






"""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_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor






class __a ( unittest.TestCase					):



  def __init__(self	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Union[str, Any]=7					,      lowercase_	:		int=3					,      lowercase_	:		Optional[Any]=18					,      lowercase_	:		int=30					,      lowercase_	:		Optional[int]=400					,      lowercase_	:		Any=True					,      lowercase_	:		List[Any]=None					,      lowercase_	:		Tuple=True					,      lowercase_	:		int=None					,      lowercase_	:		Any=True					,      lowercase_	:		str=[0.5, 0.5, 0.5]					,      lowercase_	:		List[Any]=[0.5, 0.5, 0.5]					,      )		->				List[str]:
   __A						:							Dict      =						size if size is not None else {'height': 20, 'width': 20}
   __A						:							Tuple      =						crop_size if crop_size is not None else {'height': 18, 'width': 18}

   __A						:							List[str]      =						parent
   __A						:							Union[str, Any]      =						batch_size
   __A						:							Dict      =						num_channels
   __A						:							Optional[Any]      =						image_size
   __A						:							int      =						min_resolution
   __A						:							str      =						max_resolution
   __A						:							Optional[int]      =						do_resize
   __A						:							Union[str, Any]      =						size
   __A						:							Any      =						do_center_crop
   __A						:							Tuple      =						crop_size
   __A						:							List[str]      =						do_normalize
   __A						:							str      =						image_mean
   __A						:							Dict      =						image_std



  def     A_					(self	:		Any )		->				Dict:
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }






@require_torch
@require_vision
class __a ( snake_case_			,unittest.TestCase					):
  __lowercase     :     List[Any]       						=       DeiTImageProcessor if is_vision_available() else None
  __lowercase     :     str       						=       True



  def     A_					(self	:		Tuple )		->				Optional[Any]:
   __A						:							Dict      =						DeiTImageProcessingTester(self )



  @property
  def     A_					(self	:		Union[str, Any] )		->				int:
   return self.image_processor_tester.prepare_image_processor_dict()



  def     A_					(self	:		Optional[Any] )		->				int:
   __A						:							Any      =						self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(lowercase_					,      'do_resize' ) )
   self.assertTrue(hasattr(lowercase_					,      'size' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_center_crop' ) )
   self.assertTrue(hasattr(lowercase_					,      'center_crop' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_normalize' ) )
   self.assertTrue(hasattr(lowercase_					,      'image_mean' ) )
   self.assertTrue(hasattr(lowercase_					,      'image_std' ) )



  def     A_					(self	:		Dict )		->				Optional[int]:
   __A						:							List[Any]      =						self.image_processing_class.from_dict(self.image_processor_dict )
   self.assertEqual(image_processor.size					,      {'height': 20, 'width': 20} )
   self.assertEqual(image_processor.crop_size					,      {'height': 18, 'width': 18} )

   __A						:							List[Any]      =						self.image_processing_class.from_dict(self.image_processor_dict					,      size=42					,      crop_size=84 )
   self.assertEqual(image_processor.size					,      {'height': 42, 'width': 42} )
   self.assertEqual(image_processor.crop_size					,      {'height': 84, 'width': 84} )



  def     A_					(self	:		Union[str, Any] )		->				Dict:
   pass



  def     A_					(self	:		Tuple )		->				str:
   # Initialize image_processing
   __A						:							Dict      =						self.image_processing_class(**self.image_processor_dict )
   # create random PIL images
   __A						:							Optional[int]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      Image.Image )

   # Test not batched input
   __A						:							Tuple      =						image_processing(image_inputs[0]					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )

   # Test batched
   __A						:							Optional[Any]      =						image_processing(lowercase_					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )



  def     A_					(self	:		Union[str, Any] )		->				List[Any]:
   # Initialize image_processing
   __A						:							Union[str, Any]      =						self.image_processing_class(**self.image_processor_dict )
   # create random numpy tensors
   __A						:							List[Any]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      np.ndarray )

   # Test not batched input
   __A						:							Optional[Any]      =						image_processing(image_inputs[0]					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )

   # Test batched
   __A						:							Tuple      =						image_processing(lowercase_					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )



  def     A_					(self	:		Tuple )		->				Tuple:
   # Initialize image_processing
   __A						:							Union[str, Any]      =						self.image_processing_class(**self.image_processor_dict )
   # create random PyTorch tensors
   __A						:							Optional[int]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      torch.Tensor )

   # Test not batched input
   __A						:							Tuple      =						image_processing(image_inputs[0]					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )

   # Test batched
   __A						:							str      =						image_processing(lowercase_					,      return_tensors='pt' ).pixel_values
   self.assertEqual(
       encoded_images.shape					,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['height'],
           self.image_processor_tester.crop_size['width'],
       )					,      )













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int		)	->		int:
 return len_board * len_board_column * row + column




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					set[int]  ,						)	->		bool:

 if board[row][column] != word[word_index]:
  return False

 if word_index == len(UpperCamelCase_		) - 1:
  return True

 __A						:							List[str]      =						[(0, 1), (0, -1), (-1, 0), (1, 0)]
 __A						:							Any      =						len(UpperCamelCase_		)
 __A						:							Union[str, Any]      =						len(board[0]		)
 for direction in traverts_directions:
  __A						:							Any      =						row + direction[0]
  __A						:							List[Any]      =						column + direction[1]
  if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
   continue

  __A						:							Any      =						get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)
  if key in visited_points_set:
   continue

  visited_points_set.add(UpperCamelCase_		)
  if exits_word(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						word_index + 1  ,						UpperCamelCase_		):
   return True

  visited_points_set.remove(UpperCamelCase_		)

 return False




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str		)	->		bool:
 __A						:							Optional[Any]      =						(
     'The board should be a non empty matrix of single chars strings.'
 )

 __A						:							Dict      =						len(UpperCamelCase_		)
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(UpperCamelCase_		)

 for row in board:
  if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
   raise ValueError(UpperCamelCase_		)

  for item in row:
   if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) != 1:
    raise ValueError(UpperCamelCase_		)

    # Validate word
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(
      'The word parameter should be a string of length greater than 0.'		)

 __A						:							List[Any]      =						len(board[0]		)
 for i in range(UpperCamelCase_		):
  for j in range(UpperCamelCase_		):
   if exits_word(
       UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						0  ,						{get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)}		):
    return True

 return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()





