

"""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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__=10    ,							lowercase__=10    ,							lowercase__=False    ,							lowercase__=False   ):
					"""simple docstring"""
					A    =      []
					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    =      [Image.fromarray(np.moveaxis(lowercase__    ,							0    ,							-1   )   ) for frame in video]

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

					return video
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__=False    ,							lowercase__=False    ,							lowercase__=False   ):
					"""simple docstring"""
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					A    =      []
					for i in range(image_processor_tester.batch_size   ):
										if equal_resolution:
															A    =      A    =      image_processor_tester.max_resolution
										else:
															A    ,					A    =      np.random.choice(
															    np.arange(image_processor_tester.min_resolution    ,							image_processor_tester.max_resolution   )    ,							2   )
															A    =      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		):


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

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


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							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				(     _A  ,	unittest.TestCase		):
		SCREAMING_SNAKE_CASE              =					TvltImageProcessor if is_vision_available() else None


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
							A    =      TvltImageProcessorTester(self)


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


		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	str):
							# Initialize image_processor
							A    =      self.image_processing_class(**self.image_processor_dict)
							# create random PIL videos
							A    =      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    =      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    =      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       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							# Initialize image_processor
							A    =      self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							A    =      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    =      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    =      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       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							# Initialize image_processor
							A    =      self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							A    =      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    =      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    =      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"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															res *= a

										a *= a
										b >>= 1

					return res
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															A    =      ((res % c) * (a % c)) % c

										a *= a
										b >>= 1

					return res





