"""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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import TvltImageProcessor

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]=10		,			_SCREAMING_SNAKE_CASE	:Optional[Any]=10		,			_SCREAMING_SNAKE_CASE	:str=False		,			_SCREAMING_SNAKE_CASE	:Any=False  )				-> Optional[Any]:
						a_							:			Union[str, Any]     		=					[]
						for i in range(image_processor_tester.num_frames  ):
												video.append(np.random.randint(255		,			size=(image_processor_tester.num_channels, width, height)		,			dtype=np.uinta  )  )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												a_							:			List[str]     		=					[Image.fromarray(np.moveaxis(lowercase__		,			0		,			-1  )  ) for frame in video]

						if torchify:
												a_							:			Optional[int]     		=					[torch.from_numpy(lowercase__  ) for frame in video]

						return video

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:Tuple=False		,			_SCREAMING_SNAKE_CASE	:Dict=False		,			_SCREAMING_SNAKE_CASE	:str=False  )				-> Dict:
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						a_							:			List[Any]     		=					[]
						for i in range(image_processor_tester.batch_size  ):
												if equal_resolution:
																		a_							:			Optional[int]     		=					image_processor_tester.max_resolution
												else:
																		a_							:			str     		=					np.random.choice(
																		    np.arange(image_processor_tester.min_resolution		,			image_processor_tester.max_resolution  )		,			2  )
																		a_							:			List[str]     		=					prepare_video(
																		    image_processor_tester=lowercase__		,			width=lowercase__		,			height=lowercase__		,			numpify=lowercase__		,			torchify=lowercase__		,			)
												video_inputs.append(lowercase__  )

						return video_inputs
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=7       ,     _SCREAMING_SNAKE_CASE=3       ,     _SCREAMING_SNAKE_CASE=4       ,     _SCREAMING_SNAKE_CASE=1_8       ,     _SCREAMING_SNAKE_CASE=3_0       ,     _SCREAMING_SNAKE_CASE=4_0_0       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]       ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=None       ,     )    ->	List[str]:
							a_							:			List[Any]     		=					size if size is not None else {"shortest_edge": 1_8}
							a_							:			Optional[int]     		=					crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

							a_							:			Tuple     		=					parent
							a_							:			List[str]     		=					batch_size
							a_							:			Any     		=					num_channels
							a_							:			Dict     		=					num_frames
							a_							:			int     		=					image_size
							a_							:			Optional[Any]     		=					min_resolution
							a_							:			Tuple     		=					max_resolution
							a_							:			Any     		=					do_resize
							a_							:			Any     		=					size
							a_							:			Optional[int]     		=					do_normalize
							a_							:			Union[str, Any]     		=					image_mean
							a_							:			str     		=					image_std
							a_							:			List[str]     		=					do_center_crop
							a_							:			Union[str, Any]     		=					crop_size







	def       A    (			self )    ->	Tuple:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}




@require_torch
@require_vision
class   UpperCAmelCase__				(    lowerCamelCase_,      unittest.TestCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[str]						      =							TvltImageProcessor if is_vision_available() else None







	def       A    (			self )    ->	List[str]:
							a_							:			int     		=					TvltImageProcessorTester(self )







	@property
	def       A    (			self )    ->	List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()







	def       A    (			self )    ->	List[Any]:
							a_							:			Optional[Any]     		=					self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "image_mean" ) )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "image_std" ) )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "do_normalize" ) )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "do_resize" ) )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "do_center_crop" ) )
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,     "size" ) )







	def       A    (			self )    ->	Optional[int]:
							a_							:			Dict     		=					self.image_processing_class(**self.image_processor_dict )
							# create random PIL videos
							a_							:			List[str]     		=					prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_SCREAMING_SNAKE_CASE )
							for video in video_inputs:
													self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													self.assertIsInstance(video[0]       ,     Image.Image )

							# Test not batched input
							a_							:			int     		=					image_processor(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
							a_							:			int     		=					image_processor(_SCREAMING_SNAKE_CASE       ,     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[Any]:
							a_							:			int     		=					self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							a_							:			str     		=					prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_SCREAMING_SNAKE_CASE       ,     numpify=_SCREAMING_SNAKE_CASE )
							for video in video_inputs:
													self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													self.assertIsInstance(video[0]       ,     np.ndarray )

							# Test not batched input
							a_							:			str     		=					image_processor(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
							a_							:			List[Any]     		=					image_processor(_SCREAMING_SNAKE_CASE       ,     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]:
							a_							:			Dict     		=					self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							a_							:			Dict     		=					prepare_video_inputs(self.image_processor_tester       ,     equal_resolution=_SCREAMING_SNAKE_CASE       ,     torchify=_SCREAMING_SNAKE_CASE )
							for video in video_inputs:
													self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													self.assertIsInstance(video[0]       ,     torch.Tensor )

							# Test not batched input
							a_							:			List[str]     		=					image_processor(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
							a_							:			Optional[Any]     		=					image_processor(_SCREAMING_SNAKE_CASE       ,     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"],
							    )       ,     )





"""simple docstring"""






import numpy as np

UpperCamelCase     					=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self )    ->	None:
							a_							:			List[Any]     		=					np.array(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							a_		,					a_							:			List[str]     		=					np.where(letter == self.SQUARE )
							a_							:			Union[str, Any]     		=					np.concatenate([indexa + 1, indexa + 1] )
							return indexes







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							return self.SQUARE[indexa - 1, indexa - 1]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			List[str]     		=					message.lower()
							a_							:			Union[str, Any]     		=					message.replace("j"       ,     "i" )

							a_							:			Tuple     		=					""
							for letter_index in range(len(_SCREAMING_SNAKE_CASE ) ):
													if message[letter_index] != " ":
																			a_							:			Dict     		=					self.letter_to_numbers(message[letter_index] )
																			a_							:			str     		=					encoded_message + str(numbers[0] ) + str(numbers[1] )
													elif message[letter_index] == " ":
																			a_							:			Any     		=					encoded_message + " "

							return encoded_message







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			Dict     		=					message.replace(" "       ,     "  " )
							a_							:			str     		=					""
							for numbers_index in range(int(len(_SCREAMING_SNAKE_CASE ) / 2 ) ):
													if message[numbers_index * 2] != " ":
																			a_							:			str     		=					message[numbers_index * 2]
																			a_							:			str     		=					message[numbers_index * 2 + 1]

																			a_							:			Optional[Any]     		=					self.numbers_to_letter(int(_SCREAMING_SNAKE_CASE )       ,     int(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Optional[Any]     		=					decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			a_							:			str     		=					decoded_message + " "

							return decoded_message




