






# 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     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42
  A_						       = 42




class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 1




  @register_to_config
  def __init__(  self		,		_UpperCAmelCase = 2000		,		_UpperCAmelCase = 0.1_5		,		_UpperCAmelCase = 0.0_1		,		_UpperCAmelCase = 1_3_4_8.0		,		_UpperCAmelCase = 1e-5		,		_UpperCAmelCase = 1		,		)      ->			Tuple:
     # standard deviation of the initial noise distribution
     UpperCamelCase_						=  sigma_max

     # setable values
     UpperCamelCase_						=  None

     self.set_sigmas(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			torch.FloatTensor:
     return sample




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			str:
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

     UpperCamelCase_						=  torch.linspace(1		,		_UpperCAmelCase		,		_UpperCAmelCase		,		device=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			Any:
     UpperCamelCase_						=  sigma_min if sigma_min is not None else self.config.sigma_min
     UpperCamelCase_						=  sigma_max if sigma_max is not None else self.config.sigma_max
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(_UpperCAmelCase		,		_UpperCAmelCase			)

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




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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 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'			)

     UpperCamelCase_						=  timestep * torch.ones(
         sample.shape[0]		,		device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
     UpperCamelCase_						=  (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
     UpperCamelCase_						=  timesteps.to(self.discrete_sigmas.device			)

     UpperCamelCase_						=  self.discrete_sigmas[timesteps].to(sample.device			)
     UpperCamelCase_						=  self.get_adjacent_sigma(_UpperCAmelCase		,		_UpperCAmelCase			).to(sample.device			)
     UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase			)
     UpperCamelCase_						=  (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
     UpperCamelCase_						=  diffusion.flatten()
     while len(diffusion.shape			) < len(sample.shape			):
        UpperCamelCase_						=  diffusion.unsqueeze(-1			)
     UpperCamelCase_						=  drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     UpperCamelCase_						=  randn_tensor(
         sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase		,		device=sample.device		,		dtype=sample.dtype			)
     UpperCamelCase_						=  sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     UpperCamelCase_						=  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=_UpperCAmelCase		,		prev_sample_mean=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 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
     UpperCamelCase_						=  randn_tensor(sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase			).to(sample.device			)

     # compute step size from the model_output, the noise, and the snr
     UpperCamelCase_						=  torch.norm(model_output.reshape(model_output.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  torch.norm(noise.reshape(noise.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     UpperCamelCase_						=  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
     UpperCamelCase_						=  step_size.flatten()
     while len(step_size.shape			) < len(sample.shape			):
        UpperCamelCase_						=  step_size.unsqueeze(-1			)
     UpperCamelCase_						=  sample + step_size * model_output
     UpperCamelCase_						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_UpperCAmelCase			)




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




  def __len__(  self			)      ->			Optional[int]:
     return self.config.num_train_timesteps









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 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								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 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






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







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							Image.Image    )

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






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							numpify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							np.ndarray    )

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






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

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

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


