code: 
  
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any] 						=					getLogger(__name__)
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any] 						=					4
  SCREAMING_SNAKE_CASE__    :    List[str] 						=					7
  
  SCREAMING_SNAKE_CASE__    :    Any 						=					{
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if isinstance(SCREAMING_SNAKE_CASE					,						torch.Tensor   ):
  						return image
  			elif isinstance(SCREAMING_SNAKE_CASE					,						PIL.Image.Image   ):
  						SCREAMING_SNAKE_CASE_			=	[image]
  
  			if isinstance(image[0]					,						PIL.Image.Image   ):
  						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image[0].size
  						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
  						SCREAMING_SNAKE_CASE_			=	[np.array(i.resize((w, h)   )   )[None, :] for i in image]
  						SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE					,						axis=0   )
  						SCREAMING_SNAKE_CASE_			=	np.array(SCREAMING_SNAKE_CASE   ).astype(np.floataa   ) / 255.0
  						SCREAMING_SNAKE_CASE_			=	image.transpose(0					,						3					,						1					,						2   )
  						SCREAMING_SNAKE_CASE_			=	2.0 * image - 1.0
  						SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE   )
  			elif isinstance(image[0]					,						torch.Tensor   ):
  						SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE					,						dim=0   )
  
  			return image
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 350			,    )     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(
  				    vae=SCREAMING_SNAKE_CASE			,    text_encoder=SCREAMING_SNAKE_CASE			,    tokenizer=SCREAMING_SNAKE_CASE			,    unet=SCREAMING_SNAKE_CASE			,    low_res_scheduler=SCREAMING_SNAKE_CASE			,    scheduler=SCREAMING_SNAKE_CASE			,    safety_checker=SCREAMING_SNAKE_CASE			,    feature_extractor=SCREAMING_SNAKE_CASE			,    watermarker=SCREAMING_SNAKE_CASE			,    max_noise_level=SCREAMING_SNAKE_CASE			,    )
  
  
  
  
  
  	def __call__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 75			,    SCREAMING_SNAKE_CASE = 9.0			,    SCREAMING_SNAKE_CASE = 20			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = 1			,    SCREAMING_SNAKE_CASE = 0.0			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = 1			,    )     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.check_inputs(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				# 2. Define call parameters
  				if prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	1
  				elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	self._execution_device
  				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
  				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
  				# corresponds to doing no classifier free guidance.
  				SCREAMING_SNAKE_CASE_			=	guidance_scale > 1.0
  
  				# 3. Encode input prompt
  				SCREAMING_SNAKE_CASE_			=	self._encode_prompt(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    prompt_embeds=SCREAMING_SNAKE_CASE			,    negative_prompt_embeds=SCREAMING_SNAKE_CASE			,    )
  
  				SCREAMING_SNAKE_CASE_			=	ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]
  
  				# 4. Preprocess image
  				SCREAMING_SNAKE_CASE_			=	preprocess(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	image.cpu()
  
  				# 5. set timesteps
  				self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.scheduler.timesteps
  
  				# 5. Add noise to image
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([noise_level]			,    dtype=torch.long			,    device=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	torch.randn(image.shape			,    generator=SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	2 if do_classifier_free_guidance else 1
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([image] * batch_multiplier * num_images_per_prompt			)
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([noise_level] * image.shape[0]			)
  
  				# 6. Prepare latent variables
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape[2:]
  				SCREAMING_SNAKE_CASE_			=	self.prepare_latents(
  				    batch_size * num_images_per_prompt			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )
  
  				# 7. Check that sizes of image and latents match
  				SCREAMING_SNAKE_CASE_			=	image.shape[1]
  				if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
  							raise ValueError(
  							    'Incorrect configuration settings! The config of `pipeline.unet` expects'
  							    f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
  							    f' `num_channels_image`: {num_channels_image} '
  							    f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
  							    ' `pipeline.unet` or your `image` input.'			)
  
  				# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
  				SCREAMING_SNAKE_CASE_			=	self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	next(
  				    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')			,    'tensor(float)'			)
  				SCREAMING_SNAKE_CASE_			=	ORT_TO_NP_TYPE[timestep_dtype]
  
  				# 9. Denoising loop
  				SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE			) - num_inference_steps * self.scheduler.order
  				with self.progress_bar(total=SCREAMING_SNAKE_CASE			) as progress_bar:
  							for i, t in enumerate(SCREAMING_SNAKE_CASE			):
  										# expand the latents if we are doing classifier free guidance
  										SCREAMING_SNAKE_CASE_			=	np.concatenate([latents] * 2			) if do_classifier_free_guidance else latents
  
  										# concat latents, mask, masked_image_latents in the channel dimension
  										SCREAMING_SNAKE_CASE_			=	self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  										SCREAMING_SNAKE_CASE_			=	np.concatenate([latent_model_input, image]			,    axis=1			)
  
  										# timestep to tensor
  										SCREAMING_SNAKE_CASE_			=	np.array([t]			,    dtype=SCREAMING_SNAKE_CASE			)
  
  										# predict the noise residual
  										SCREAMING_SNAKE_CASE_			=	self.unet(
  										    sample=SCREAMING_SNAKE_CASE			,    timestep=SCREAMING_SNAKE_CASE			,    encoder_hidden_states=SCREAMING_SNAKE_CASE			,    class_labels=noise_level.astype(np.intaa			)			,    )[0]
  
  										# perform guidance
  										if do_classifier_free_guidance:
  													SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	np.split(SCREAMING_SNAKE_CASE			,    2			)
  										SCREAMING_SNAKE_CASE_			=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  										# compute the previous noisy sample x_t -> x_t-1
  										SCREAMING_SNAKE_CASE_			=	self.scheduler.step(
  										    torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			).prev_sample
  
  										# call the callback, if provided
  										if i == len(SCREAMING_SNAKE_CASE			) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
  													progress_bar.update()
  													if callback is not None and i % callback_steps == 0:
  																callback(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
          # 10. Post-processing
  				SCREAMING_SNAKE_CASE_			=	self.decode_latents(latents.float()			)
  
  				# 11. Convert to PIL
  				if output_type == "pil":
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pil(SCREAMING_SNAKE_CASE			)
  
  				if not return_dict:
  							return (image,)
  
  				return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1 / 0.0_8_3_3_3 * latents
  				SCREAMING_SNAKE_CASE_			=	self.vae(latent_sample=SCREAMING_SNAKE_CASE			)[0]
  				SCREAMING_SNAKE_CASE_			=	np.clip(image / 2 + 0.5			,    0			,    1			)
  				SCREAMING_SNAKE_CASE_			=	image.transpose((0, 2, 3, 1)			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	1
  				elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape[0]
  
  				if prompt_embeds is None:
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer(
  							    SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=self.tokenizer.model_max_length			,    truncation=SCREAMING_SNAKE_CASE			,    return_tensors='pt'			,    )
  							SCREAMING_SNAKE_CASE_			=	text_inputs.input_ids
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer(SCREAMING_SNAKE_CASE			,    padding='longest'			,    return_tensors='pt'			).input_ids
  
  							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
  							    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  										SCREAMING_SNAKE_CASE_			=	self.tokenizer.batch_decode(
  										    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
  										logger.warning(
  										    'The following part of your input was truncated because CLIP can only handle sequences up to'
  										    f' {self.tokenizer.model_max_length} tokens: {removed_text}'			)
  
  							# no positional arguments to text_encoder
  							SCREAMING_SNAKE_CASE_			=	self.text_encoder(
  							    input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE			)			,    )
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds[0]
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape
  				# duplicate text embeddings for each generation per prompt, using mps friendly method
  				SCREAMING_SNAKE_CASE_			=	prompt_embeds.repeat(1			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	prompt_embeds.reshape(bs_embed * num_images_per_prompt			,    SCREAMING_SNAKE_CASE			,    -1			)
  
  				# get unconditional embeddings for classifier free guidance
  				if do_classifier_free_guidance and negative_prompt_embeds is None:
  							SCREAMING_SNAKE_CASE_			=	42
  							if negative_prompt is None:
  										SCREAMING_SNAKE_CASE_			=	[''] * batch_size
  							elif type(SCREAMING_SNAKE_CASE			) is not type(SCREAMING_SNAKE_CASE			):
  										raise TypeError(
  										    f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE			)} !='
  										    f' {type(SCREAMING_SNAKE_CASE			)}.'			)
  							elif isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  										SCREAMING_SNAKE_CASE_			=	[negative_prompt]
  							elif batch_size != len(SCREAMING_SNAKE_CASE			):
  										raise ValueError(
  										    f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE			)}, but `prompt`:'
  										    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
  										    ' the batch size of `prompt`.'			)
  							else:
  										SCREAMING_SNAKE_CASE_			=	negative_prompt
  
  							SCREAMING_SNAKE_CASE_			=	text_input_ids.shape[-1]
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer(
  							    SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=SCREAMING_SNAKE_CASE			,    truncation=SCREAMING_SNAKE_CASE			,    return_tensors='pt'			,    )
  
  							# if hasattr(uncond_input, "attention_mask"):
  							#     attention_mask = uncond_input.attention_mask.to(device)
  							# else:
  							#     attention_mask = None
  
  							SCREAMING_SNAKE_CASE_			=	self.text_encoder(
  							    input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE			)			,    )
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings[0]
  
  				if do_classifier_free_guidance:
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings.shape[1]
  							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings.repeat(1			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings.reshape(batch_size * num_images_per_prompt			,    SCREAMING_SNAKE_CASE			,    -1			)
  
  							# For classifier free guidance, we need to do two forward passes.
  							# Here we concatenate the unconditional and text embeddings into a single batch
  							# to avoid doing two forward passes
  							SCREAMING_SNAKE_CASE_			=	np.concatenate([uncond_embeddings, prompt_embeds]			)
  
  				return prompt_embeds
  
style_context: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					logging.get_logger(__name__)
  
  
  if is_vision_available():
  				import PIL
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''pixel_values''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 1 / 255			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				None:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'height': 2560, 'width': 1920}
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# The previous feature extractor size parameter was in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				if (output_width < output_height and input_width > input_height) or (
  				    output_width > output_height and input_width < input_height
  				):
  							SCREAMING_SNAKE_CASE_			=	np.rotaa(SCREAMING_SNAKE_CASE			,    3			)
  
  				if data_format is not None:
  							SCREAMING_SNAKE_CASE_			=	to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	output_width - input_width
  				SCREAMING_SNAKE_CASE_			=	output_height - input_height
  
  				if random_padding:
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_height + 1			)
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_width + 1			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	delta_height // 2
  							SCREAMING_SNAKE_CASE_			=	delta_width // 2
  
  				SCREAMING_SNAKE_CASE_			=	delta_height - pad_top
  				SCREAMING_SNAKE_CASE_			=	delta_width - pad_left
  
  				SCREAMING_SNAKE_CASE_			=	((pad_top, pad_bottom), (pad_left, pad_right))
  				return pad(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				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(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				# We always resize to the smallest of either the input or output size.
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				if height == input_height and width == input_width:
  							return image
  
  				if input_height > input_width:
  							SCREAMING_SNAKE_CASE_			=	int(input_width * height / input_height			)
  				elif input_width > input_height:
  							SCREAMING_SNAKE_CASE_			=	int(input_height * width / input_width			)
  
  				return resize(
  				    SCREAMING_SNAKE_CASE			,    size=(height, width)			,    resample=SCREAMING_SNAKE_CASE			,    reducing_gap=2.0			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(size['height']			,    size['width']			)
  				SCREAMING_SNAKE_CASE_			=	get_resize_output_image_size(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    default_to_square=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resize(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  				return resized_image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return rescale(SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return normalize(SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,    **SCREAMING_SNAKE_CASE			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	do_resize if do_resize is not None else self.do_resize
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else self.size
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# Previous feature extractor had size in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resample if resample is not None else self.resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad if do_pad is not None else self.do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale if do_rescale is not None else self.do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor if rescale_factor is not None else self.rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize if do_normalize is not None else self.do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else self.image_mean
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else self.image_std
  
  				SCREAMING_SNAKE_CASE_			=	make_list_of_images(SCREAMING_SNAKE_CASE			)
  
  				if not valid_images(SCREAMING_SNAKE_CASE			):
  							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.
  				SCREAMING_SNAKE_CASE_			=	[to_numpy_array(SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_align_long_axis:
  							SCREAMING_SNAKE_CASE_			=	[self.align_long_axis(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE_			=	[self.resize(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_thumbnail:
  							SCREAMING_SNAKE_CASE_			=	[self.thumbnail(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_pad:
  							SCREAMING_SNAKE_CASE_			=	[self.pad_image(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    random_padding=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE_			=	[self.rescale(image=SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	[self.normalize(image=SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	{'pixel_values': images}
  				return BatchFeature(data=SCREAMING_SNAKE_CASE			,    tensor_type=SCREAMING_SNAKE_CASE			)
  