

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 ViTImageProcessor







class        a__       (   unittest.TestCase       ):







		"""simple docstring"""




		def __init__(			self    : List[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : str=1_3				,				UpperCAmelCase__    : Dict=3				,				UpperCAmelCase__    : Optional[int]=2_2_4				,				UpperCAmelCase__    : Optional[int]=3_0				,				UpperCAmelCase__    : int=4_0_0				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : int=None				,				UpperCAmelCase__    : List[str]=True				,				UpperCAmelCase__    : str=[0.5, 0.5, 0.5]				,				UpperCAmelCase__    : Any=[0.5, 0.5, 0.5]				,				)					->Any:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   size if size is not None else {"""height""": 1_8, """width""": 1_8}
								SCREAMING_SNAKE_CASE :      Any       =   parent
								SCREAMING_SNAKE_CASE :      int       =   batch_size
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_channels
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_size
								SCREAMING_SNAKE_CASE :      Any       =   min_resolution
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   max_resolution
								SCREAMING_SNAKE_CASE :      Dict       =   do_resize
								SCREAMING_SNAKE_CASE :      Any       =   size
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   do_normalize
								SCREAMING_SNAKE_CASE :      Any       =   image_mean
								SCREAMING_SNAKE_CASE :      Any       =   image_std




		def  _lowercase      (			self    : int		)					->Optional[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,
								}







@require_torch
@require_vision
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







		"""simple docstring"""




		UpperCAmelCase__      : int			       =ViTImageProcessor if is_vision_available() else None


		def  _lowercase      (			self    : int		)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[Any]       =   EfficientFormerImageProcessorTester(self		)


		@property
		def  _lowercase      (			self    : Optional[int]		)					->Tuple:
								"""simple docstring"""







								return self.image_proc_tester.prepare_image_processor_dict()


		def  _lowercase      (			self    : str		)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[str]       =   self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(__UpperCamelCase				,				"""image_mean"""		)		)
								self.assertTrue(hasattr(__UpperCamelCase				,				"""image_std"""		)		)
								self.assertTrue(hasattr(__UpperCamelCase				,				"""do_normalize"""		)		)
								self.assertTrue(hasattr(__UpperCamelCase				,				"""do_resize"""		)		)
								self.assertTrue(hasattr(__UpperCamelCase				,				"""size"""		)		)


		def  _lowercase      (			self    : Optional[int]		)					->Dict:
								"""simple docstring"""







								pass


		def  _lowercase      (			self    : Dict		)					->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   prepare_image_inputs(self.image_proc_tester				,				equal_resolution=__UpperCamelCase		)
								for image in image_inputs:
														self.assertIsInstance(__UpperCamelCase				,				Image.Image		)

								# Test not batched input
								SCREAMING_SNAKE_CASE :      List[str]       =   image_processor(image_inputs[0]				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)

								# Test batched
								SCREAMING_SNAKE_CASE :      Tuple       =   image_processor(__UpperCamelCase				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)


		def  _lowercase      (			self    : List[str]		)					->Tuple:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								SCREAMING_SNAKE_CASE :      str       =   prepare_image_inputs(self.image_proc_tester				,				equal_resolution=__UpperCamelCase				,				numpify=__UpperCamelCase		)
								for image in image_inputs:
														self.assertIsInstance(__UpperCamelCase				,				np.ndarray		)

								# Test not batched input
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   image_processor(image_inputs[0]				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)

								# Test batched
								SCREAMING_SNAKE_CASE :      Optional[int]       =   image_processor(__UpperCamelCase				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)




		def  _lowercase      (			self    : Tuple		)					->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Tuple       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE :      str       =   prepare_image_inputs(self.image_proc_tester				,				equal_resolution=__UpperCamelCase				,				torchify=__UpperCamelCase		)
								for image in image_inputs:
														self.assertIsInstance(__UpperCamelCase				,				torch.Tensor		)

								# Test not batched input
								SCREAMING_SNAKE_CASE :      List[Any]       =   image_processor(image_inputs[0]				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)

								# Test batched
								SCREAMING_SNAKE_CASE :      int       =   image_processor(__UpperCamelCase				,				return_tensors="""pt"""		).pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["""height"""],
								        self.image_proc_tester.size["""width"""],
								    )				,				)







def 					__lowercase      (     _A						,    _A			)     ->						int:
      assert x is not None
      assert y is not None

      SCREAMING_SNAKE_CASE :      int       =   len(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   len(_A			)

      # declaring the array for storing the dp values
      SCREAMING_SNAKE_CASE :      Tuple       =   [[0] * (n + 1) for _ in range(m + 1			)]  # noqa: E741

      for i in range(1						,    m + 1			):
            for j in range(1						,    n + 1			):
                  SCREAMING_SNAKE_CASE :      List[Any]       =   1 if x[i - 1] == y[j - 1] else 0

                  SCREAMING_SNAKE_CASE :      int       =   max(l[i - 1][j]						,    l[i][j - 1]						,    l[i - 1][j - 1] + match			)

      SCREAMING_SNAKE_CASE :      int       =   """"""
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   m, n
      while i > 0 and j > 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   1 if x[i - 1] == y[j - 1] else 0

            if l[i][j] == l[i - 1][j - 1] + match:
                  if match == 1:
                        SCREAMING_SNAKE_CASE :      List[Any]       =   x[i - 1] + seq
                  i -= 1
                  j -= 1
            elif l[i][j] == l[i - 1][j]:
                  i -= 1
            else:
                  j -= 1

      return l[m][n], seq


if __name__ == "__main__":
  UpperCAmelCase__ :     str				    =    """AGGTAB"""
  UpperCAmelCase__ :     Optional[int]				    =    """GXTXAYB"""
  UpperCAmelCase__ :     Tuple				    =    4
  UpperCAmelCase__ :     Dict				    =    """GTAB"""

  UpperCAmelCase__    ,   UpperCAmelCase__ :     Optional[int]				    =    longest_common_subsequence(a, b)
  print("""len =""", ln, """, sub-sequence =""", subseq)
  import doctest

  doctest.testmod()




