
"""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 ViTImageProcessor







class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : Any					,A_       : int=7					,A_       : Tuple=3					,A_       : Optional[Any]=18					,A_       : List[str]=30					,A_       : Union[str, Any]=400					,A_       : Any=True					,A_       : List[str]=None					,A_       : Any=True					,A_       : List[str]=[0.5, 0.5, 0.5]					,A_       : Dict=[0.5, 0.5, 0.5]					,)       ->				Union[str, Any]:
							A  				=				size if size is not None else {'height': 18, 'width': 18}
							A  				=				parent
							A  				=				batch_size
							A  				=				num_channels
							A  				=				image_size
							A  				=				min_resolution
							A  				=				max_resolution
							A  				=				do_resize
							A  				=				size
							A  				=				do_normalize
							A  				=				image_mean
							A  				=				image_std




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				List[str]:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}







@require_torch
@require_vision
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      ViTImageProcessor if is_vision_available() else None


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[str]:
							A  				=				ViTImageProcessingTester(self )


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				int:
							return self.image_processor_tester.prepare_image_processor_dict()


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Any:
							A  				=				self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(A_					,'image_mean' ) )
							self.assertTrue(hasattr(A_					,'image_std' ) )
							self.assertTrue(hasattr(A_					,'do_normalize' ) )
							self.assertTrue(hasattr(A_					,'do_resize' ) )
							self.assertTrue(hasattr(A_					,'size' ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size					,{'height': 18, 'width': 18} )

							A  				=				self.image_processing_class.from_dict(self.image_processor_dict					,size=42 )
							self.assertEqual(image_processor.size					,{'height': 42, 'width': 42} )


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				int:
							pass


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Optional[Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							A  				=				prepare_image_inputs(self.image_processor_tester					,equal_resolution=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_					,Image.Image )

							# Test not batched input
							A  				=				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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)

							# Test batched
							A  				=				image_processing(A_					,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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Union[str, Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							A  				=				prepare_image_inputs(self.image_processor_tester					,equal_resolution=A_					,numpify=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_					,np.ndarray )

							# Test not batched input
							A  				=				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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)

							# Test batched
							A  				=				image_processing(A_					,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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)




						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							A  				=				prepare_image_inputs(self.image_processor_tester					,equal_resolution=A_					,torchify=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_					,torch.Tensor )

							# Test not batched input
							A  				=				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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)

							# Test batched
							A  				=				image_processing(A_					,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['height'],
							        self.image_processor_tester.size['width'],
							    )					,)

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_lowercase											=			logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

							A  				=				np.linspace(0					,1					,A_ )
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							# setable values
							A  				=				None
							A  				=				torch.from_numpy(A_ )
							A  				=				torch.from_numpy(A_ )
							A  				=				False
							A  				=				False


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

							A  				=				(schedule_timesteps == timestep).nonzero()
							return indices.item()


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

							A  				=				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							if num_inference_steps is None and timesteps is None:
								raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.' )

							if num_inference_steps is not None and timesteps is not None:
								raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.' )

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
								for i in range(1					,len(A_ ) ):
									if timesteps[i] >= timesteps[i - 1]:
										raise ValueError('`timesteps` must be in descending order.' )

								if timesteps[0] >= self.config.num_train_timesteps:
									raise ValueError(
									    F'`timesteps` must start before `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps}.' )

								A  				=				np.array(A_					,dtype=np.intaa )
								A  				=				True
							else:
								if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
									    F' maximal {self.config.num_train_timesteps} timesteps.' )

								A  				=				num_inference_steps

								A  				=				self.config.num_train_timesteps // self.num_inference_steps
								A  				=				(np.arange(0					,A_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								A  				=				False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							A  				=				self.config.num_train_timesteps
							A  				=				timesteps[::-1].copy()
							A  				=				ramp / (num_train_timesteps - 1)
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							A  				=				np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
							A  				=				torch.from_numpy(A_ ).to(device=A_ )

							if str(A_ ).startswith('mps' ):
								# mps does not support float64
								A  				=				torch.from_numpy(A_ ).to(A_					,dtype=torch.floataa )
							else:
								A  				=				torch.from_numpy(A_ ).to(device=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Any:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.config.rho
							A  				=				sigma_min ** (1 / rho)
							A  				=				sigma_max ** (1 / rho)
							A  				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / (sigma**2 + sigma_data**2)
							A  				=				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							A  				=				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,torch.LongTensor )
							):
								raise ValueError(
								    (
								        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
								        F' `{self.__class__}.step()` is not supported. Make sure to pass'
								        ' one of the `scheduler.timesteps` as a timestep.'
								    )					,)

							if not self.is_scale_input_called:
								logger.warning(
								    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
								    'See `StableDiffusionPipeline` for a usage example.' )

							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.index_for_timestep(A_ )

							# sigma_next corresponds to next_t in original implementation
							A  				=				self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
								A  				=				self.sigmas[step_index + 1]
							else:
								# Set sigma_next to sigma_min
								A  				=				self.sigmas[-1]

							# Get scalings for boundary conditions
							A				, A  				=				self.get_scalings_for_boundary_condition(A_ )

							# 1. Denoise model output using boundary conditions
							A  				=				c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
								A  				=				denoised.clamp(-1					,1 )

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps ) > 1:
								A  				=				randn_tensor(
								    model_output.shape					,dtype=model_output.dtype					,device=model_output.device					,generator=A_ )
							else:
								A  				=				torch.zeros_like(A_ )
							A  				=				noise * self.config.s_noise

							A  				=				sigma_next.clamp(min=A_					,max=A_ )

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							A  				=				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
								return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,)       ->				torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							A  				=				self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
								# mps does not support float64
								A  				=				self.timesteps.to(original_samples.device					,dtype=torch.floataa )
								A  				=				timesteps.to(original_samples.device					,dtype=torch.floataa )
							else:
								A  				=				self.timesteps.to(original_samples.device )
								A  				=				timesteps.to(original_samples.device )

							A  				=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

							A  				=				original_samples + noise * sigma
							return noisy_samples




						def __len__(	self       : List[str] )       ->				List[Any]:
							return self.config.num_train_timesteps