

"""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 ViltImageProcessor
class 	_lowerCAmelCase    (      unittest.TestCase       ):

							def __init__( self       :    Dict ,		a       :    str ,		a       :    str=7 ,		a       :    Union[str, Any]=3 ,		a       :    str=18 ,		a       :    List[Any]=30 ,		a       :    Any=400 ,		a       :    List[str]=True ,		a       :    int=None ,		a       :    int=2 ,		a       :    Tuple=True ,		a       :    Any=[0.5, 0.5, 0.5] ,		a       :    Tuple=[0.5, 0.5, 0.5] ,		)   ->    Dict:

									"""simple docstring"""
									lowercase										=							size if size is not None else {'''shortest_edge''': 30}
									lowercase										=							parent
									lowercase										=							batch_size
									lowercase										=							num_channels
									lowercase										=							image_size
									lowercase										=							min_resolution
									lowercase										=							max_resolution
									lowercase										=							do_resize
									lowercase										=							size
									lowercase										=							size_divisor
									lowercase										=							do_normalize
									lowercase										=							image_mean
									lowercase										=							image_std

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Dict:

									"""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,
									    "size_divisor": self.size_divisor,
									}

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    int ,		a       :    Optional[int]=False    )   ->    Tuple:

									"""simple docstring"""
									if not batched:
											lowercase										=							self.size['''shortest_edge''']
											lowercase										=							image_inputs[0]
											if isinstance(a ,		Image.Image    ):
													lowercase					,  lowercase										=							image.size
											else:
													lowercase					,  lowercase										=							image.shape[1], image.shape[2]
											lowercase										=							size / min(a ,		a    )
											if h < w:
													lowercase					,  lowercase										=							size, scale * w
											else:
													lowercase					,  lowercase										=							scale * h, size

											lowercase										=							int((1333 / 800) * size    )
											if max(a ,		a    ) > max_size:
													lowercase										=							max_size / max(a ,		a    )
													lowercase										=							newh * scale
													lowercase										=							neww * scale

											lowercase					,  lowercase										=							int(newh + 0.5    ), int(neww + 0.5    )
											lowercase					,  lowercase										=							(
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

									else:
											lowercase										=							[]
											for image in image_inputs:
													lowercase					,  lowercase										=							self.get_expected_values([image]    )
													expected_values.append((expected_height, expected_width)    )
											lowercase										=							max(a ,		key=lambda a    : item[0]    )[0]
											lowercase										=							max(a ,		key=lambda a    : item[1]    )[1]

									return expected_height, expected_width




@require_torch
@require_vision
class 	_lowerCAmelCase    (      __snake_case       ,       unittest.TestCase       ):
							__lowerCAmelCase					:    List[Any]     =  ViltImageProcessor if is_vision_available() else None

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    str:

									"""simple docstring"""
									lowercase										=							ViltImageProcessingTester(self    )

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

									"""simple docstring"""
									return self.image_processor_tester.prepare_image_processor_dict()

							def 					_lowerCAmelCase  ( self       :    str    )   ->    str:

									"""simple docstring"""
									lowercase										=							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'''    )    )
									self.assertTrue(hasattr(a ,		'''size_divisor'''    )    )

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

									"""simple docstring"""
									lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size ,		{'''shortest_edge''': 30}    )

									lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict ,		size=42    )
									self.assertEqual(image_processor.size ,		{'''shortest_edge''': 42}    )

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

									"""simple docstring"""
									pass

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Tuple:

									"""simple docstring"""
									# Initialize image_processing
									lowercase										=							self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									lowercase										=							prepare_image_inputs(self.image_processor_tester ,		equal_resolution=a    )
									for image in image_inputs:
											self.assertIsInstance(a ,		Image.Image    )

									# Test not batched input
									lowercase										=							image_processing(image_inputs[0] ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a    )
									self.assertEqual(
									    encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

									# Test batched
									lowercase										=							image_processing(a ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a ,		batched=a    )
									self.assertEqual(
									    encoded_images.shape ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    ) ,		)

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

									"""simple docstring"""
									# Initialize image_processing
									lowercase										=							self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									lowercase										=							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
									lowercase										=							image_processing(image_inputs[0] ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a    )
									self.assertEqual(
									    encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

									# Test batched
									lowercase										=							image_processing(a ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a ,		batched=a    )
									self.assertEqual(
									    encoded_images.shape ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    ) ,		)

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    List[Any]:

									"""simple docstring"""
									# Initialize image_processing
									lowercase										=							self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									lowercase										=							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
									lowercase										=							image_processing(image_inputs[0] ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a    )
									self.assertEqual(
									    encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

									# Test batched
									lowercase										=							image_processing(a ,		return_tensors='''pt'''    ).pixel_values

									lowercase					,  lowercase										=							self.image_processor_tester.get_expected_values(a ,		batched=a    )
									self.assertEqual(
									    encoded_images.shape ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_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


__lowerCAmelCase				      =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    torch.FloatTensor




class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):
							__lowerCAmelCase					:    int     =  1

							@register_to_config
							def __init__( self       :    Union[str, Any] ,		a       :    int = 40 ,		a       :    float = 0.002 ,		a       :    float = 80.0 ,		a       :    float = 0.5 ,		a       :    float = 1.0 ,		a       :    float = 7.0 ,		a       :    bool = True ,		)   ->    List[Any]:

									"""simple docstring"""
									# standard deviation of the initial noise distribution
									lowercase										=							sigma_max

									lowercase										=							np.linspace(0 ,		1 ,		a    )
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									# setable values
									lowercase										=							None
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							False
									lowercase										=							False

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Optional[int] ,		a       :    str=None    )   ->    Any:

									"""simple docstring"""
									if schedule_timesteps is None:
											lowercase										=							self.timesteps

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

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

									"""simple docstring"""
									# Get sigma corresponding to timestep
									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )
									lowercase										=							self.index_for_timestep(a    )
									lowercase										=							self.sigmas[step_idx]

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

									lowercase										=							True
									return sample

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Union[float, np.ndarray]    )   ->    str:

									"""simple docstring"""
									if not isinstance(a ,		np.ndarray    ):
											lowercase										=							np.array(a ,		dtype=np.floataa    )

									lowercase										=							1000 * 0.25 * np.log(sigmas + 1E-44    )

									return timesteps

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

									"""simple docstring"""
									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}."""    )

											lowercase										=							np.array(a ,		dtype=np.intaa    )
											lowercase										=							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."""    )

											lowercase										=							num_inference_steps

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

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

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

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

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    int    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

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

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    str    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.config.sigma_data

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

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_data

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

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor] ,		a       :    torch.FloatTensor ,		a       :    Optional[torch.Generator] = None ,		a       :    bool = True ,		)   ->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									"""simple docstring"""

									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    ):
											lowercase										=							timestep.to(self.timesteps.device    )

									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.index_for_timestep(a    )

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

									# Get scalings for boundary conditions
									lowercase					,  lowercase										=							self.get_scalings_for_boundary_condition(a    )

									# 1. Denoise model output using boundary conditions
									lowercase										=							c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
											lowercase										=							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:
											lowercase										=							randn_tensor(
											    model_output.shape ,		dtype=model_output.dtype ,		device=model_output.device ,		generator=a    )
									else:
											lowercase										=							torch.zeros_like(a    )
									lowercase										=							noise * self.config.s_noise

									lowercase										=							sigma_next.clamp(min=a ,		max=a    )

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

									if not return_dict:
											return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=a    )

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		)   ->    torch.FloatTensor:

									"""simple docstring"""
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowercase										=							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
											lowercase										=							self.timesteps.to(original_samples.device ,		dtype=torch.floataa    )
											lowercase										=							timesteps.to(original_samples.device ,		dtype=torch.floataa    )
									else:
											lowercase										=							self.timesteps.to(original_samples.device    )
											lowercase										=							timesteps.to(original_samples.device    )

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

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

									lowercase										=							original_samples + noise * sigma
									return noisy_samples

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

									"""simple docstring"""
									return self.config.num_train_timesteps