





import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class   _UpperCAmelCase	(   nn.Module					):




					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase=1.0       )						->  Tuple:
							super().__init__()

							UpperCamelCase__         =	cut_size
							UpperCamelCase__         =	cut_power




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Optional[int]:
							UpperCamelCase__  ,			UpperCamelCase__         =	pixel_values.shape[2:4]
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase    ,      self.cut_size       )
							UpperCamelCase__         =	[]
							for _ in range(__lowerCAmelCase       ):
									UpperCamelCase__         =	int(torch.rand([]       ) ** self.cut_power * (max_size - min_size) + min_size       )
									UpperCamelCase__         =	torch.randint(0    ,      sideX - size + 1    ,      ()       )
									UpperCamelCase__         =	torch.randint(0    ,      sideY - size + 1    ,      ()       )
									UpperCamelCase__         =	pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(__lowerCAmelCase    ,      self.cut_size       )       )
							return torch.cat(__lowerCAmelCase       )



def     a						(      _lowercase   :     Tuple ,						_lowercase   :     int       )     ->Tuple:
		UpperCamelCase__         =	F.normalize(_lowercase ,						dim=-1       )
		UpperCamelCase__         =	F.normalize(_lowercase ,						dim=-1       )
		return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )

def     a						(      _lowercase   :     List[Any] ,						_lowercase   :     str       )     ->Any:
		for param in model.parameters():
				UpperCamelCase__         =	value

class   _UpperCAmelCase	(   _lowercase					):




					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      )						->  Optional[Any]:
							super().__init__()
							self.register_modules(
							    vae=__lowerCAmelCase    ,      text_encoder=__lowerCAmelCase    ,      clip_model=__lowerCAmelCase    ,      tokenizer=__lowerCAmelCase    ,      unet=__lowerCAmelCase    ,      scheduler=__lowerCAmelCase    ,      feature_extractor=__lowerCAmelCase    ,      )

							UpperCamelCase__         =	transforms.Normalize(mean=feature_extractor.image_mean    ,      std=feature_extractor.image_std       )
							UpperCamelCase__         =	(
							    feature_extractor.size
							    if isinstance(feature_extractor.size    ,      __lowerCAmelCase       )
							    else feature_extractor.size["""shortest_edge"""]
							)
							UpperCamelCase__         =	MakeCutouts(self.cut_out_size       )

							set_requires_grad(self.text_encoder    ,      __lowerCAmelCase       )
							set_requires_grad(self.clip_model    ,      __lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase = "auto"       )						->  Optional[int]:
							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									UpperCamelCase__         =	self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  str:
							self.enable_attention_slicing(__lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  Any:
							set_requires_grad(self.vae    ,      __lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  Optional[int]:
							set_requires_grad(self.vae    ,      __lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  Union[str, Any]:
							set_requires_grad(self.unet    ,      __lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  List[str]:
							set_requires_grad(self.unet    ,      __lowerCAmelCase       )




					@torch.enable_grad()
					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=True    ,      )						->  List[str]:
							UpperCamelCase__         =	latents.detach().requires_grad_()

							UpperCamelCase__         =	self.scheduler.scale_model_input(__lowerCAmelCase    ,      __lowerCAmelCase       )

							# predict the noise residual
							UpperCamelCase__         =	self.unet(__lowerCAmelCase    ,      __lowerCAmelCase    ,      encoder_hidden_states=__lowerCAmelCase       ).sample

							if isinstance(self.scheduler    ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)       ):
									UpperCamelCase__         =	self.scheduler.alphas_cumprod[timestep]
									UpperCamelCase__         =	1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									UpperCamelCase__         =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									UpperCamelCase__         =	torch.sqrt(__lowerCAmelCase       )
									UpperCamelCase__         =	pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler    ,      __lowerCAmelCase       ):
									UpperCamelCase__         =	self.scheduler.sigmas[index]
									UpperCamelCase__         =	latents - sigma * noise_pred
							else:
									raise ValueError(F"""scheduler type {type(self.scheduler       )} not supported"""       )

							UpperCamelCase__         =	1 / self.vae.config.scaling_factor * sample
							UpperCamelCase__         =	self.vae.decode(__lowerCAmelCase       ).sample
							UpperCamelCase__         =	(image / 2 + 0.5).clamp(0    ,      1       )

							if use_cutouts:
									UpperCamelCase__         =	self.make_cutouts(__lowerCAmelCase    ,      __lowerCAmelCase       )
							else:
									UpperCamelCase__         =	transforms.Resize(self.cut_out_size       )(__lowerCAmelCase       )
							UpperCamelCase__         =	self.normalize(__lowerCAmelCase       ).to(latents.dtype       )

							UpperCamelCase__         =	self.clip_model.get_image_features(__lowerCAmelCase       )
							UpperCamelCase__         =	image_embeddings_clip / image_embeddings_clip.norm(p=2    ,      dim=-1    ,      keepdim=__lowerCAmelCase       )

							if use_cutouts:
									UpperCamelCase__         =	spherical_dist_loss(__lowerCAmelCase    ,      __lowerCAmelCase       )
									UpperCamelCase__         =	dists.view([num_cutouts, sample.shape[0], -1]       )
									UpperCamelCase__         =	dists.sum(2       ).mean(0       ).sum() * clip_guidance_scale
							else:
									UpperCamelCase__         =	spherical_dist_loss(__lowerCAmelCase    ,      __lowerCAmelCase       ).mean() * clip_guidance_scale

							UpperCamelCase__         =	-torch.autograd.grad(__lowerCAmelCase    ,      __lowerCAmelCase       )[0]

							if isinstance(self.scheduler    ,      __lowerCAmelCase       ):
									UpperCamelCase__         =	latents.detach() + grads * (sigma**2)
									UpperCamelCase__         =	noise_pred_original
							else:
									UpperCamelCase__         =	noise_pred_original - torch.sqrt(__lowerCAmelCase       ) * grads
							return noise_pred, latents




					@torch.no_grad()
					def __call__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 512    ,      __lowerCAmelCase = 512    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = 0.0    ,      __lowerCAmelCase = 100    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = 4    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = "pil"    ,      __lowerCAmelCase = True    ,      )						->  List[str]:
							if isinstance(__lowerCAmelCase    ,      __lowerCAmelCase       ):
									UpperCamelCase__         =	1
							elif isinstance(__lowerCAmelCase    ,      __lowerCAmelCase       ):
									UpperCamelCase__         =	len(__lowerCAmelCase       )
							else:
									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase       )}"""       )

							if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""       )

							# get prompt text embeddings
							UpperCamelCase__         =	self.tokenizer(
							    __lowerCAmelCase    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__lowerCAmelCase    ,      return_tensors="""pt"""    ,      )
							UpperCamelCase__         =	self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
							# duplicate text embeddings for each generation per prompt
							UpperCamelCase__         =	text_embeddings.repeat_interleave(__lowerCAmelCase    ,      dim=0       )

							if clip_guidance_scale > 0:
									if clip_prompt is not None:
											UpperCamelCase__         =	self.tokenizer(
											    __lowerCAmelCase    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__lowerCAmelCase    ,      return_tensors="""pt"""    ,      ).input_ids.to(self.device       )
									else:
											UpperCamelCase__         =	text_input.input_ids.to(self.device       )
									UpperCamelCase__         =	self.clip_model.get_text_features(__lowerCAmelCase       )
									UpperCamelCase__         =	text_embeddings_clip / text_embeddings_clip.norm(p=2    ,      dim=-1    ,      keepdim=__lowerCAmelCase       )
									# duplicate text embeddings clip for each generation per prompt
									UpperCamelCase__         =	text_embeddings_clip.repeat_interleave(__lowerCAmelCase    ,      dim=0       )

							# 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.
							UpperCamelCase__         =	guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									UpperCamelCase__         =	text_input.input_ids.shape[-1]
									UpperCamelCase__         =	self.tokenizer([""""""]    ,      padding="""max_length"""    ,      max_length=__lowerCAmelCase    ,      return_tensors="""pt"""       )
									UpperCamelCase__         =	self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
									# duplicate unconditional embeddings for each generation per prompt
									UpperCamelCase__         =	uncond_embeddings.repeat_interleave(__lowerCAmelCase    ,      dim=0       )

									# 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
									UpperCamelCase__         =	torch.cat([uncond_embeddings, text_embeddings]       )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							UpperCamelCase__         =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							UpperCamelCase__         =	text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not work reproducibly on mps
											UpperCamelCase__         =	torch.randn(__lowerCAmelCase    ,      generator=__lowerCAmelCase    ,      device="""cpu"""    ,      dtype=__lowerCAmelCase       ).to(
											    self.device       )
									else:
											UpperCamelCase__         =	torch.randn(__lowerCAmelCase    ,      generator=__lowerCAmelCase    ,      device=self.device    ,      dtype=__lowerCAmelCase       )
							else:
									if latents.shape != latents_shape:
											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
									UpperCamelCase__         =	latents.to(self.device       )

							# set timesteps
							UpperCamelCase__         =	"""offset""" in set(inspect.signature(self.scheduler.set_timesteps       ).parameters.keys()       )
							UpperCamelCase__         =	{}
							if accepts_offset:
									UpperCamelCase__         =	1

							self.scheduler.set_timesteps(__lowerCAmelCase    ,      **__lowerCAmelCase       )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							UpperCamelCase__         =	self.scheduler.timesteps.to(self.device       )

							# scale the initial noise by the standard deviation required by the scheduler
							UpperCamelCase__         =	latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							UpperCamelCase__         =	"""eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
							UpperCamelCase__         =	{}
							if accepts_eta:
									UpperCamelCase__         =	eta

							# check if the scheduler accepts generator
							UpperCamelCase__         =	"""generator""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
							if accepts_generator:
									UpperCamelCase__         =	generator

							for i, t in enumerate(self.progress_bar(__lowerCAmelCase       )       ):
									# expand the latents if we are doing classifier free guidance
									UpperCamelCase__         =	torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
									UpperCamelCase__         =	self.scheduler.scale_model_input(__lowerCAmelCase    ,      __lowerCAmelCase       )

									# predict the noise residual
									UpperCamelCase__         =	self.unet(__lowerCAmelCase    ,      __lowerCAmelCase    ,      encoder_hidden_states=__lowerCAmelCase       ).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
											UpperCamelCase__  ,			UpperCamelCase__         =	noise_pred.chunk(2       )
											UpperCamelCase__         =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
											UpperCamelCase__         =	(
											    text_embeddings.chunk(2       )[1] if do_classifier_free_guidance else text_embeddings
											)
											UpperCamelCase__  ,			UpperCamelCase__         =	self.cond_fn(
											    __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      )

									# compute the previous noisy sample x_t -> x_t-1
									UpperCamelCase__         =	self.scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      **__lowerCAmelCase       ).prev_sample

							# scale and decode the image latents with vae
							UpperCamelCase__         =	1 / self.vae.config.scaling_factor * latents
							UpperCamelCase__         =	self.vae.decode(__lowerCAmelCase       ).sample

							UpperCamelCase__         =	(image / 2 + 0.5).clamp(0    ,      1       )
							UpperCamelCase__         =	image.cpu().permute(0    ,      2    ,      3    ,      1       ).numpy()

							if output_type == "pil":
									UpperCamelCase__         =	self.numpy_to_pil(__lowerCAmelCase       )

							if not return_dict:
									return (image, None)

							return StableDiffusionPipelineOutput(images=__lowerCAmelCase    ,      nsfw_content_detected=__lowerCAmelCase       )












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


__lowerCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   ["pixel_values"]




					def __init__(						self    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = PILImageResampling.BILINEAR    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = 1 / 255    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  None:
							super().__init__(**__lowerCAmelCase       )

							UpperCamelCase__         =	size if size is not None else {"""height""": 2_560, """width""": 1_920}
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# The previous feature extractor size parameter was in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )

							UpperCamelCase__         =	do_resize
							UpperCamelCase__         =	size
							UpperCamelCase__         =	resample
							UpperCamelCase__         =	do_thumbnail
							UpperCamelCase__         =	do_align_long_axis
							UpperCamelCase__         =	do_pad
							UpperCamelCase__         =	do_rescale
							UpperCamelCase__         =	rescale_factor
							UpperCamelCase__         =	do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__         =	image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None       )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

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

							if data_format is not None:
									UpperCamelCase__         =	to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       )

							return image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )

							UpperCamelCase__         =	output_width - input_width
							UpperCamelCase__         =	output_height - input_height

							if random_padding:
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_height + 1       )
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_width + 1       )
							else:
									UpperCamelCase__         =	delta_height // 2
									UpperCamelCase__         =	delta_width // 2

							UpperCamelCase__         =	delta_height - pad_top
							UpperCamelCase__         =	delta_width - pad_left

							UpperCamelCase__         =	((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowerCAmelCase    ,      __lowerCAmelCase    ,      data_format=__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      *__lowerCAmelCase    ,      **__lowerCAmelCase       )						->  List[Any]:
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
							return self.pad_image(*__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )

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

							if input_height > input_width:
									UpperCamelCase__         =	int(input_width * height / input_height       )
							elif input_width > input_height:
									UpperCamelCase__         =	int(input_height * width / input_width       )

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




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	min(size["""height"""]    ,      size["""width"""]       )
							UpperCamelCase__         =	get_resize_output_image_size(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      default_to_square=__lowerCAmelCase       )
							UpperCamelCase__         =	resize(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )
							return resized_image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  List[str]:
							return rescale(__lowerCAmelCase    ,      scale=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							return normalize(__lowerCAmelCase    ,      mean=__lowerCAmelCase    ,      std=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = ChannelDimension.FIRST    ,      **__lowerCAmelCase    ,      )						->  PIL.Image.Image:
							UpperCamelCase__         =	do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__         =	size if size is not None else self.size
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# Previous feature extractor had size in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	resample if resample is not None else self.resample
							UpperCamelCase__         =	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase__         =	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase__         =	do_pad if do_pad is not None else self.do_pad
							UpperCamelCase__         =	do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__         =	rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__         =	do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__         =	image_std if image_std is not None else self.image_std

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

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

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

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

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

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

							if do_thumbnail:
									UpperCamelCase__         =	[self.thumbnail(image=__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_pad:
									UpperCamelCase__         =	[self.pad_image(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      random_padding=__lowerCAmelCase       ) for image in images]

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

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

							UpperCamelCase__         =	[to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	{"""pixel_values""": images}
							return BatchFeature(data=__lowerCAmelCase    ,      tensor_type=__lowerCAmelCase       )





