



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ConditionalDetrImageProcessor



class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=7    ,	lowerCAmelCase=3    ,	lowerCAmelCase=30    ,	lowerCAmelCase=400    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase=[0.5, 0.5, 0.5]    ,	lowerCAmelCase=[0.5, 0.5, 0.5]    ,	lowerCAmelCase=True    ,	lowerCAmelCase=1 / 255    ,	lowerCAmelCase=True    ,	):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				min_resolution
							UpperCAmelCase_						=				max_resolution
							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							UpperCAmelCase_						=				do_normalize
							UpperCAmelCase_						=				image_mean
							UpperCAmelCase_						=				image_std
							UpperCAmelCase_						=				do_rescale
							UpperCAmelCase_						=				rescale_factor
							UpperCAmelCase_						=				do_pad



					def    A__		(			self     ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=False     ):
							if not batched:
									UpperCAmelCase_						=				image_inputs[0]
									if isinstance(lowerCAmelCase    ,	Image.Image     ):
											UpperCAmelCase_					,						UpperCAmelCase_						=				image.size
									else:
											UpperCAmelCase_					,						UpperCAmelCase_						=				image.shape[1], image.shape[2]
									if w < h:
											UpperCAmelCase_						=				int(self.size["shortest_edge"] * h / w     )
											UpperCAmelCase_						=				self.size["shortest_edge"]
									elif w > h:
											UpperCAmelCase_						=				self.size["shortest_edge"]
											UpperCAmelCase_						=				int(self.size["shortest_edge"] * w / h     )
									else:
											UpperCAmelCase_						=				self.size["shortest_edge"]
											UpperCAmelCase_						=				self.size["shortest_edge"]

							else:
									UpperCAmelCase_						=				[]
									for image in image_inputs:
											UpperCAmelCase_					,						UpperCAmelCase_						=				self.get_expected_values([image]     )
											expected_values.append((expected_height, expected_width)     )
									UpperCAmelCase_						=				max(lowerCAmelCase    ,	key=lambda lowerCAmelCase     : item[0]     )[0]
									UpperCAmelCase_						=				max(lowerCAmelCase    ,	key=lambda lowerCAmelCase     : item[1]     )[1]

							return expected_height, expected_width





@require_torch
@require_vision
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[Any]									=  ConditionalDetrImageProcessor if is_vision_available() else None



					def    A__		(			self     ):
							UpperCAmelCase_						=				ConditionalDetrImageProcessingTester(self     )



					@property
					def    A__		(			self     ):
							return self.image_processor_tester.prepare_image_processor_dict()



					def    A__		(			self     ):
							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    ,	"size"     )     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.image_processing_class.from_dict(self.image_processor_dict     )
							self.assertEqual(image_processor.size    ,	{"shortest_edge": 18, "longest_edge": 1333}     )
							self.assertEqual(image_processor.do_pad    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				self.image_processing_class.from_dict(
							    self.image_processor_dict    ,	size=42    ,	max_size=84    ,	pad_and_return_pixel_mask=lowerCAmelCase     )
							self.assertEqual(image_processor.size    ,	{"shortest_edge": 42, "longest_edge": 84}     )
							self.assertEqual(image_processor.do_pad    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random PIL images
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	Image.Image     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random numpy tensors
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase    ,	numpify=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	np.ndarray     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random PyTorch tensors
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase    ,	torchify=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	torch.Tensor     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					@slow
					def    A__		(			self     ):
							# prepare image and target
							UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,	"r"     ) as f:
									UpperCAmelCase_						=				json.loads(f.read()     )

							UpperCAmelCase_						=				{"image_id": 3_9769, "annotations": target}

							# encode them
							UpperCAmelCase_						=				ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"     )
							UpperCAmelCase_						=				image_processing(images=lowerCAmelCase    ,	annotations=lowerCAmelCase    ,	return_tensors="pt"     )

							# verify pixel values
							UpperCAmelCase_						=				torch.Size([1, 3, 800, 1066]     )
							self.assertEqual(encoding["pixel_values"].shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

							# verify area
							UpperCAmelCase_						=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	lowerCAmelCase     )     )
							# verify boxes
							UpperCAmelCase_						=				torch.Size([6, 4]     )
							self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	lowerCAmelCase    ,	atol=1e-3     )     )
							# verify image_id
							UpperCAmelCase_						=				torch.tensor([3_9769]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	lowerCAmelCase     )     )
							# verify is_crowd
							UpperCAmelCase_						=				torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	lowerCAmelCase     )     )
							# verify class_labels
							UpperCAmelCase_						=				torch.tensor([75, 75, 63, 65, 17, 17]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	lowerCAmelCase     )     )
							# verify orig_size
							UpperCAmelCase_						=				torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	lowerCAmelCase     )     )
							# verify size
							UpperCAmelCase_						=				torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	lowerCAmelCase     )     )



					@slow
					def    A__		(			self     ):
							# prepare image, target and masks_path
							UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,	"r"     ) as f:
									UpperCAmelCase_						=				json.loads(f.read()     )

							UpperCAmelCase_						=				{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

							UpperCAmelCase_						=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

							# encode them
							UpperCAmelCase_						=				ConditionalDetrImageProcessor(format="coco_panoptic"     )
							UpperCAmelCase_						=				image_processing(images=lowerCAmelCase    ,	annotations=lowerCAmelCase    ,	masks_path=lowerCAmelCase    ,	return_tensors="pt"     )

							# verify pixel values
							UpperCAmelCase_						=				torch.Size([1, 3, 800, 1066]     )
							self.assertEqual(encoding["pixel_values"].shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

							# verify area
							UpperCAmelCase_						=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	lowerCAmelCase     )     )
							# verify boxes
							UpperCAmelCase_						=				torch.Size([6, 4]     )
							self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	lowerCAmelCase    ,	atol=1e-3     )     )
							# verify image_id
							UpperCAmelCase_						=				torch.tensor([3_9769]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	lowerCAmelCase     )     )
							# verify is_crowd
							UpperCAmelCase_						=				torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	lowerCAmelCase     )     )
							# verify class_labels
							UpperCAmelCase_						=				torch.tensor([17, 17, 63, 75, 75, 93]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	lowerCAmelCase     )     )
							# verify masks
							UpperCAmelCase_						=				82_2873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,	lowerCAmelCase     )
							# verify orig_size
							UpperCAmelCase_						=				torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	lowerCAmelCase     )     )
							# verify size
							UpperCAmelCase_						=				torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	lowerCAmelCase     )     )






from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							List[List[ImageInput]]:
		if isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ) and isinstance(videos[0]			,     (list, tuple) ) and is_valid_image(videos[0][0] ):
				return videos

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

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

		raise ValueError(f'''Could not make batched video from {videos}''' )



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[Any]									=  ['pixel_values']



					def __init__(			self    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	lowerCAmelCase = 1 / 255    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 224}
							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_						=				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    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							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    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							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    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return rescale(lowerCAmelCase    ,	scale=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return normalize(lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	):
							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."     )

							# 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     )

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

							UpperCAmelCase_						=				to_channel_dimension_format(lowerCAmelCase    ,	lowerCAmelCase     )
							return image



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	**lowerCAmelCase    ,	):
							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_						=				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    ,	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     )

