




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






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			)
