






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   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   UnCLIPImageVariationPipeline
						_SCREAMING_SNAKE_CASE		:    str						=   IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
						_SCREAMING_SNAKE_CASE		:    Tuple						=   IMAGE_VARIATION_BATCH_PARAMS

						_SCREAMING_SNAKE_CASE		:    Tuple						=   [
						    '''generator''',
						    '''return_dict''',
						    '''decoder_num_inference_steps''',
						    '''super_res_num_inference_steps''',
						]
						_SCREAMING_SNAKE_CASE		:    int						=   False






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 1_00






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
												return tokenizer






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  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=10_00       ,  )
												return CLIPTextModelWithProjection(_UpperCamelCase				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  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(_UpperCamelCase				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    'clip_embeddings_dim': self.text_embedder_hidden_size,
												    'time_embed_dim': self.time_embed_dim,
												    'cross_attention_dim': self.cross_attention_dim,
												}

												lowerCAmelCase__        =  UnCLIPTextProjModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    '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',
												}

												lowerCAmelCase__        =  UNetaDConditionModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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				):






												"""simple docstring"""
												torch.manual_seed(0				)

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






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# seeded differently to get different unet than `self.dummy_super_res_first`
												torch.manual_seed(1				)

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_decoder
												lowerCAmelCase__        =  self.dummy_text_proj
												lowerCAmelCase__        =  self.dummy_text_encoder
												lowerCAmelCase__        =  self.dummy_tokenizer
												lowerCAmelCase__        =  self.dummy_super_res_first
												lowerCAmelCase__        =  self.dummy_super_res_last

												lowerCAmelCase__        =  UnCLIPScheduler(
												    variance_type='learned_range'       ,  prediction_type='epsilon'       ,  num_train_timesteps=10_00       ,  )

												lowerCAmelCase__        =  UnCLIPScheduler(
												    variance_type='fixed_small_log'       ,  prediction_type='epsilon'       ,  num_train_timesteps=10_00       ,  )

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

												lowerCAmelCase__        =  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       ,  _UpperCamelCase       ,  _UpperCamelCase=0       ,  _UpperCamelCase=True				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												if str(_UpperCamelCase				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)

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

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(**_UpperCamelCase				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(
												    **_UpperCamelCase       ,  return_dict=_UpperCamelCase       ,  )[0]

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

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

												lowerCAmelCase__        =  np.array(
												    [
												        0.99_97,
												        0.00_02,
												        0.99_97,
												        0.99_97,
												        0.99_69,
												        0.00_23,
												        0.99_97,
												        0.99_69,
												        0.99_70,
												    ]				)

												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				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(**_UpperCamelCase				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(
												    **_UpperCamelCase       ,  return_dict=_UpperCamelCase       ,  )[0]

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

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

												lowerCAmelCase__        =  np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]				)

												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				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)
												lowerCAmelCase__        =  [
												    pipeline_inputs['image'],
												    pipeline_inputs['image'],
												]

												lowerCAmelCase__        =  pipe(**_UpperCamelCase				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)
												lowerCAmelCase__        =  [
												    tuple_pipeline_inputs['image'],
												    tuple_pipeline_inputs['image'],
												]

												lowerCAmelCase__        =  pipe(
												    **_UpperCamelCase       ,  return_dict=_UpperCamelCase       ,  )[0]

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

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

												lowerCAmelCase__        =  np.array(
												    [
												        0.99_97,
												        0.99_89,
												        0.00_08,
												        0.00_21,
												        0.99_60,
												        0.00_18,
												        0.00_14,
												        0.00_02,
												        0.99_33,
												    ]				)

												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				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch.device('cpu'				)


												class       __SCREAMING_SNAKE_CASE   :
																		_SCREAMING_SNAKE_CASE		:    Tuple						=   1




												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
												lowerCAmelCase__        =  pipe.decoder.dtype
												lowerCAmelCase__        =  1

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

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

												lowerCAmelCase__        =  self.get_dummy_inputs(_UpperCamelCase       ,  pil_image=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(
												    **_UpperCamelCase       ,  decoder_latents=_UpperCamelCase       ,  super_res_latents=_UpperCamelCase				).images

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

												lowerCAmelCase__        =  pipe(
												    **_UpperCamelCase       ,  decoder_latents=_UpperCamelCase       ,  super_res_latents=_UpperCamelCase       ,  image_embeddings=_UpperCamelCase       ,  ).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				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch_device == 'cpu'

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

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






						@skip_mps
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch_device == 'cpu'
												lowerCAmelCase__        =  True
												lowerCAmelCase__        =  [
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  [
												    'decoder_num_inference_steps',
												    'super_res_num_inference_steps',
												]

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






						@skip_mps
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return super().test_save_load_local()







						@skip_mps
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return super().test_save_load_optional_components()




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'				)
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'				)

												lowerCAmelCase__        =  UnCLIPImageVariationPipeline.from_pretrained(
												    'kakaobrain/karlo-v1-alpha-image-variations'       ,  torch_dtype=torch.floataa				)
												lowerCAmelCase__        =  pipeline.to(_UpperCamelCase				)
												pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  torch.Generator(device='cpu'				).manual_seed(0				)
												lowerCAmelCase__        =  pipeline(
												    _UpperCamelCase       ,  generator=_UpperCamelCase       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (2_56, 2_56, 3)

												assert_mean_pixel_difference(_UpperCamelCase       ,  _UpperCamelCase       ,  15				)













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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												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 if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCAmelCase__        =  image_std if image_std is not None else IMAGENET_STANDARD_STD






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return rescale(_UpperCamelCase       ,  scale=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												return normalize(_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  do_resize if do_resize is not None else self.do_resize
												lowerCAmelCase__        =  size if size is not None else self.size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												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__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												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__        =  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__        =  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_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.
												lowerCAmelCase__        =  [to_numpy_array(_UpperCamelCase				) for image in images]

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

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

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

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

												lowerCAmelCase__        =  [to_channel_dimension_format(_UpperCamelCase       ,  _UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  {'pixel_values': images}
												return BatchFeature(data=_UpperCamelCase       ,  tensor_type=_UpperCamelCase				)





