





'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   UnCLIPImageVariationPipeline
     UpperCAmelCase__       :						str      =   IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
     UpperCAmelCase__       :						int      =   IMAGE_VARIATION_BATCH_PARAMS

     UpperCAmelCase__       :						int      =   [
         '''generator''',
         '''return_dict''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]
     UpperCAmelCase__       :						Optional[int]      =   False

     @property
     def 	UpperCamelCase(					self	):
       return 32

     @property
     def 	UpperCamelCase(					self	):
       return 32

     @property
     def 	UpperCamelCase(					self	):
       return self.time_input_dim

     @property
     def 	UpperCamelCase(					self	):
       return self.time_input_dim * 4

     @property
     def 	UpperCamelCase(					self	):
       return 100

     @property
     def 	UpperCamelCase(					self	):
       _snake_case     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
       return tokenizer

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				CLIPTextConfig(
           bos_token_id=0						,   eos_token_id=2						,   hidden_size=self.text_embedder_hidden_size						,   projection_dim=self.text_embedder_hidden_size						,   intermediate_size=37						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_000						,   )
       return CLIPTextModelWithProjection(lowerCamelCase	)

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						,   projection_dim=self.text_embedder_hidden_size						,   num_hidden_layers=5						,   num_attention_heads=4						,   image_size=32						,   intermediate_size=37						,   patch_size=1						,   )
       return CLIPVisionModelWithProjection(lowerCamelCase	)

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)

       _snake_case     		=				{
           "clip_embeddings_dim": self.text_embedder_hidden_size,
           "time_embed_dim": self.time_embed_dim,
           "cross_attention_dim": self.cross_attention_dim,
       }

       _snake_case     		=				UnCLIPTextProjModel(**lowerCamelCase	)
       return model

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)

       _snake_case     		=				{
           "sample_size": 32,
           # RGB in channels
           "in_channels": 3,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 6,
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": "identity",
       }

       _snake_case     		=				UNetaDConditionModel(**lowerCamelCase	)
       return model

     @property
     def 	UpperCamelCase(					self	):
       return {
           "sample_size": 64,
           "layers_per_block": 1,
           "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
           "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "in_channels": 6,
           "out_channels": 3,
       }

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)

       _snake_case     		=				UNetaDModel(**self.dummy_super_res_kwargs	)
       return model

     @property
     def 	UpperCamelCase(					self	):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1	)

       _snake_case     		=				UNetaDModel(**self.dummy_super_res_kwargs	)
       return model

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.dummy_decoder
       _snake_case     		=				self.dummy_text_proj
       _snake_case     		=				self.dummy_text_encoder
       _snake_case     		=				self.dummy_tokenizer
       _snake_case     		=				self.dummy_super_res_first
       _snake_case     		=				self.dummy_super_res_last

       _snake_case     		=				UnCLIPScheduler(
           variance_type="learned_range"						,   prediction_type="epsilon"						,   num_train_timesteps=1_000						,   )

       _snake_case     		=				UnCLIPScheduler(
           variance_type="fixed_small_log"						,   prediction_type="epsilon"						,   num_train_timesteps=1_000						,   )

       _snake_case     		=				CLIPImageProcessor(crop_size=32						,   size=32	)

       _snake_case     		=				self.dummy_image_encoder

       return {
           "decoder": decoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_proj": text_proj,
           "feature_extractor": feature_extractor,
           "image_encoder": image_encoder,
           "super_res_first": super_res_first,
           "super_res_last": super_res_last,
           "decoder_scheduler": decoder_scheduler,
           "super_res_scheduler": super_res_scheduler,
       }

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=0						,   lowerCamelCase=True	):
       _snake_case     		=				floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
       if str(lowerCamelCase	).startswith("mps"	):
         _snake_case     		=				torch.manual_seed(lowerCamelCase	)
       else:
         _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)

       if pil_image:
         _snake_case     		=				input_image * 0.5 + 0.5
         _snake_case     		=				input_image.clamp(0						,   1	)
         _snake_case     		=				input_image.cpu().permute(0						,   2						,   3						,   1	).float().numpy()
         _snake_case     		=				DiffusionPipeline.numpy_to_pil(lowerCamelCase	)[0]

       return {
           "image": input_image,
           "generator": generator,
           "decoder_num_inference_steps": 2,
           "super_res_num_inference_steps": 2,
           "output_type": "np",
       }

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

       _snake_case     		=				self.get_dummy_components()

       _snake_case     		=				self.pipeline_class(**lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)

       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)

       _snake_case     		=				pipe(**lowerCamelCase	)
       _snake_case     		=				output.images

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)

       _snake_case     		=				pipe(
           **lowerCamelCase						,   return_dict=lowerCamelCase						,   )[0]

       _snake_case     		=				image[0, -3:, -3:, -1]
       _snake_case     		=				image_from_tuple[0, -3:, -3:, -1]

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

       _snake_case     		=				np.array(
           [
               0.9997,
               0.0002,
               0.9997,
               0.9997,
               0.9969,
               0.0023,
               0.9997,
               0.9969,
               0.9970,
           ]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

       _snake_case     		=				self.get_dummy_components()

       _snake_case     		=				self.pipeline_class(**lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)

       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)

       _snake_case     		=				pipe(**lowerCamelCase	)
       _snake_case     		=				output.images

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)

       _snake_case     		=				pipe(
           **lowerCamelCase						,   return_dict=lowerCamelCase						,   )[0]

       _snake_case     		=				image[0, -3:, -3:, -1]
       _snake_case     		=				image_from_tuple[0, -3:, -3:, -1]

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

       _snake_case     		=				np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

       _snake_case     		=				self.get_dummy_components()

       _snake_case     		=				self.pipeline_class(**lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)

       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)
       _snake_case     		=				[
           pipeline_inputs["image"],
           pipeline_inputs["image"],
       ]

       _snake_case     		=				pipe(**lowerCamelCase	)
       _snake_case     		=				output.images

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)
       _snake_case     		=				[
           tuple_pipeline_inputs["image"],
           tuple_pipeline_inputs["image"],
       ]

       _snake_case     		=				pipe(
           **lowerCamelCase						,   return_dict=lowerCamelCase						,   )[0]

       _snake_case     		=				image[0, -3:, -3:, -1]
       _snake_case     		=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (2, 64, 64, 3)

       _snake_case     		=				np.array(
           [
               0.9997,
               0.9989,
               0.0008,
               0.0021,
               0.9960,
               0.0018,
               0.0014,
               0.0002,
               0.9933,
           ]	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.device("cpu"	)


       class       __SCREAMING_SNAKE_CASE	:



            '''simple docstring'''






            UpperCAmelCase__       :						int      =   1







       _snake_case     		=				self.get_dummy_components()

       _snake_case     		=				self.pipeline_class(**lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)

       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(0	)
       _snake_case     		=				pipe.decoder.dtype
       _snake_case     		=				1

       _snake_case     		=				(
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       _snake_case     		=				pipe.prepare_latents(
           lowerCamelCase						,   dtype=lowerCamelCase						,   device=lowerCamelCase						,   generator=lowerCamelCase						,   latents=lowerCamelCase						,   scheduler=DummyScheduler()	)

       _snake_case     		=				(
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       _snake_case     		=				pipe.prepare_latents(
           lowerCamelCase						,   dtype=lowerCamelCase						,   device=lowerCamelCase						,   generator=lowerCamelCase						,   latents=lowerCamelCase						,   scheduler=DummyScheduler()	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)

       _snake_case     		=				pipe(
           **lowerCamelCase						,   decoder_latents=lowerCamelCase						,   super_res_latents=lowerCamelCase	).images

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase						,   pil_image=lowerCamelCase	)
       # Don't pass image, instead pass embedding
       _snake_case     		=				pipeline_inputs.pop("image"	)
       _snake_case     		=				pipe.image_encoder(lowerCamelCase	).image_embeds

       _snake_case     		=				pipe(
           **lowerCamelCase						,   decoder_latents=lowerCamelCase						,   super_res_latents=lowerCamelCase						,   image_embeddings=lowerCamelCase						,   ).images

       # make sure passing text embeddings manually is identical
       assert np.abs(img_out_a - img_out_a	).max() < 1e-4

     @skip_mps
     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch_device == "cpu"

       # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
       _snake_case     		=				1e-2

       self._test_attention_slicing_forward_pass(
           test_max_difference=lowerCamelCase						,   expected_max_diff=lowerCamelCase	)

     @skip_mps
     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch_device == "cpu"
       _snake_case     		=				True
       _snake_case     		=				[
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]

       self._test_inference_batch_single_identical(
           test_max_difference=lowerCamelCase						,   relax_max_difference=lowerCamelCase						,   additional_params_copy_to_batched_inputs=lowerCamelCase						,   )

     def 	UpperCamelCase(					self	):
       _snake_case     		=				[
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]

       if torch_device == "mps":
         # TODO: MPS errors with larger batch sizes
         _snake_case     		=				[2, 3]
         self._test_inference_batch_consistent(
             batch_sizes=lowerCamelCase						,   additional_params_copy_to_batched_inputs=lowerCamelCase						,   )
       else:
         self._test_inference_batch_consistent(
             additional_params_copy_to_batched_inputs=lowerCamelCase	)

     @skip_mps
     def 	UpperCamelCase(					self	):
       return super().test_dict_tuple_outputs_equivalent()

     @skip_mps
     def 	UpperCamelCase(					self	):
       return super().test_save_load_local()






     @skip_mps
     def 	UpperCamelCase(					self	):
       return super().test_save_load_optional_components()







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



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 	UpperCamelCase(					self	):
       _snake_case     		=				load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"	)
       _snake_case     		=				load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"	)

       _snake_case     		=				UnCLIPImageVariationPipeline.from_pretrained(
           "kakaobrain/karlo-v1-alpha-image-variations"						,   torch_dtype=torch.floataa	)
       _snake_case     		=				pipeline.to(lowerCamelCase	)
       pipeline.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				torch.Generator(device="cpu"	).manual_seed(0	)
       _snake_case     		=				pipeline(
           lowerCamelCase						,   generator=lowerCamelCase						,   output_type="np"						,   )

       _snake_case     		=				output.images[0]

       assert image.shape == (256, 256, 3)

       assert_mean_pixel_difference(lowerCamelCase						,   lowerCamelCase						,   15	)









'''simple docstring'''



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

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

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					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						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       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."	)

       if do_resize and size is None:
         raise ValueError("Size 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."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

       # All transformations expect numpy arrays.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

       if do_rescale:
         _snake_case     		=				[self.rescale(image=lowerCamelCase						,   scale=lowerCamelCase	) for image in images]

       if do_normalize:
         _snake_case     		=				[self.normalize(image=lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase	) for image in images]

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


