

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 						__UpperCamelCase    (					unittest.TestCase						):




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Union[str, Any]=7		,	lowerCAmelCase	:						Tuple=3		,	lowerCAmelCase	:						Any=10		,	lowerCAmelCase	:						Union[str, Any]=18		,	lowerCAmelCase	:						str=30		,	lowerCAmelCase	:						Tuple=400		,	lowerCAmelCase	:						Optional[int]=True		,	lowerCAmelCase	:						Tuple=None		,	lowerCAmelCase	:						List[str]=True		,	lowerCAmelCase	:						str=[0.5, 0.5, 0.5]		,	lowerCAmelCase	:						List[str]=[0.5, 0.5, 0.5]		,	lowerCAmelCase	:						Optional[Any]=None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			size if size is not None else {"shortest_edge": 18}
		UpperCAmelCase_       				=			crop_size if crop_size is not None else {"height": 18, "width": 18}

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




	def       __A					(		self	:						Union[str, 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,
		    "crop_size": self.crop_size,
		}


@require_torch
@require_vision
class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     VivitImageProcessor if is_vision_available() else None




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			VivitImageProcessingTester(self							)




	@property
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return self.image_processor_tester.prepare_image_processor_dict()




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"image_mean"							)							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"image_std"							)							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"do_normalize"							)							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"do_resize"							)							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"do_center_crop"							)							)
		self.assertTrue(hasattr(lowerCAmelCase		,	"size"							)							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			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}							)

		UpperCAmelCase_       				=			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	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL videos
		UpperCAmelCase_       				=			prepare_video_inputs(self.image_processor_tester		,	equal_resolution=lowerCAmelCase							)
		for video in video_inputs:
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)
			self.assertIsInstance(video[0]		,	Image.Image							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(lowerCAmelCase		,	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_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		UpperCAmelCase_       				=			prepare_video_inputs(self.image_processor_tester		,	equal_resolution=lowerCAmelCase		,	numpify=lowerCAmelCase							)
		for video in video_inputs:
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)
			self.assertIsInstance(video[0]		,	np.ndarray							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(lowerCAmelCase		,	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	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		UpperCAmelCase_       				=			prepare_video_inputs(self.image_processor_tester		,	equal_resolution=lowerCAmelCase		,	torchify=lowerCAmelCase							)
		for video in video_inputs:
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)
			self.assertIsInstance(video[0]		,	torch.Tensor							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(lowerCAmelCase		,	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"],
		    )		,	)


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_a:    Optional[Any] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	if isinstance(A       ,     (list, tuple)    ) and isinstance(videos[0]       ,     (list, tuple)    ) and is_valid_image(videos[0][0]    ):
		return videos

	elif isinstance(A       ,     (list, tuple)    ) and is_valid_image(videos[0]    ):
		return [videos]

	elif is_valid_image(A    ):
		return [[videos]]

	raise ValueError(F"Could not make batched video from {videos}"    )




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




	def __init__(		self	:						int		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[int, float] = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_       				=			size if size is not None else {"shortest_edge": 256}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else {"height": 224, "width": 224}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

		UpperCAmelCase_       				=			do_resize
		UpperCAmelCase_       				=			size
		UpperCAmelCase_       				=			do_center_crop
		UpperCAmelCase_       				=			crop_size
		UpperCAmelCase_       				=			resample
		UpperCAmelCase_       				=			do_rescale
		UpperCAmelCase_       				=			rescale_factor
		UpperCAmelCase_       				=			offset
		UpperCAmelCase_       				=			do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		UpperCAmelCase_       				=			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		if "shortest_edge" in size:
			UpperCAmelCase_       				=			get_resize_output_image_size(lowerCAmelCase		,	size["shortest_edge"]		,	default_to_square=lowerCAmelCase							)
		elif "height" in size and "width" in size:
			UpperCAmelCase_       				=			(size["height"], size["width"])
		else:
			raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
		return resize(lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"							)
		return center_crop(lowerCAmelCase		,	size=(size["height"], size["width"])		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			image.astype(np.floataa							)
		if offset:
			UpperCAmelCase_       				=			image - (scale / 2)
		return rescale(lowerCAmelCase		,	scale=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







		return normalize(lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[ChannelDimension] = ChannelDimension.FIRST		,	):


		'''simple docstring'''







		if do_resize and size is None or resample is None:
			raise ValueError("Size and resample must be specified if do_resize is True."							)

		if do_center_crop and crop_size is None:
			raise ValueError("Crop size must be specified if do_center_crop is True."							)

		if do_rescale and rescale_factor is None:
			raise ValueError("Rescale factor must be specified if do_rescale is True."							)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("Image mean and std must be specified if do_normalize is True."							)

		if offset and not do_rescale:
			raise ValueError("For offset, do_rescale must also be set to True."							)

		# All transformations expect numpy arrays.
		UpperCAmelCase_       				=			to_numpy_array(lowerCAmelCase							)

		if do_resize:
			UpperCAmelCase_       				=			self.resize(image=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase							)

		if do_center_crop:
			UpperCAmelCase_       				=			self.center_crop(lowerCAmelCase		,	size=lowerCAmelCase							)

		if do_rescale:
			UpperCAmelCase_       				=			self.rescale(image=lowerCAmelCase		,	scale=lowerCAmelCase		,	offset=lowerCAmelCase							)

		if do_normalize:
			UpperCAmelCase_       				=			self.normalize(image=lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase							)

		UpperCAmelCase_       				=			to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							)
		return image




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			do_resize if do_resize is not None else self.do_resize
		UpperCAmelCase_       				=			resample if resample is not None else self.resample
		UpperCAmelCase_       				=			do_center_crop if do_center_crop is not None else self.do_center_crop
		UpperCAmelCase_       				=			do_rescale if do_rescale is not None else self.do_rescale
		UpperCAmelCase_       				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		UpperCAmelCase_       				=			offset if offset is not None else self.offset
		UpperCAmelCase_       				=			do_normalize if do_normalize is not None else self.do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else self.image_mean
		UpperCAmelCase_       				=			image_std if image_std is not None else self.image_std

		UpperCAmelCase_       				=			size if size is not None else self.size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else self.crop_size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	param_name="crop_size"							)

		if not valid_images(lowerCAmelCase							):
			raise ValueError(
			    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
			    "torch.Tensor, tf.Tensor or jax.ndarray."							)

		UpperCAmelCase_       				=			make_batched(lowerCAmelCase							)

		UpperCAmelCase_       				=			[
		    [
		        self._preprocess_image(
		            image=lowerCAmelCase		,	do_resize=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	do_center_crop=lowerCAmelCase		,	crop_size=lowerCAmelCase		,	do_rescale=lowerCAmelCase		,	rescale_factor=lowerCAmelCase		,	offset=lowerCAmelCase		,	do_normalize=lowerCAmelCase		,	image_mean=lowerCAmelCase		,	image_std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	)
		        for img in video
		    ]
		    for video in videos
		]

		UpperCAmelCase_       				=			{"pixel_values": videos}
		return BatchFeature(data=lowerCAmelCase		,	tensor_type=lowerCAmelCase							)