


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		Optional[int]=0.999      ,  a__:		Optional[Any]="cosine"      ,  )  ->		Tuple:


							'''simple docstring'''





							if alpha_transform_type == "cosine":

														def alpha_bar_fn(a__:		List[str]	):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(a__:		Optional[int]	):
																					return math.exp(t * -12.0	)

							else:
														raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

							_UpperCAmelCase					=      []
							for i in range(a__	):
														_UpperCAmelCase					=      i / num_diffusion_timesteps
														_UpperCAmelCase					=      (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(a__	) / alpha_bar_fn(a__	)      ,  a__	)	)
							return torch.tensor(a__      ,  dtype=torch.floataa	)







class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase      ):
		_a     :  Optional[Any]           =     [e.name for e in KarrasDiffusionSchedulers]
		_a     :  List[Any]           =     2
		@register_to_config
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = 1000      ,   _SCREAMING_SNAKE_CASE = 0.00085      ,   _SCREAMING_SNAKE_CASE = 0.012      ,   _SCREAMING_SNAKE_CASE = "linear"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = "epsilon"      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = 1.0      ,   _SCREAMING_SNAKE_CASE = "linspace"      ,   _SCREAMING_SNAKE_CASE = 0      ,   )			->	Dict:




									"""simple docstring"""





									if trained_betas is not None:
																_UpperCAmelCase					=      torch.tensor(_SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa						)
									elif beta_schedule == "linear":
																_UpperCAmelCase					=      torch.linspace(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa						)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_UpperCAmelCase					=      (
																    torch.linspace(beta_start**0.5      ,   beta_end**0.5      ,   _SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa						) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_UpperCAmelCase					=      betas_for_alpha_bar(_SCREAMING_SNAKE_CASE      ,   alpha_transform_type='cosine'						)
									elif beta_schedule == "exp":
																_UpperCAmelCase					=      betas_for_alpha_bar(_SCREAMING_SNAKE_CASE      ,   alpha_transform_type='exp'						)
									else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

									_UpperCAmelCase					=      1.0 - self.betas
									_UpperCAmelCase					=      torch.cumprod(self.alphas      ,   dim=0						)

									#  set all values
									self.set_timesteps(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      use_karras_sigmas
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Optional[Any]:




									"""simple docstring"""





									if schedule_timesteps is None:
																_UpperCAmelCase					=      self.timesteps

									_UpperCAmelCase					=      (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter						) == 0:
																_UpperCAmelCase					=      1 if len(_SCREAMING_SNAKE_CASE						) > 1 else 0
									else:
																_UpperCAmelCase					=      timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE						) else timestep
																_UpperCAmelCase					=      self._index_counter[timestep_int]

									return indices[pos].item()
		@property
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	torch.FloatTensor:




									"""simple docstring"""





									_UpperCAmelCase					=      self.index_for_timestep(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.sigmas[step_index]
									_UpperCAmelCase					=      sample / ((sigma**2 + 1) ** 0.5)
									return sample
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      num_inference_steps

									_UpperCAmelCase					=      num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																_UpperCAmelCase					=      np.linspace(0      ,   num_train_timesteps - 1      ,   _SCREAMING_SNAKE_CASE      ,   dtype=_SCREAMING_SNAKE_CASE						)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																_UpperCAmelCase					=      num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_UpperCAmelCase					=      (np.arange(0      ,   _SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE						)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																_UpperCAmelCase					=      num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_UpperCAmelCase					=      (np.arange(_SCREAMING_SNAKE_CASE      ,   0      ,   -step_ratio						)).round().copy().astype(_SCREAMING_SNAKE_CASE						)
																timesteps -= 1
									else:
																raise ValueError(
																    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

									_UpperCAmelCase					=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
									_UpperCAmelCase					=      np.log(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      np.interp(_SCREAMING_SNAKE_CASE      ,   np.arange(0      ,   len(_SCREAMING_SNAKE_CASE						)						)      ,   _SCREAMING_SNAKE_CASE						)

									if self.config.use_karras_sigmas:
																_UpperCAmelCase					=      self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE      ,   num_inference_steps=self.num_inference_steps						)
																_UpperCAmelCase					=      np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for sigma in sigmas]						)

									_UpperCAmelCase					=      np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
									_UpperCAmelCase					=      torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(device=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2						), sigmas[-1:]]						)

									_UpperCAmelCase					=      torch.from_numpy(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

									if str(_SCREAMING_SNAKE_CASE						).startswith('mps'						):
																# mps does not support float64
																_UpperCAmelCase					=      timesteps.to(_SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa						)
									else:
																_UpperCAmelCase					=      timesteps.to(device=_SCREAMING_SNAKE_CASE						)

									# empty dt and derivative
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									_UpperCAmelCase					=      defaultdict(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      np.log(_SCREAMING_SNAKE_CASE						)

									# get distribution
									_UpperCAmelCase					=      log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									_UpperCAmelCase					=      np.cumsum((dists >= 0)      ,   axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
									_UpperCAmelCase					=      low_idx + 1

									_UpperCAmelCase					=      log_sigmas[low_idx]
									_UpperCAmelCase					=      log_sigmas[high_idx]

									# interpolate sigmas
									_UpperCAmelCase					=      (low - log_sigma) / (low - high)
									_UpperCAmelCase					=      np.clip(_SCREAMING_SNAKE_CASE      ,   0      ,   1						)

									# transform interpolation to time range
									_UpperCAmelCase					=      (1 - w) * low_idx + w * high_idx
									_UpperCAmelCase					=      t.reshape(sigma.shape						)
									return t
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	torch.FloatTensor:




									"""simple docstring"""





									_UpperCAmelCase					=      in_sigmas[-1].item()
									_UpperCAmelCase					=      in_sigmas[0].item()

									_UpperCAmelCase					=      7.0  # 7.0 is the value used in the paper
									_UpperCAmelCase					=      np.linspace(0      ,   1      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sigma_min ** (1 / rho)
									_UpperCAmelCase					=      sigma_max ** (1 / rho)
									_UpperCAmelCase					=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.dt is None
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = True      ,   )			->	Union[SchedulerOutput, Tuple]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.index_for_timestep(_SCREAMING_SNAKE_CASE						)

									# advance index counter by 1
									_UpperCAmelCase					=      timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE						) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																_UpperCAmelCase					=      self.sigmas[step_index]
																_UpperCAmelCase					=      self.sigmas[step_index + 1]
									else:
																# 2nd order / Heun's method
																_UpperCAmelCase					=      self.sigmas[step_index - 1]
																_UpperCAmelCase					=      self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									_UpperCAmelCase					=      0
									_UpperCAmelCase					=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																_UpperCAmelCase					=      sigma_hat if self.state_in_first_order else sigma_next
																_UpperCAmelCase					=      sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																_UpperCAmelCase					=      sigma_hat if self.state_in_first_order else sigma_next
																_UpperCAmelCase					=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
									elif self.config.prediction_type == "sample":
																_UpperCAmelCase					=      model_output
									else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

									if self.config.clip_sample:
																_UpperCAmelCase					=      pred_original_sample.clamp(
																    -self.config.clip_sample_range      ,   self.config.clip_sample_range						)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																_UpperCAmelCase					=      (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																_UpperCAmelCase					=      sigma_next - sigma_hat

																# store for 2nd order step
																_UpperCAmelCase					=      derivative
																_UpperCAmelCase					=      dt
																_UpperCAmelCase					=      sample
									else:
																# 2. 2nd order / Heun's method
																_UpperCAmelCase					=      (sample - pred_original_sample) / sigma_next
																_UpperCAmelCase					=      (self.prev_derivative + derivative) / 2

																# 3. take prev timestep & sample
																_UpperCAmelCase					=      self.dt
																_UpperCAmelCase					=      self.sample

																# free dt and derivative
																# Note, this puts the scheduler in "first order mode"
																_UpperCAmelCase					=      None
																_UpperCAmelCase					=      None
																_UpperCAmelCase					=      None

									_UpperCAmelCase					=      sample + derivative * dt

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	torch.FloatTensor:




									"""simple docstring"""





									_UpperCAmelCase					=      self.sigmas.to(device=original_samples.device      ,   dtype=original_samples.dtype						)
									if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE						):
																# mps does not support float64
																_UpperCAmelCase					=      self.timesteps.to(original_samples.device      ,   dtype=torch.floataa						)
																_UpperCAmelCase					=      timesteps.to(original_samples.device      ,   dtype=torch.floataa						)
									else:
																_UpperCAmelCase					=      self.timesteps.to(original_samples.device						)
																_UpperCAmelCase					=      timesteps.to(original_samples.device						)

									_UpperCAmelCase					=      [self.index_for_timestep(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for t in timesteps]

									_UpperCAmelCase					=      sigmas[step_indices].flatten()
									while len(sigma.shape						) < len(original_samples.shape						):
																_UpperCAmelCase					=      sigma.unsqueeze(-1						)

									_UpperCAmelCase					=      original_samples + noise * sigma
									return noisy_samples






		def __len__(      self						)			->	Tuple:




									"""simple docstring"""





									return self.config.num_train_timesteps




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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      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      UpperCAmelCase__    (      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 = 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"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_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					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      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 or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

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

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

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

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

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

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

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
