






"""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 PoolFormerImageProcessor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]=7      ,      lowerCAmelCase_   :					str=3      ,      lowerCAmelCase_   :					Tuple=30      ,      lowerCAmelCase_   :					str=400      ,      lowerCAmelCase_   :					Dict=True      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					List[str]=0.9      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					Dict=[0.5, 0.5, 0.5]      ,      lowerCAmelCase_   :					str=[0.5, 0.5, 0.5]      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   size if size is not None else {"shortest_edge": 30}
				UpperCAmelCase_	:					Dict     	=   crop_size if crop_size is not None else {"height": 30, "width": 30}
				UpperCAmelCase_	:					int     	=   parent
				UpperCAmelCase_	:					Dict     	=   batch_size
				UpperCAmelCase_	:					List[Any]     	=   num_channels
				UpperCAmelCase_	:					Union[str, Any]     	=   min_resolution
				UpperCAmelCase_	:					List[str]     	=   max_resolution
				UpperCAmelCase_	:					Any     	=   do_resize_and_center_crop
				UpperCAmelCase_	:					str     	=   size
				UpperCAmelCase_	:					int     	=   crop_pct
				UpperCAmelCase_	:					List[str]     	=   crop_size
				UpperCAmelCase_	:					List[str]     	=   do_normalize
				UpperCAmelCase_	:					Dict     	=   image_mean
				UpperCAmelCase_	:					List[Any]     	=   image_std





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				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_			(__A							,		unittest.TestCase	):
	__magic_name__       =							PoolFormerImageProcessor if is_vision_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Tuple     	=   PoolFormerImageProcessingTester(self		)





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_resize_and_center_crop"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "size"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "crop_pct"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_normalize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_mean"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_std"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   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}		)

				UpperCAmelCase_	:					Optional[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 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Any:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				UpperCAmelCase_	:					int     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      Image.Image		)

				# Test not batched input
				UpperCAmelCase_	:					Dict     	=   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
				UpperCAmelCase_	:					Union[str, Any]     	=   image_processing(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				# Initialize image_processing
				UpperCAmelCase_	:					List[Any]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase_	:					str     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_      ,      numpify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      np.ndarray		)

				# Test not batched input
				UpperCAmelCase_	:					Union[str, 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
				UpperCAmelCase_	:					Union[str, Any]     	=   image_processing(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Union[str, Any]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase_	:					Tuple     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_      ,      torchify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)

				# Test not batched input
				UpperCAmelCase_	:					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
				UpperCAmelCase_	:					List[Any]     	=   image_processing(lowerCAmelCase_      ,      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"],
				    )      ,      )







from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bool:


							'''simple docstring'''





							_UpperCAmelCase											=     get_failure_array(a__			)

							# 2) Step through text searching for pattern
							_UpperCAmelCase						,			_UpperCAmelCase											=     0, 0  # index into text, pattern
							while i < len(a__			):
														if pattern[j] == text[i]:
																					if j == (len(a__			) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					_UpperCAmelCase											=     failure[j - 1]
																					continue
														i += 1
							return False

def 			lowerCAmelCase__					(     a__:						str			)    ->						list[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     [0]
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1
							while j < len(a__			):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					_UpperCAmelCase											=     failure[i - 1]
																					continue
														j += 1
														failure.append(a__			)
							return failure


if __name__ == "__main__":
		# Test 1)
		lowerCAmelCase__    :List[Any]       =				'''abc1abc12'''
		lowerCAmelCase__    :Optional[int]       =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
		lowerCAmelCase__    :int       =				'''alskfjaldsk23adsfabcabc'''
		assert kmp(pattern, texta) and not kmp(pattern, texta)

		# Test 2)
		lowerCAmelCase__    :List[str]       =				'''ABABX'''
		lowerCAmelCase__    :int       =				'''ABABZABABYABABX'''
		assert kmp(pattern, text)

		# Test 3)
		lowerCAmelCase__    :int       =				'''AAAB'''
		lowerCAmelCase__    :Any       =				'''ABAAAAAB'''
		assert kmp(pattern, text)

		# Test 4)
		lowerCAmelCase__    :Union[str, Any]       =				'''abcdabcy'''
		lowerCAmelCase__    :Any       =				'''abcxabcdabxabcdabcdabcy'''
		assert kmp(pattern, text)

		# Test 5)
		lowerCAmelCase__    :List[Any]       =				'''aabaabaaa'''
		assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
