





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 DeformableDetrImageProcessor

class SCREAMING_SNAKE_CASE_ (					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
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
										lowerCamelCase__       =       parent
										lowerCamelCase__       =       batch_size
										lowerCamelCase__       =       num_channels
										lowerCamelCase__       =       min_resolution
										lowerCamelCase__       =       max_resolution
										lowerCamelCase__       =       do_resize
										lowerCamelCase__       =       size
										lowerCamelCase__       =       do_normalize
										lowerCamelCase__       =       image_mean
										lowerCamelCase__       =       image_std
										lowerCamelCase__       =       do_rescale
										lowerCamelCase__       =       rescale_factor
										lowerCamelCase__       =       do_pad





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

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

										return expected_height, expected_width

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ (					__lowerCamelCase							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    DeformableDetrImageProcessor if is_vision_available() else None





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       DeformableDetrImageProcessingTester(self	)





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(a_	,  "image_mean"	)	)
										self.assertTrue(hasattr(a_	,  "image_std"	)	)
										self.assertTrue(hasattr(a_	,  "do_normalize"	)	)
										self.assertTrue(hasattr(a_	,  "do_resize"	)	)
										self.assertTrue(hasattr(a_	,  "do_rescale"	)	)
										self.assertTrue(hasattr(a_	,  "do_pad"	)	)
										self.assertTrue(hasattr(a_	,  "size"	)	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       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	,  a_	)

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





					def 					__magic_name__ ( self	):
										pass





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

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

										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	)

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

										# Test batched
										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	,  batched=a_	)

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

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

										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	)

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

										# Test batched
										lowerCamelCase__       =       image_processing(a_	,  return_tensors="pt"	).pixel_values

										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	,  batched=a_	)

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





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

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

										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	)

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

										# Test batched
										lowerCamelCase__       =       image_processing(a_	,  return_tensors="pt"	).pixel_values

										lowerCamelCase__       =       self.image_processor_tester.get_expected_values(a_	,  batched=a_	)

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





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

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

										# encode them
										lowerCamelCase__       =       DeformableDetrImageProcessor()
										lowerCamelCase__       =       image_processing(images=a_	,  annotations=a_	,  return_tensors="pt"	)

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

										lowerCamelCase__       =       torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  a_	,  atol=1E-4	)	)

										# verify area
										lowerCamelCase__       =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  a_	)	)
										# verify boxes
										lowerCamelCase__       =       torch.Size([6, 4]	)
										self.assertEqual(encoding["labels"][0]["boxes"].shape	,  a_	)
										lowerCamelCase__       =       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  a_	,  atol=1E-3	)	)
										# verify image_id
										lowerCamelCase__       =       torch.tensor([3_9769]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  a_	)	)
										# verify is_crowd
										lowerCamelCase__       =       torch.tensor([0, 0, 0, 0, 0, 0]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  a_	)	)
										# verify class_labels
										lowerCamelCase__       =       torch.tensor([75, 75, 63, 65, 17, 17]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  a_	)	)
										# verify orig_size
										lowerCamelCase__       =       torch.tensor([480, 640]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  a_	)	)
										# verify size
										lowerCamelCase__       =       torch.tensor([800, 1066]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  a_	)	)






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

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

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

										# encode them
										lowerCamelCase__       =       DeformableDetrImageProcessor(format="coco_panoptic"	)
										lowerCamelCase__       =       image_processing(images=a_	,  annotations=a_	,  masks_path=a_	,  return_tensors="pt"	)

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

										lowerCamelCase__       =       torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  a_	,  atol=1E-4	)	)

										# verify area
										lowerCamelCase__       =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  a_	)	)
										# verify boxes
										lowerCamelCase__       =       torch.Size([6, 4]	)
										self.assertEqual(encoding["labels"][0]["boxes"].shape	,  a_	)
										lowerCamelCase__       =       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  a_	,  atol=1E-3	)	)
										# verify image_id
										lowerCamelCase__       =       torch.tensor([3_9769]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  a_	)	)
										# verify is_crowd
										lowerCamelCase__       =       torch.tensor([0, 0, 0, 0, 0, 0]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  a_	)	)
										# verify class_labels
										lowerCamelCase__       =       torch.tensor([17, 17, 63, 75, 75, 93]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  a_	)	)
										# verify masks
										lowerCamelCase__       =       82_2873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,  a_	)
										# verify orig_size
										lowerCamelCase__       =       torch.tensor([480, 640]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  a_	)	)
										# verify size
										lowerCamelCase__       =       torch.tensor([800, 1066]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  a_	)	)








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_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->List[List[ImageInput]]:
					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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["pixel_values"]





					def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 256}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										if "shortest_edge" in size:
															lowerCamelCase__       =       get_resize_output_image_size(_lowerCAmelCase	,  size["shortest_edge"]	,  default_to_square=_lowerCAmelCase	)
										elif "height" in size and "width" in size:
															lowerCamelCase__       =       (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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       image.astype(np.floataa	)
										if offset:
															lowerCamelCase__       =       image - (scale / 2)
										return rescale(_lowerCAmelCase	,  scale=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										return normalize(_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( 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	,  ):
										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.
										lowerCamelCase__       =       to_numpy_array(_lowerCAmelCase	)

										if do_resize:
															lowerCamelCase__       =       self.resize(image=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	)

										if do_center_crop:
															lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	,  size=_lowerCAmelCase	)

										if do_rescale:
															lowerCamelCase__       =       self.rescale(image=_lowerCAmelCase	,  scale=_lowerCAmelCase	,  offset=_lowerCAmelCase	)

										if do_normalize:
															lowerCamelCase__       =       self.normalize(image=_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	)

										lowerCamelCase__       =       to_channel_dimension_format(_lowerCAmelCase	,  _lowerCAmelCase	)
										return image






					def 					__magic_name__ ( 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 = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__       =       resample if resample is not None else self.resample
										lowerCamelCase__       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__       =       do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__       =       offset if offset is not None else self.offset
										lowerCamelCase__       =       do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__       =       image_std if image_std is not None else self.image_std

										lowerCamelCase__       =       size if size is not None else self.size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       =       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."	)

										lowerCamelCase__       =       make_batched(_lowerCAmelCase	)

										lowerCamelCase__       =       [
										    [
										        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
										]

										lowerCamelCase__       =       {"pixel_values": videos}
										return BatchFeature(data=_lowerCAmelCase	,  tensor_type=_lowerCAmelCase	)

