



"""simple docstring"""







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase__		:    Union[str, Any]				   =     8

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Union[str, Any]=BITS						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			x.device

  lowerCAmelCase_       :    Union[str, Any]								=			(x * 255).int().clamp(0   ,       255						)

  lowerCAmelCase_       :    Optional[Any]								=			2 ** torch.arange(bits - 1   ,       -1   ,       -1   ,       device=lowerCamelCase__						)
  lowerCAmelCase_       :    Any								=			rearrange(lowerCamelCase__   ,       'd -> d 1 1'						)
  lowerCAmelCase_       :    Union[str, Any]								=			rearrange(lowerCamelCase__   ,       'b c h w -> b c 1 h w'						)

  lowerCAmelCase_       :    Tuple								=			((x & mask) != 0).float()
  lowerCAmelCase_       :    Union[str, Any]								=			rearrange(lowerCamelCase__   ,       'b c d h w -> b (c d) h w'						)
  lowerCAmelCase_       :    str								=			bits * 2 - 1
  return bits

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=BITS						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			x.device

  lowerCAmelCase_       :    Optional[int]								=			(x > 0).int()
  lowerCAmelCase_       :    Dict								=			2 ** torch.arange(bits - 1   ,       -1   ,       -1   ,       device=lowerCamelCase__   ,       dtype=torch.intaa						)

  lowerCAmelCase_       :    Tuple								=			rearrange(lowerCamelCase__   ,       'd -> d 1 1'						)
  lowerCAmelCase_       :    List[str]								=			rearrange(lowerCamelCase__   ,       'b (c d) h w -> b c d h w'   ,       d=8						)
  lowerCAmelCase_       :    Tuple								=			reduce(x * mask   ,       'b c d h w -> b c h w'   ,       'sum'						)
  return (dec / 255).clamp(0.0   ,       1.0						)

def     UpperCamelCase_   (   self				:    Dict   ,       lowerCAmelCase__				:    torch.FloatTensor   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    torch.FloatTensor   ,       lowerCAmelCase__				:    float = 0.0   ,       lowerCAmelCase__				:    bool = True   ,       lowerCAmelCase__				:    List[Any]=None   ,       lowerCAmelCase__				:    bool = True   ,       )						->       Union[DDIMSchedulerOutput, Tuple]:

  """simple docstring"""




  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  lowerCAmelCase_       :    Optional[int]								=			timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  lowerCAmelCase_       :    Dict								=			self.alphas_cumprod[timestep]
  lowerCAmelCase_       :    Any								=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  lowerCAmelCase_       :    List[Any]								=			1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCAmelCase_       :    Union[str, Any]								=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  lowerCAmelCase_       :    str								=			self.bit_scale
  if self.config.clip_sample:
    lowerCAmelCase_       :    List[str]								=			torch.clamp(lowerCamelCase__   ,       -scale   ,       lowerCamelCase__						)

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  lowerCAmelCase_       :    Dict								=			self._get_variance(lowerCamelCase__   ,       lowerCamelCase__						)
  lowerCAmelCase_       :    Any								=			eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    lowerCAmelCase_       :    str								=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCAmelCase_       :    Optional[int]								=			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCAmelCase_       :    Any								=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    lowerCAmelCase_       :    Optional[int]								=			model_output.device if torch.is_tensor(lowerCamelCase__						) else '''cpu'''
    lowerCAmelCase_       :    Tuple								=			torch.randn(model_output.shape   ,       dtype=model_output.dtype   ,       generator=lowerCamelCase__						).to(lowerCamelCase__						)
    lowerCAmelCase_       :    Any								=			self._get_variance(lowerCamelCase__   ,       lowerCamelCase__						) ** 0.5 * eta * noise

    lowerCAmelCase_       :    Optional[int]								=			prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=lowerCamelCase__   ,       pred_original_sample=lowerCamelCase__						)

def     UpperCamelCase_   (   self				:    List[str]   ,       lowerCAmelCase__				:    torch.FloatTensor   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    torch.FloatTensor   ,       lowerCAmelCase__				:    Optional[Any]="epsilon"   ,       lowerCAmelCase__				:    Optional[Any]=None   ,       lowerCAmelCase__				:    bool = True   ,       )						->       Union[DDPMSchedulerOutput, Tuple]:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    lowerCAmelCase_       :    List[str]								=			torch.split(lowerCamelCase__   ,       sample.shape[1]   ,       dim=1						)
  else:
    lowerCAmelCase_       :    int								=			None

  # 1. compute alphas, betas
  lowerCAmelCase_       :    List[str]								=			self.alphas_cumprod[t]
  lowerCAmelCase_       :    Any								=			self.alphas_cumprod[t - 1] if t > 0 else self.one
  lowerCAmelCase_       :    Optional[Any]								=			1 - alpha_prod_t
  lowerCAmelCase_       :    Dict								=			1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    lowerCAmelCase_       :    List[Any]								=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    lowerCAmelCase_       :    List[str]								=			model_output
  else:
    raise ValueError(f"Unsupported prediction_type {prediction_type}."						)

  # 3. Clip "predicted x_0"
  lowerCAmelCase_       :    Optional[int]								=			self.bit_scale
  if self.config.clip_sample:
    lowerCAmelCase_       :    Any								=			torch.clamp(lowerCamelCase__   ,       -scale   ,       lowerCamelCase__						)

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  lowerCAmelCase_       :    List[Any]								=			(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  lowerCAmelCase_       :    int								=			self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  lowerCAmelCase_       :    int								=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  lowerCAmelCase_       :    List[str]								=			0
  if t > 0:
    lowerCAmelCase_       :    List[Any]								=			torch.randn(
        model_output.size()   ,       dtype=model_output.dtype   ,       layout=model_output.layout   ,       generator=lowerCamelCase__						).to(model_output.device						)
    lowerCAmelCase_       :    Any								=			(self._get_variance(lowerCamelCase__   ,       predicted_variance=lowerCamelCase__						) ** 0.5) * noise

  lowerCAmelCase_       :    str								=			pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=lowerCamelCase__   ,       pred_original_sample=lowerCamelCase__						)







class 				UpperCamelCase__       (     a__  ):




      """simple docstring"""




      def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				UNetaDConditionModel   ,      SCREAMING_SNAKE_CASE_    :				Union[DDIMScheduler, DDPMScheduler]   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = 1.0   ,      ):
        super().__init__()
        lowerCAmelCase_       :    Any								=			bit_scale
        lowerCAmelCase_       :    Optional[Any]								=			(
            ddim_bit_scheduler_step if isinstance(_lowerCamelCase   ,      _lowerCamelCase	) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=_lowerCamelCase   ,      scheduler=_lowerCamelCase	)





      @torch.no_grad()
      def __call__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = 2_5_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = 2_5_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = 5_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[torch.Generator] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = 1   ,      SCREAMING_SNAKE_CASE_    :				Optional[str] = "pil"   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)   ,      generator=_lowerCamelCase   ,      )
        lowerCAmelCase_       :    str								=			decimal_to_bits(_lowerCamelCase	) * self.bit_scale
        lowerCAmelCase_       :    int								=			latents.to(self.device	)

        self.scheduler.set_timesteps(_lowerCamelCase	)

        for t in self.progress_bar(self.scheduler.timesteps	):
          # predict the noise residual
          lowerCAmelCase_       :    Optional[int]								=			self.unet(_lowerCamelCase   ,      _lowerCamelCase	).sample

          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase_       :    Tuple								=			self.scheduler.step(_lowerCamelCase   ,      _lowerCamelCase   ,      _lowerCamelCase	).prev_sample

        lowerCAmelCase_       :    Dict								=			bits_to_decimal(_lowerCamelCase	)

        if output_type == "pil":
          lowerCAmelCase_       :    Dict								=			self.numpy_to_pil(_lowerCamelCase	)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=_lowerCamelCase	)





"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:    Tuple				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""pixel_values"""]



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Dict[str, int]] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BICUBIC   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Union[int, float] = 1 / 2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_       :    List[str]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      default_to_square=SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'	)

        lowerCAmelCase_       :    List[Any]								=			do_resize
        lowerCAmelCase_       :    Any								=			do_rescale
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    List[str]								=			do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size
        lowerCAmelCase_       :    Optional[Any]								=			size
        lowerCAmelCase_       :    Tuple								=			resample
        lowerCAmelCase_       :    Optional[int]								=			rescale_factor
        lowerCAmelCase_       :    Optional[Any]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = PILImageResampling.BILINEAR   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if "shortest_edge" in size:
          lowerCAmelCase_       :    Any								=			get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,      size=size['shortest_edge']   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          lowerCAmelCase_       :    Union[str, Any]								=			(size['height'], size['width'])
        else:
          raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"	)
        return resize(SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        lowerCAmelCase_       :    Any								=			get_size_dict(SCREAMING_SNAKE_CASE_	)
        if "height" not in size or "width" not in size:
          raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(SCREAMING_SNAKE_CASE_   ,      size=(size['height'], size['width'])   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				float   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return rescale(SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				np.ndarray   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Union[float, List[float]]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, ChannelDimension]] = None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        return normalize(SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_   ,      data_format=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				ImageInput   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict[str, int] = None   ,      SCREAMING_SNAKE_CASE_    :				PILImageResampling = None   ,      SCREAMING_SNAKE_CASE_    :				bool = None   ,      SCREAMING_SNAKE_CASE_    :				int = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[float] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[float, List[float]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_       :    Optional[int]								=			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_       :    Optional[Any]								=			do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_       :    Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_       :    Dict								=			crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_       :    Optional[Any]								=			get_size_dict(SCREAMING_SNAKE_CASE_   ,      param_name='crop_size'   ,      default_to_square=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			resample if resample is not None else self.resample
        lowerCAmelCase_       :    Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_       :    str								=			image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_       :    List[str]								=			image_std if image_std is not None else self.image_std

        lowerCAmelCase_       :    Tuple								=			size if size is not None else self.size
        lowerCAmelCase_       :    str								=			get_size_dict(SCREAMING_SNAKE_CASE_	)

        if not is_batched(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[Any]								=			[images]

        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_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.'	)

        # All transformations expect numpy arrays.
        lowerCAmelCase_       :    str								=			[to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

        if do_resize:
          lowerCAmelCase_       :    Any								=			[self.resize(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_   ,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_center_crop:
          lowerCAmelCase_       :    Optional[int]								=			[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,      size=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_rescale:
          lowerCAmelCase_       :    Tuple								=			[self.rescale(image=SCREAMING_SNAKE_CASE_   ,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

        if do_normalize:
          lowerCAmelCase_       :    str								=			[self.normalize(image=SCREAMING_SNAKE_CASE_   ,      mean=SCREAMING_SNAKE_CASE_   ,      std=SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Any								=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) for image in images]

        lowerCAmelCase_       :    Optional[Any]								=			{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,      tensor_type=SCREAMING_SNAKE_CASE_	)





