






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      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[Any]=7       ,				_lowerCAmelCase      :							Any=3       ,				_lowerCAmelCase      :							Tuple=30       ,				_lowerCAmelCase      :							List[Any]=400       ,				_lowerCAmelCase      :							Any=True       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							Union[str, Any]=0.9       ,				_lowerCAmelCase      :							List[Any]=None       ,				_lowerCAmelCase      :							Optional[Any]=True       ,				_lowerCAmelCase      :							Tuple=[0.5, 0.5, 0.5]       ,				_lowerCAmelCase      :							str=[0.5, 0.5, 0.5]       ,				)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  size if size is not None else {"""shortest_edge""": 30}
		__lowercase             =  crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  num_channels
		__lowercase             =  min_resolution
		__lowercase             =  max_resolution
		__lowercase             =  do_resize_and_center_crop
		__lowercase             =  size
		__lowercase             =  crop_pct
		__lowercase             =  crop_size
		__lowercase             =  do_normalize
		__lowercase             =  image_mean
		__lowercase             =  image_std







	def  _a   (  self      :							List[str]			)		-> Union[str, Any]:
		"""simple docstring"""







		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    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Dict   =       PoolFormerImageProcessor if is_vision_available() else None







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase             =  PoolFormerImageProcessingTester(self			)







	@property
	def  _a   (  self      :							List[str]			)		-> Optional[Any]:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  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  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  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}			)

		__lowercase             =  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  _a   (  self      :							int			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							List[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PIL images
		__lowercase             =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				Image.Image			)

		# Test not batched input
		__lowercase             =  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
		__lowercase             =  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  _a   (  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random numpy tensors
		__lowercase             =  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
		__lowercase             =  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
		__lowercase             =  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  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PyTorch tensors
		__lowercase             =  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
		__lowercase             =  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
		__lowercase             =  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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  """"""
	for ch in key:
		if ch == " " or ch not in key_no_dups and ch.isalpha():
			key_no_dups += ch
	return key_no_dups





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [chr(i + 65					) for i in range(26					)]
	# Remove duplicate characters from key
	__lowercase             =  remove_duplicates(key.upper()					)
	__lowercase             =  len(lowerCamelCase					)
	# First fill cipher with key characters
	__lowercase             =  {alphabet[i]: char for i, char in enumerate(lowerCamelCase					)}
	# Then map remaining characters in alphabet to
	# the alphabet from the beginning
	for i in range(len(lowerCamelCase					)				,  26					):
		__lowercase             =  alphabet[i - offset]
		# Ensure we are not mapping letters to letters previously mapped
		while char in key:
			offset -= 1
			__lowercase             =  alphabet[i - offset]
		__lowercase             =  char
	return cipher_alphabet





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return "".join(cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {v: k for k, v in cipher_map.items()}
	return "".join(rev_cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  input("""Enter message to encode or decode: """					).strip()
	__lowercase             =  input("""Enter keyword: """					).strip()
	__lowercase             =  input("""Encipher or decipher? E/D:"""					).strip()[0].lower()
	try:
		__lowercase             =  {"""e""": encipher, """d""": decipher}[option]
	except KeyError:
		raise KeyError("""invalid input option"""					)
	__lowercase             =  create_cipher_map(lowerCamelCase					)
	print(func(lowerCamelCase				,  lowerCamelCase					)					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

