
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 PoolFormerImageProcessor





class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					Dict				,A   :					List[Any]=7				,A   :					Any=3				,A   :					List[Any]=30				,A   :					List[Any]=4_00				,A   :					Union[str, Any]=True				,A   :					List[str]=None				,A   :					Optional[Any]=0.9				,A   :					Any=None				,A   :					List[Any]=True				,A   :					List[Any]=[0.5, 0.5, 0.5]				,A   :					Union[str, Any]=[0.5, 0.5, 0.5]				,):
					__A       =					size if size is not None else {"shortest_edge": 30}
					__A       =					crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					min_resolution
					__A       =					max_resolution
					__A       =					do_resize_and_center_crop
					__A       =					size
					__A       =					crop_pct
					__A       =					crop_size
					__A       =					do_normalize
					__A       =					image_mean
					__A       =					image_std







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "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 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	PoolFormerImageProcessor if is_vision_available() else None






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					PoolFormerImageProcessingTester(self					)






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"do_resize_and_center_crop"					)					)
					self.assertTrue(hasattr(A				,"size"					)					)
					self.assertTrue(hasattr(A				,"crop_pct"					)					)
					self.assertTrue(hasattr(A				,"do_normalize"					)					)
					self.assertTrue(hasattr(A				,"image_mean"					)					)
					self.assertTrue(hasattr(A				,"image_std"					)					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.image_processing_class.from_dict(self.image_processor_dict					)
					self.assertEqual(image_processor.size				,{"shortest_edge": 30}					)
					self.assertEqual(image_processor.crop_size				,{"height": 30, "width": 30}					)

					__A       =					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[Any]					):
					pass






	def 							UpperCamelCase_    (			self   :					str					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,Image.Image					)

					# Test not batched input
					__A       =					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       =					image_processing(A				,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 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A				,numpify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,np.ndarray					)

					# Test not batched input
					__A       =					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       =					image_processing(A				,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 							UpperCamelCase_    (			self   :					Any					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A				,torchify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,torch.Tensor					)

					# Test not batched input
					__A       =					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       =					image_processing(A				,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 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

