
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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import SwinaSRImageProcessor
	from transformers.image_transforms import get_image_size







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=7		,   snake_case=3		,   snake_case=18		,   snake_case=30		,   snake_case=400		,   snake_case=True		,   snake_case=1 / 255		,   snake_case=True		,   snake_case=8		,   ):
		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   num_channels
		lowercase					  =   image_size
		lowercase					  =   min_resolution
		lowercase					  =   max_resolution
		lowercase					  =   do_rescale
		lowercase					  =   rescale_factor
		lowercase					  =   do_pad
		lowercase					  =   pad_size





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return {
		    "do_rescale": self.do_rescale,
		    "rescale_factor": self.rescale_factor,
		    "do_pad": self.do_pad,
		    "pad_size": self.pad_size,
		}





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case=False		,   snake_case=False		,   snake_case=False	):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		if equal_resolution:
			lowercase					  =   []
			for i in range(self.batch_size	):
				image_inputs.append(
				    np.random.randint(
				        255		,   size=(self.num_channels, self.max_resolution, self.max_resolution)		,   dtype=np.uinta	)	)
		else:
			lowercase					  =   []
			for i in range(self.batch_size	):
				lowercase  , lowercase					  =   np.random.choice(np.arange(self.min_resolution		,   self.max_resolution	)		,   2	)
				image_inputs.append(np.random.randint(255		,   size=(self.num_channels, width, height)		,   dtype=np.uinta	)	)

		if not numpify and not torchify:
			# PIL expects the channel dimension as last dimension
			lowercase					  =   [Image.fromarray(np.moveaxis(snake_case		,   0		,   -1	)	) for x in image_inputs]

		if torchify:
			lowercase					  =   [torch.from_numpy(snake_case	) for x in image_inputs]

		return image_inputs







@require_torch
@require_vision
class 	A_       (      __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[Any]										=	SwinaSRImageProcessor if is_vision_available() else None





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   SwinaSRImageProcessingTester(self	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.image_processor_tester.prepare_image_processor_dict()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(snake_case		,   'do_rescale'	)	)
		self.assertTrue(hasattr(snake_case		,   'rescale_factor'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_pad'	)	)
		self.assertTrue(hasattr(snake_case		,   'pad_size'	)	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase  , lowercase					  =   get_image_size(snake_case	)
		lowercase					  =   self.image_processor_tester.pad_size

		lowercase					  =   (old_height // size + 1) * size - old_height
		lowercase					  =   (old_width // size + 1) * size - old_width
		return old_height + pad_height, old_width + pad_width





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processor
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		lowercase					  =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case	)
		for image in image_inputs:
			self.assertIsInstance(snake_case		,   Image.Image	)

		# Test not batched input
		lowercase					  =   image_processor(image_inputs[0]		,   return_tensors='pt'	).pixel_values
		lowercase  , lowercase					  =   self.calculate_expected_size(np.array(image_inputs[0]	)	)
		self.assertEqual(
		    encoded_images.shape		,   (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processor
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		lowercase					  =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case		,   numpify=snake_case	)
		for image in image_inputs:
			self.assertIsInstance(snake_case		,   np.ndarray	)

		# Test not batched input
		lowercase					  =   image_processor(image_inputs[0]		,   return_tensors='pt'	).pixel_values
		lowercase  , lowercase					  =   self.calculate_expected_size(image_inputs[0]	)
		self.assertEqual(
		    encoded_images.shape		,   (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processor
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		lowercase					  =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case		,   torchify=snake_case	)
		for image in image_inputs:
			self.assertIsInstance(snake_case		,   torch.Tensor	)

		# Test not batched input
		lowercase					  =   image_processor(image_inputs[0]		,   return_tensors='pt'	).pixel_values
		lowercase  , lowercase					  =   self.calculate_expected_size(image_inputs[0]	)
		self.assertEqual(
		    encoded_images.shape		,   (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )		,   )



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

