



'''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   lowercase_	(   lowerCAmelCase__	:  str			,							lowerCAmelCase__	:  Optional[Any]=10			,							lowerCAmelCase__	:  int=10			,							lowerCAmelCase__	:  int=False			,							lowerCAmelCase__	:  Optional[Any]=False ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[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
						__UpperCAmelCase   :      Optional[Any]										=     [Image.fromarray(np.moveaxis(lowerCAmelCase__			,							0			,							-1 ) ) for frame in video]

			if torchify:
						__UpperCAmelCase   :      Optional[Any]										=     [torch.from_numpy(lowerCAmelCase__ ) for frame in video]

			return video


def   lowercase_	(   lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  str=False			,							lowerCAmelCase__	:  Optional[Any]=False			,							lowerCAmelCase__	:  List[str]=False ):

			"""simple docstring"""





			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

			return video_inputs



class _A  (    unittest.TestCase    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=4 ,       __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=True ,       __UpperCAmelCase=None ,       )       ->     int:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     size if size is not None else {"""shortest_edge""": 18}
					__UpperCAmelCase   :      Optional[Any]										=     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					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 _A  (    __SCREAMING_SNAKE_CASE	,				unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    str						      =			TvltImageProcessor if is_vision_available() else None





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     TvltImageProcessorTester(self				)





		@property
		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     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				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PIL videos
					__UpperCAmelCase   :      List[Any]										=     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
					__UpperCAmelCase   :      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
					__UpperCAmelCase   :      Tuple										=     image_processor(__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:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     self.image_processing_class(**self.image_processor_dict				)
					# create random numpy tensors
					__UpperCAmelCase   :      List[Any]										=     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
					__UpperCAmelCase   :      Union[str, Any]										=     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
					__UpperCAmelCase   :      Optional[Any]										=     image_processor(__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				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					__UpperCAmelCase   :      int										=     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
					__UpperCAmelCase   :      Optional[Any]										=     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
					__UpperCAmelCase   :      Union[str, Any]										=     image_processor(__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"""],
					    ) ,       )









'''simple docstring'''
from __future__ import annotations

from typing import Any



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:  # noqa: A002

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     item
					__UpperCAmelCase   :      str										=     next



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Node | None										=     None
					__UpperCAmelCase   :      Optional[int]										=     0





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     Node(__UpperCAmelCase ,       self.head				)
					self.size += 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					# Switched 'self.is_empty()' to 'self.head is None'
					# because mypy was considering the possibility that 'self.head'
					# can be None in below else part and giving error
					if self.head is None:
								return None
					else:
								__UpperCAmelCase   :      str										=     self.head.item
								__UpperCAmelCase   :      Tuple										=     self.head.next
								self.size -= 1
								return item





		def 							__A       (     self				)       ->     bool:

					'''simple docstring'''
					return self.head is None





		def __str__(     self				)       ->     str:

					'''simple docstring'''
					if self.is_empty():
								return ""
					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self.head
								__UpperCAmelCase   :      List[Any]										=     """"""
								__UpperCAmelCase   :      list[str]										=     []
								while iterate:
											item_list.append(str(iterate.item				)				)
											__UpperCAmelCase   :      Any										=     iterate.next

								__UpperCAmelCase   :      Dict										=     """ --> """.join(__UpperCAmelCase				)

								return item_str





		def __len__(     self				)       ->     int:

					'''simple docstring'''
					return self.size




