
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_video_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import VivitImageProcessor





class 			_lowerCAmelCase      (			unittest.TestCase ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=7       ,       _UpperCamelCase=3       ,       _UpperCamelCase=10       ,       _UpperCamelCase=18       ,       _UpperCamelCase=30       ,       _UpperCamelCase=400       ,       _UpperCamelCase=True       ,       _UpperCamelCase=None       ,       _UpperCamelCase=True       ,       _UpperCamelCase=[0.5, 0.5, 0.5]       ,       _UpperCamelCase=[0.5, 0.5, 0.5]       ,       _UpperCamelCase=None       ,       )       ->   str:
							lowerCAmelCase_      			=			size if size is not None else {"shortest_edge": 18}
							lowerCAmelCase_      			=			crop_size if crop_size is not None else {"height": 18, "width": 18}

							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			batch_size
							lowerCAmelCase_      			=			num_channels
							lowerCAmelCase_      			=			num_frames
							lowerCAmelCase_      			=			image_size
							lowerCAmelCase_      			=			min_resolution
							lowerCAmelCase_      			=			max_resolution
							lowerCAmelCase_      			=			do_resize
							lowerCAmelCase_      			=			size
							lowerCAmelCase_      			=			do_normalize
							lowerCAmelCase_      			=			image_mean
							lowerCAmelCase_      			=			image_std
							lowerCAmelCase_      			=			crop_size




				def   __a		(      self  )       ->   Optional[Any]:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}





@require_torch
@require_vision
class 			_lowerCAmelCase      (			__a      ,   unittest.TestCase ):
				_lowercase    	=VivitImageProcessor if is_vision_available() else None




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			VivitImageProcessingTester(self  )




				@property
				def   __a		(      self  )       ->   Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			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       ,       "do_center_crop"  )  )
							self.assertTrue(hasattr(_UpperCamelCase       ,       "size"  )  )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			self.image_processing_class.from_dict(self.image_processor_dict  )
							self.assertEqual(image_processor.size       ,       {"shortest_edge": 18}  )
							self.assertEqual(image_processor.crop_size       ,       {"height": 18, "width": 18}  )

							lowerCAmelCase_      			=			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  )       ->   Optional[int]:
							# Initialize image_processing
							lowerCAmelCase_      			=			self.image_processing_class(**self.image_processor_dict  )
							# create random PIL videos
							lowerCAmelCase_      			=			prepare_video_inputs(self.image_processor_tester       ,       equal_resolution=_UpperCamelCase  )
							for video in video_inputs:
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
										self.assertIsInstance(video[0]       ,       Image.Image  )

							# Test not batched input
							lowerCAmelCase_      			=			image_processing(video_inputs[0]       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )

							# Test batched
							lowerCAmelCase_      			=			image_processing(_UpperCamelCase       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )




				def   __a		(      self  )       ->   int:
							# Initialize image_processing
							lowerCAmelCase_      			=			self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase_      			=			prepare_video_inputs(self.image_processor_tester       ,       equal_resolution=_UpperCamelCase       ,       numpify=_UpperCamelCase  )
							for video in video_inputs:
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
										self.assertIsInstance(video[0]       ,       np.ndarray  )

							# Test not batched input
							lowerCAmelCase_      			=			image_processing(video_inputs[0]       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )

							# Test batched
							lowerCAmelCase_      			=			image_processing(_UpperCamelCase       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )




				def   __a		(      self  )       ->   List[str]:
							# Initialize image_processing
							lowerCAmelCase_      			=			self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase_      			=			prepare_video_inputs(self.image_processor_tester       ,       equal_resolution=_UpperCamelCase       ,       torchify=_UpperCamelCase  )
							for video in video_inputs:
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
										self.assertIsInstance(video[0]       ,       torch.Tensor  )

							# Test not batched input
							lowerCAmelCase_      			=			image_processing(video_inputs[0]       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )

							# Test batched
							lowerCAmelCase_      			=			image_processing(_UpperCamelCase       ,       return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,       (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )       ,       )





import itertools
import string
from collections.abc import Generator, Iterable






def      lowerCamelCase__       (       __lowerCAmelCase						:						Iterable[str]					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			iter(__lowerCAmelCase       )
			while True:
						lowerCAmelCase_      			=			tuple(itertools.islice(__lowerCAmelCase					,							__lowerCAmelCase       )       )
						if not chunk:
									return
						yield chunk






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"".join([c.upper() for c in dirty if c in string.ascii_letters]       )
			lowerCAmelCase_      			=			""

			if len(__lowerCAmelCase       ) < 2:
						return dirty

			for i in range(len(__lowerCAmelCase       ) - 1       ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__lowerCAmelCase       ) & 1:
						clean += "X"

			return clean






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"ABCDEFGHIKLMNOPQRSTUVWXYZ"
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCAmelCase_      			=			[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__lowerCAmelCase       )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__lowerCAmelCase       )

			return table






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			prepare_input(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



