

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				Dict											=							StableDiffusionModelEditingPipeline
						lowercase  :				List[Any]											=							TEXT_TO_IMAGE_PARAMS
						lowercase  :				List[Any]											=							TEXT_TO_IMAGE_BATCH_PARAMS
						lowercase  :				List[Any]											=							TEXT_TO_IMAGE_IMAGE_PARAMS
						lowercase  :				List[Any]											=							TEXT_TO_IMAGE_IMAGE_PARAMS

						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									torch.manual_seed(0    )
									__UpperCamelCase    :    Tuple								=      UNetaDConditionModel(
									    block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
									__UpperCamelCase    :    str								=      DDIMScheduler()
									torch.manual_seed(0    )
									__UpperCamelCase    :    Optional[Any]								=      AutoencoderKL(
									    block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
									torch.manual_seed(0    )
									__UpperCamelCase    :    Union[str, Any]								=      CLIPTextConfig(
									    bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
									__UpperCamelCase    :    Union[str, Any]								=      CLIPTextModel(__UpperCamelCase    )
									__UpperCamelCase    :    Tuple								=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

									__UpperCamelCase    :    Any								=      {
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase=0    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      torch.manual_seed(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      {
									    "prompt": "A field of roses",
									    "generator": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    "height": None,
									    "width": None,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs

						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      "cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase    :    Dict								=      self.get_dummy_components()
									__UpperCamelCase    :    Dict								=      StableDiffusionModelEditingPipeline(**__UpperCamelCase    )
									__UpperCamelCase    :    Optional[Any]								=      sd_pipe.to(__UpperCamelCase    )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

									__UpperCamelCase    :    List[Any]								=      self.get_dummy_inputs(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      sd_pipe(**__UpperCamelCase    ).images
									__UpperCamelCase    :    List[str]								=      image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__UpperCamelCase    :    Optional[Any]								=      np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      "cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase    :    str								=      self.get_dummy_components()
									__UpperCamelCase    :    Union[str, Any]								=      StableDiffusionModelEditingPipeline(**__UpperCamelCase    )
									__UpperCamelCase    :    Optional[Any]								=      sd_pipe.to(__UpperCamelCase    )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

									__UpperCamelCase    :    Dict								=      self.get_dummy_inputs(__UpperCamelCase    )
									__UpperCamelCase    :    str								=      "french fries"
									__UpperCamelCase    :    Union[str, Any]								=      sd_pipe(**__UpperCamelCase , negative_prompt=__UpperCamelCase    )
									__UpperCamelCase    :    Tuple								=      output.images
									__UpperCamelCase    :    Optional[Any]								=      image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__UpperCamelCase    :    Optional[int]								=      np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      "cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase    :    Any								=      self.get_dummy_components()
									__UpperCamelCase    :    Optional[Any]								=      EulerAncestralDiscreteScheduler(
									    beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear"    )
									__UpperCamelCase    :    Tuple								=      StableDiffusionModelEditingPipeline(**__UpperCamelCase    )
									__UpperCamelCase    :    Optional[Any]								=      sd_pipe.to(__UpperCamelCase    )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

									__UpperCamelCase    :    Dict								=      self.get_dummy_inputs(__UpperCamelCase    )
									__UpperCamelCase    :    Optional[Any]								=      sd_pipe(**__UpperCamelCase    ).images
									__UpperCamelCase    :    List[str]								=      image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__UpperCamelCase    :    List[Any]								=      np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      "cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase    :    Optional[int]								=      self.get_dummy_components()
									__UpperCamelCase    :    str								=      PNDMScheduler()
									__UpperCamelCase    :    Union[str, Any]								=      StableDiffusionModelEditingPipeline(**__UpperCamelCase    )
									__UpperCamelCase    :    Optional[int]								=      sd_pipe.to(__UpperCamelCase    )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

									__UpperCamelCase    :    Tuple								=      self.get_dummy_inputs(__UpperCamelCase    )
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(__UpperCamelCase    ):
												__UpperCamelCase    :    List[Any]								=      sd_pipe(**__UpperCamelCase    ).images

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									super().test_inference_batch_single_identical(expected_max_diff=5E-3    )




						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3    )







@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

						def __lowerCamelCase					(				self , __UpperCamelCase=0    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      torch.manual_seed(__UpperCamelCase    )
									__UpperCamelCase    :    Optional[int]								=      {
									    "prompt": "A field of roses",
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      "CompVis/stable-diffusion-v1-4"
									__UpperCamelCase    :    Any								=      StableDiffusionModelEditingPipeline.from_pretrained(__UpperCamelCase , safety_checker=__UpperCamelCase    )
									pipe.to(__UpperCamelCase    )
									pipe.set_progress_bar_config(disable=__UpperCamelCase    )
									pipe.enable_attention_slicing()

									__UpperCamelCase    :    List[Any]								=      self.get_inputs()
									__UpperCamelCase    :    Optional[int]								=      pipe(**__UpperCamelCase    ).images
									__UpperCamelCase    :    str								=      image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									__UpperCamelCase    :    int								=      np.array(
									    [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658]    )

									assert np.abs(expected_slice - image_slice    ).max() < 1E-2

									# make sure image changes after editing
									pipe.edit_model("A pack of roses" , "A pack of blue roses"    )

									__UpperCamelCase    :    Tuple								=      pipe(**__UpperCamelCase    ).images
									__UpperCamelCase    :    List[str]								=      image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									assert np.abs(expected_slice - image_slice    ).max() > 1E-1




						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__UpperCamelCase    :    int								=      "CompVis/stable-diffusion-v1-4"
									__UpperCamelCase    :    List[str]								=      DDIMScheduler.from_pretrained(__UpperCamelCase , subfolder="scheduler"    )
									__UpperCamelCase    :    Any								=      StableDiffusionModelEditingPipeline.from_pretrained(
									    __UpperCamelCase , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase    )
									__UpperCamelCase    :    Any								=      pipe.to(__UpperCamelCase    )
									pipe.set_progress_bar_config(disable=__UpperCamelCase    )
									pipe.enable_attention_slicing(1    )
									pipe.enable_sequential_cpu_offload()

									__UpperCamelCase    :    Tuple								=      self.get_inputs()
									__UpperCamelCase    :    Union[str, Any]								=      pipe(**__UpperCamelCase    )

									__UpperCamelCase    :    Dict								=      torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 10**9


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase     :       str        =       logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				str											=							['pixel_values']

						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )							->	None:




									'''simple docstring'''


									super().__init__(**__UpperCamelCase    )

									__UpperCamelCase    :    Optional[Any]								=      size if size is not None else {"height": 25_60, "width": 19_20}
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# The previous feature extractor size parameter was in (width, height) format
												__UpperCamelCase    :    Dict								=      size[::-1]
									__UpperCamelCase    :    Optional[int]								=      get_size_dict(__UpperCamelCase    )

									__UpperCamelCase    :    List[Any]								=      do_resize
									__UpperCamelCase    :    Tuple								=      size
									__UpperCamelCase    :    Optional[int]								=      resample
									__UpperCamelCase    :    Optional[Any]								=      do_thumbnail
									__UpperCamelCase    :    Any								=      do_align_long_axis
									__UpperCamelCase    :    str								=      do_pad
									__UpperCamelCase    :    str								=      do_rescale
									__UpperCamelCase    :    Union[str, Any]								=      rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize
									__UpperCamelCase    :    List[Any]								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      size["height"], size["width"]

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
												__UpperCamelCase    :    str								=      np.rotaa(__UpperCamelCase , 3    )

									if data_format is not None:
												__UpperCamelCase    :    str								=      to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    )

									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = None , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      size["height"], size["width"]
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      get_image_size(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      output_width - input_width
									__UpperCamelCase    :    Union[str, Any]								=      output_height - input_height

									if random_padding:
												__UpperCamelCase    :    Any								=      np.random.randint(low=0 , high=delta_height + 1    )
												__UpperCamelCase    :    Union[str, Any]								=      np.random.randint(low=0 , high=delta_width + 1    )
									else:
												__UpperCamelCase    :    Any								=      delta_height // 2
												__UpperCamelCase    :    Dict								=      delta_width // 2

									__UpperCamelCase    :    Optional[int]								=      delta_height - pad_top
									__UpperCamelCase    :    Dict								=      delta_width - pad_left

									__UpperCamelCase    :    str								=      ((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(__UpperCamelCase , __UpperCamelCase , data_format=__UpperCamelCase    )

						def __lowerCamelCase					(				self , *__UpperCamelCase , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
									return self.pad_image(*__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      size["height"], size["width"]

									# We always resize to the smallest of either the input or output size.
									__UpperCamelCase    :    int								=      min(__UpperCamelCase , __UpperCamelCase    )
									__UpperCamelCase    :    List[str]								=      min(__UpperCamelCase , __UpperCamelCase    )

									if height == input_height and width == input_width:
												return image

									if input_height > input_width:
												__UpperCamelCase    :    List[Any]								=      int(input_width * height / input_height    )
									elif input_width > input_height:
												__UpperCamelCase    :    Tuple								=      int(input_height * width / input_width    )

									return resize(
									    __UpperCamelCase , size=(height, width) , resample=__UpperCamelCase , reducing_gap=2.0 , data_format=__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    str								=      min(size["height"] , size["width"]    )
									__UpperCamelCase    :    Tuple								=      get_resize_output_image_size(__UpperCamelCase , size=__UpperCamelCase , default_to_square=__UpperCamelCase    )
									__UpperCamelCase    :    List[Any]								=      resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )
									return resized_image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	Dict:




									'''simple docstring'''


									return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


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




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      do_resize if do_resize is not None else self.do_resize
									__UpperCamelCase    :    Any								=      size if size is not None else self.size
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# Previous feature extractor had size in (width, height) format
												__UpperCamelCase    :    List[Any]								=      size[::-1]
									__UpperCamelCase    :    List[Any]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      resample if resample is not None else self.resample
									__UpperCamelCase    :    Any								=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									__UpperCamelCase    :    List[Any]								=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									__UpperCamelCase    :    Optional[int]								=      do_pad if do_pad is not None else self.do_pad
									__UpperCamelCase    :    Dict								=      do_rescale if do_rescale is not None else self.do_rescale
									__UpperCamelCase    :    List[str]								=      rescale_factor if rescale_factor is not None else self.rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase    :    Union[str, Any]								=      image_mean if image_mean is not None else self.image_mean
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else self.image_std

									__UpperCamelCase    :    List[Any]								=      make_list_of_images(__UpperCamelCase    )

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

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

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

									if do_pad and size is None:
												raise ValueError("Size must be specified if do_pad 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    :    Union[str, Any]								=      [to_numpy_array(__UpperCamelCase    ) for image in images]

									if do_align_long_axis:
												__UpperCamelCase    :    Tuple								=      [self.align_long_axis(__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_resize:
												__UpperCamelCase    :    Optional[int]								=      [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase    ) for image in images]

									if do_thumbnail:
												__UpperCamelCase    :    Union[str, Any]								=      [self.thumbnail(image=__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_pad:
												__UpperCamelCase    :    Optional[Any]								=      [self.pad_image(image=__UpperCamelCase , size=__UpperCamelCase , random_padding=__UpperCamelCase    ) for image in images]

									if do_rescale:
												__UpperCamelCase    :    Optional[int]								=      [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase    ) for image in images]

									if do_normalize:
												__UpperCamelCase    :    Union[str, Any]								=      [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    List[str]								=      [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    int								=      {"pixel_values": images}
									return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase    )