

'''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   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:torch.FloatTensor
 __SCREAMING_SNAKE_CASE					:torch.FloatTensor






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 2000			,				a__		:   float = 0.15			,				a__		:   float = 0.01			,				a__		:   float = 1_348.0			,				a__		:   float = 1E-5			,				a__		:   int = 1			,				):
      # standard deviation of the initial noise distribution
      __magic_name__						       =    sigma_max

      # setable values
      __magic_name__						       =    None

      self.set_sigmas(a__			,				a__			,				a__			,				a__      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   Optional[int] = None      ):
      return sample



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   int			,				a__		:   float = None			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

      __magic_name__						       =    torch.linspace(1			,				a__			,				a__			,				device=a__      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   int			,				a__		:   float = None			,				a__		:   float = None			,				a__		:   float = None      ):
      __magic_name__						       =    sigma_min if sigma_min is not None else self.config.sigma_min
      __magic_name__						       =    sigma_max if sigma_max is not None else self.config.sigma_max
      __magic_name__						       =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
           self.set_timesteps(a__			,				a__      )

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



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   Dict      ):
      return torch.where(
          timesteps == 0			,				torch.zeros_like(t.to(timesteps.device      )      )			,				self.discrete_sigmas[timesteps - 1].to(timesteps.device      )			,				)



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   Optional[torch.Generator] = None			,				a__		:   bool = True			,				):
      if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''      )

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

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

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



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   torch.FloatTensor			,				a__		:   torch.FloatTensor			,				a__		:   Optional[torch.Generator] = None			,				a__		:   bool = True			,				):
      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
      __magic_name__						       =    randn_tensor(sample.shape			,				layout=sample.layout			,				generator=a__      ).to(sample.device      )

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

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



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



 def __len__(   self		:   Any      ):
      return self.config.num_train_timesteps









'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(dataset[0]['''file''']			)
     __magic_name__						       =    Image.open(dataset[1]['''file''']			)

     return image, map




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(ds[0]['''file''']			)
     __magic_name__						       =    Image.open(ds[1]['''file''']			)
     __magic_name__						       =    Image.open(ds[2]['''file''']			)
     __magic_name__						       =    Image.open(ds[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					BeitImageProcessor if is_vision_available() else None



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    BeitImageProcessingTester(self      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(a__			,				'''do_resize'''      )      )
      self.assertTrue(hasattr(a__			,				'''size'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_normalize'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_mean'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_std'''      )      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size			,				{'''height''': 20, '''width''': 20}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 18, '''width''': 18}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )

      __magic_name__						       =    self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=a__      )
      self.assertEqual(image_processor.size			,				{'''height''': 42, '''width''': 42}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 84, '''width''': 84}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				Image.Image      )

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

      # Test batched
      __magic_name__						       =    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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Optional[int]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      __magic_name__						       =    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
      __magic_name__						       =    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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    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
      __magic_name__						       =    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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    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.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      __magic_name__						       =    []
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )
           maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				maps[0]			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test not batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_batch_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )



 def 			snake_case__  (   self		:   Any      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 150      )

      __magic_name__						       =    True
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )






