputs:
     self.assertIsInstance(snake_case__ ,							snake_case__   )
     self.assertIsInstance(video[0] ,							np.ndarray   )

   # Test not batched input
   UpperCAmelCase__			:				Dict												=       image_processing(video_inputs[0] ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           1,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)

   # Test batched
   UpperCAmelCase__			:				Optional[int]												=       image_processing(snake_case__ ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.image_processing_class(**self.image_processor_dict   )
   # create random PyTorch tensors
   UpperCAmelCase__			:				Dict												=       prepare_video_inputs(self.image_processor_tester ,							equal_resolution=snake_case__ ,							torchify=snake_case__   )
   for video in video_inputs:
     self.assertIsInstance(snake_case__ ,							snake_case__   )
     self.assertIsInstance(video[0] ,							torch.Tensor   )

   # Test not batched input
   UpperCAmelCase__			:				int												=       image_processing(video_inputs[0] ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           1,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)

   # Test batched
   UpperCAmelCase__			:				Optional[Any]												=       image_processing(snake_case__ ,							return_tensors="pt"   ).pixel_values
   self.assertEqual(
       encoded_videos.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_frames,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       ) ,							)



