



"""simple docstring"""

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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def    __snake_case       (  __A			:	Tuple       ,		__A			:	List[str]=0.9_9_9       ,		__A			:	Optional[Any]="cosine"       ,		)    ->					List[str]:






				'''simple docstring'''
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__A			:	str    ):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__A			:	List[Any]    ):
												return math.exp(t * -1_2.0    )

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

				SCREAMING_SNAKE_CASE	:       int 							=    []
				for i in range(__A    ):
								SCREAMING_SNAKE_CASE	:       int 							=    i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (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 						lowerCAmelCase__	(  _lowerCamelCase       ,       _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   [e.name for e in KarrasDiffusionSchedulers]
	_SCREAMING_SNAKE_CASE  :		int     	=   2





	@register_to_config
	def __init__(			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int = 1_000    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_0_0_8_5    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_1_2    ,					_SCREAMING_SNAKE_CASE							:					str = "linear"    ,					_SCREAMING_SNAKE_CASE							:					Optional[Union[np.ndarray, List[float]]] = None    ,					_SCREAMING_SNAKE_CASE							:					str = "epsilon"    ,					_SCREAMING_SNAKE_CASE							:					str = "linspace"    ,					_SCREAMING_SNAKE_CASE							:					int = 0    ,					)	->  str:





					"""simple docstring"""


					if trained_betas is not None:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.tensor(_SCREAMING_SNAKE_CASE    ,					dtype=torch.floataa						)
					elif beta_schedule == "linear":
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    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.
									SCREAMING_SNAKE_CASE	:       int 							=    (
									    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
									SCREAMING_SNAKE_CASE	:       str 							=    betas_for_alpha_bar(_SCREAMING_SNAKE_CASE						)
					else:
									raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    1.0 - self.betas
					SCREAMING_SNAKE_CASE	:       int 							=    torch.cumprod(self.alphas    ,					dim=0						)

					#  set all values
					self.set_timesteps(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					str=None						)	->  Dict:





					"""simple docstring"""


					if schedule_timesteps is None:
									SCREAMING_SNAKE_CASE	:       Any 							=    self.timesteps

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (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:
									SCREAMING_SNAKE_CASE	:       List[str] 							=    1 if len(_SCREAMING_SNAKE_CASE						) > 1 else 0
					else:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE						) else timestep
									SCREAMING_SNAKE_CASE	:       int 							=    self._index_counter[timestep_int]

					return indices[pos].item()





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  List[Any]:





					"""simple docstring"""


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

					return (self.sigmas.max() ** 2 + 1) ** 0.5





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					Union[float, torch.FloatTensor]    ,					)	->  torch.FloatTensor:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.index_for_timestep(_SCREAMING_SNAKE_CASE						)

					if self.state_in_first_order:
									SCREAMING_SNAKE_CASE	:       int 							=    self.sigmas[step_index]
					else:
									SCREAMING_SNAKE_CASE	:       str 							=    self.sigmas_interpol[step_index - 1]

					SCREAMING_SNAKE_CASE	:       Any 							=    sample / ((sigma**2 + 1) ** 0.5)
					return sample





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Union[str, torch.device] = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None    ,					)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    num_inference_steps

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    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":
									SCREAMING_SNAKE_CASE	:       str 							=    np.linspace(0    ,					num_train_timesteps - 1    ,					_SCREAMING_SNAKE_CASE    ,					dtype=_SCREAMING_SNAKE_CASE						)[::-1].copy()
					elif self.config.timestep_spacing == "leading":
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    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
									SCREAMING_SNAKE_CASE	:       str 							=    (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":
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    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
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    (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'."""						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    np.interp(_SCREAMING_SNAKE_CASE    ,					np.arange(0    ,					len(_SCREAMING_SNAKE_CASE						)						)    ,					_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       str 							=    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
					SCREAMING_SNAKE_CASE	:       str 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(device=_SCREAMING_SNAKE_CASE						)

					# compute up and down sigmas
					SCREAMING_SNAKE_CASE	:       str 							=    sigmas.roll(-1						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    0.0
					SCREAMING_SNAKE_CASE	:       List[str] 							=    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (sigmas_next**2 - sigmas_up**2) ** 0.5
					SCREAMING_SNAKE_CASE	:       List[str] 							=    0.0

					# compute interpolated sigmas
					SCREAMING_SNAKE_CASE	:       Tuple 							=    sigmas.log().lerp(sigmas_down.log()    ,					0.5						).exp()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    0.0

					# set sigmas
					SCREAMING_SNAKE_CASE	:       str 							=    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
					SCREAMING_SNAKE_CASE	:       Any 							=    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

					if str(_SCREAMING_SNAKE_CASE						).startswith('mps'						):
									# mps does not support float64
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE    ,					dtype=torch.floataa						)
					else:
									SCREAMING_SNAKE_CASE	:       int 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.sigma_to_t(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE    ,					dtype=timesteps.dtype						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,					dim=-1						).flatten()

					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.cat([timesteps[:1], interleaved_timesteps]						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					SCREAMING_SNAKE_CASE	:       Any 							=    defaultdict(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    sigma.log()

					# get distribution
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    log_sigma - self.log_sigmas[:, None]

					# get sigmas range
					SCREAMING_SNAKE_CASE	:       str 							=    dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    low_idx + 1

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.log_sigmas[low_idx]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.log_sigmas[high_idx]

					# interpolate sigmas
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    (low - log_sigma) / (low - high)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    w.clamp(0    ,					1						)

					# transform interpolation to time range
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    (1 - w) * low_idx + w * high_idx
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    t.view(sigma.shape						)
					return t





	@property
	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Dict:





					"""simple docstring"""


					return self.sample is None





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					Union[torch.FloatTensor, np.ndarray]    ,					_SCREAMING_SNAKE_CASE							:					Union[float, torch.FloatTensor]    ,					_SCREAMING_SNAKE_CASE							:					Union[torch.FloatTensor, np.ndarray]    ,					_SCREAMING_SNAKE_CASE							:					Optional[torch.Generator] = None    ,					_SCREAMING_SNAKE_CASE							:					bool = True    ,					)	->  Union[SchedulerOutput, Tuple]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.index_for_timestep(_SCREAMING_SNAKE_CASE						)

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

					if self.state_in_first_order:
									SCREAMING_SNAKE_CASE	:       Dict 							=    self.sigmas[step_index]
									SCREAMING_SNAKE_CASE	:       Tuple 							=    self.sigmas_interpol[step_index]
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.sigmas_up[step_index]
									SCREAMING_SNAKE_CASE	:       Any 							=    self.sigmas_down[step_index - 1]
					else:
									# 2nd order / KPDM2's method
									SCREAMING_SNAKE_CASE	:       Dict 							=    self.sigmas[step_index - 1]
									SCREAMING_SNAKE_CASE	:       Dict 							=    self.sigmas_interpol[step_index - 1]
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.sigmas_up[step_index - 1]
									SCREAMING_SNAKE_CASE	:       str 							=    self.sigmas_down[step_index - 1]

					# 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
					SCREAMING_SNAKE_CASE	:       int 							=    0
					SCREAMING_SNAKE_CASE	:       List[str] 							=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					SCREAMING_SNAKE_CASE	:       int 							=    model_output.device
					SCREAMING_SNAKE_CASE	:       List[str] 							=    randn_tensor(model_output.shape    ,					dtype=model_output.dtype    ,					device=_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE						)

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    sigma_hat if self.state_in_first_order else sigma_interpol
									SCREAMING_SNAKE_CASE	:       int 							=    sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    sigma_hat if self.state_in_first_order else sigma_interpol
									SCREAMING_SNAKE_CASE	:       Dict 							=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
					elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample'						)
					else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

					if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									SCREAMING_SNAKE_CASE	:       str 							=    (sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    sigma_interpol - sigma_hat

									# store for 2nd order step
									SCREAMING_SNAKE_CASE	:       Dict 							=    sample
									SCREAMING_SNAKE_CASE	:       List[str] 							=    dt
									SCREAMING_SNAKE_CASE	:       str 							=    sample + derivative * dt
					else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									SCREAMING_SNAKE_CASE	:       Dict 							=    (sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									SCREAMING_SNAKE_CASE	:       str 							=    sigma_down - sigma_hat

									SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.sample
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    None

									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    sample + derivative * dt
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prev_sample + noise * sigma_up

					if not return_dict:
									return (prev_sample,)

					return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					)	->  torch.FloatTensor:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    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
									SCREAMING_SNAKE_CASE	:       Tuple 							=    self.timesteps.to(original_samples.device    ,					dtype=torch.floataa						)
									SCREAMING_SNAKE_CASE	:       int 							=    timesteps.to(original_samples.device    ,					dtype=torch.floataa						)
					else:
									SCREAMING_SNAKE_CASE	:       Dict 							=    self.timesteps.to(original_samples.device						)
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    timesteps.to(original_samples.device						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.index_for_timestep(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) for t in timesteps]

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

					SCREAMING_SNAKE_CASE	:       Tuple 							=    original_samples + noise * sigma
					return noisy_samples







	def __len__(			self							:					str						)	->  str:





					"""simple docstring"""


					return self.config.num_train_timesteps










"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=7    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=3    ,					_SCREAMING_SNAKE_CASE							:					List[str]=18    ,					_SCREAMING_SNAKE_CASE							:					str=30    ,					_SCREAMING_SNAKE_CASE							:					str=400    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Dict=0.8_7_5    ,					_SCREAMING_SNAKE_CASE							:					List[str]=True    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=[0.5, 0.5, 0.5]    ,					_SCREAMING_SNAKE_CASE							:					str=[0.5, 0.5, 0.5]    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    size if size is not None else {'shortest_edge': 20}
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    parent
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Any 							=    num_channels
					SCREAMING_SNAKE_CASE	:       Any 							=    image_size
					SCREAMING_SNAKE_CASE	:       str 							=    min_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    max_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    crop_pct
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       Dict 							=    image_mean
					SCREAMING_SNAKE_CASE	:       str 							=    image_std







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""simple docstring"""


					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_pct": self.crop_pct,
					}




@require_torch
@require_vision
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   ConvNextImageProcessor if is_vision_available() else None





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ConvNextImageProcessingTester(self						)





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_resize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'size'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'crop_pct'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_normalize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_mean'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_std'						)						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 20}						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 42}						)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					pass





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					Image.Image						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					numpify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       int 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					torchify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Any 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





