




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		42
					_UpperCamelCase							=		42





class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 2_000      ,       _lowercase     :						float = 0.15      ,       _lowercase     :						float = 0.01      ,       _lowercase     :						float = 13_48.0      ,       _lowercase     :						float = 1E-5      ,       _lowercase     :						int = 1      ,       )    ->							List[str]:
								# standard deviation of the initial noise distribution
								A_									= sigma_max

								# setable values
								A_									= None

								self.set_sigmas(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						Optional[int] = None)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						int      ,       _lowercase     :						float = None      ,       _lowercase     :						Union[str, torch.device] = None)    ->							List[Any]:
								A_									= sampling_eps if sampling_eps is not None else self.config.sampling_eps

								A_									= torch.linspace(1      ,       _lowercase      ,       _lowercase      ,       device=_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						int      ,       _lowercase     :						float = None      ,       _lowercase     :						float = None      ,       _lowercase     :						float = None)    ->							Optional[int]:
								A_									= sigma_min if sigma_min is not None else self.config.sigma_min
								A_									= sigma_max if sigma_max is not None else self.config.sigma_max
								A_									= sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
											self.set_timesteps(_lowercase      ,       _lowercase)

								A_									= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								A_									= torch.exp(torch.linspace(math.log(_lowercase)      ,       math.log(_lowercase)      ,       _lowercase))
								A_									= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						str      ,       _lowercase     :						List[Any])    ->							str:
								return torch.where(
								    timesteps == 0      ,       torch.zeros_like(t.to(timesteps.device))      ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device)      ,       )



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						Optional[torch.Generator] = None      ,       _lowercase     :						bool = True      ,       )    ->							Union[SdeVeOutput, Tuple]:
								if self.timesteps is None:
											raise ValueError(
											    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

								A_									= timestep * torch.ones(
								    sample.shape[0]      ,       device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
								A_									= (timestep * (len(self.timesteps) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								A_									= timesteps.to(self.discrete_sigmas.device)

								A_									= self.discrete_sigmas[timesteps].to(sample.device)
								A_									= self.get_adjacent_sigma(_lowercase      ,       _lowercase).to(sample.device)
								A_									= torch.zeros_like(_lowercase)
								A_									= (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								A_									= diffusion.flatten()
								while len(diffusion.shape) < len(sample.shape):
											A_									= diffusion.unsqueeze(-1)
								A_									= drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								A_									= randn_tensor(
								    sample.shape      ,       layout=sample.layout      ,       generator=_lowercase      ,       device=sample.device      ,       dtype=sample.dtype)
								A_									= sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								A_									= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
											return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=_lowercase      ,       prev_sample_mean=_lowercase)



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						Optional[torch.Generator] = None      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.timesteps is None:
											raise ValueError(
											    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								A_									= randn_tensor(sample.shape      ,       layout=sample.layout      ,       generator=_lowercase).to(sample.device)

								# compute step size from the model_output, the noise, and the snr
								A_									= torch.norm(model_output.reshape(model_output.shape[0]      ,       -1)      ,       dim=-1).mean()
								A_									= torch.norm(noise.reshape(noise.shape[0]      ,       -1)      ,       dim=-1).mean()
								A_									= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								A_									= step_size * torch.ones(sample.shape[0]).to(sample.device)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								A_									= step_size.flatten()
								while len(step_size.shape) < len(sample.shape):
											A_									= step_size.unsqueeze(-1)
								A_									= sample + step_size * model_output
								A_									= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						torch.FloatTensor      ,       )    ->							torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								A_									= timesteps.to(original_samples.device)
								A_									= self.discrete_sigmas.to(original_samples.device)[timesteps]
								A_									= (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(_lowercase) * sigmas[:, None, None, None]
								)
								A_									= noise + original_samples
								return noisy_samples



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










'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       _lowercase     :						Any      ,       _lowercase     :						Tuple=7      ,       _lowercase     :						Tuple=3      ,       _lowercase     :						str=18      ,       _lowercase     :						Union[str, Any]=30      ,       _lowercase     :						Dict=400      ,       _lowercase     :						int=True      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						int=True      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						str=True      ,       _lowercase     :						Union[str, Any]=True      ,       _lowercase     :						Any=[0.5, 0.5, 0.5]      ,       _lowercase     :						str=[0.5, 0.5, 0.5]      ,       )    ->							Optional[Any]:
								A_									= parent
								A_									= batch_size
								A_									= num_channels
								A_									= image_size
								A_									= min_resolution
								A_									= max_resolution
								A_									= do_resize
								A_									= size if size is not None else {'height': 18, 'width': 20}
								A_									= do_thumbnail
								A_									= do_align_axis
								A_									= do_pad
								A_									= do_normalize
								A_									= image_mean
								A_									= image_std



					def     __snake_case						(	self     :						int)    ->							int:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		DonutImageProcessor if is_vision_available() else None



					def     __snake_case						(	self     :						Optional[int])    ->							Union[str, Any]:
								A_									= DonutImageProcessingTester(self)



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



					def     __snake_case						(	self     :						Any)    ->							Tuple:
								A_									= self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase      ,       'do_resize'))
								self.assertTrue(hasattr(_lowercase      ,       'size'))
								self.assertTrue(hasattr(_lowercase      ,       'do_thumbnail'))
								self.assertTrue(hasattr(_lowercase      ,       'do_align_long_axis'))
								self.assertTrue(hasattr(_lowercase      ,       'do_pad'))
								self.assertTrue(hasattr(_lowercase      ,       'do_normalize'))
								self.assertTrue(hasattr(_lowercase      ,       'image_mean'))
								self.assertTrue(hasattr(_lowercase      ,       'image_std'))



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size      ,       {'height': 18, 'width': 20})

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

								# Previous config had dimensions in (width, height) order
								A_									= self.image_processing_class.from_dict(self.image_processor_dict      ,       size=(42, 84))
								self.assertEqual(image_processor.size      ,       {'height': 84, 'width': 42})



					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								pass



					@is_flaky()
					def     __snake_case						(	self     :						List[str])    ->							int:
								# 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=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       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(_lowercase      ,       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'],
								    )      ,       )



					@is_flaky()
					def     __snake_case						(	self     :						List[Any])    ->							List[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=_lowercase      ,       numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       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(_lowercase      ,       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'],
								    )      ,       )



					@is_flaky()
					def     __snake_case						(	self     :						Dict)    ->							int:
								# 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=_lowercase      ,       torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       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(_lowercase      ,       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'],
								    )      ,       )




