


"""simple docstring"""






import math
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 KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	A_       (     _snake_case   ):





  """simple docstring"""

  lowercase		:	torch.FloatTensor
  lowercase		:	Optional[torch.FloatTensor]   =						None




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=0.9_9_9       ,   UpperCAmelCase__="cosine"       ,   )     ->     str:







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase__				):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCAmelCase__				):
         return math.exp(t * -1_2.0				)

   else:
      raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

   a   :	Optional[int]						=    []
   for i in range(UpperCAmelCase__				):
      a   :	Union[str, Any]						=    i / num_diffusion_timesteps
      a   :	int						=    (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__				) / alpha_bar_fn(UpperCAmelCase__				)       ,   UpperCAmelCase__				)				)
   return torch.tensor(UpperCAmelCase__       ,   dtype=torch.floataa				)


class 	A_       (     _snake_case      ,  _snake_case   ):





  """simple docstring"""

  lowercase		:	List[str]   =						[e.name for e in KarrasDiffusionSchedulers]
  lowercase		:	Optional[int]   =						1
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = "linspace"  ,					__UpperCAmelCase = 0  ,					)				->					Any:
     if trained_betas is not None:
        a   :	List[Any]						=    torch.tensor(snake_case_  ,					dtype=torch.floataa						)
     elif beta_schedule == "linear":
        a   :	str						=    torch.linspace(snake_case_  ,					snake_case_  ,					snake_case_  ,					dtype=torch.floataa						)
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        a   :	Any						=    (
            torch.linspace(beta_start**0.5  ,					beta_end**0.5  ,					snake_case_  ,					dtype=torch.floataa						) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        a   :	Any						=    betas_for_alpha_bar(snake_case_						)
     else:
        raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'						)

     a   :	Optional[int]						=    1.0 - self.betas
     a   :	Union[str, Any]						=    torch.cumprod(self.alphas  ,					dim=0						)

     a   :	int						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     a   :	Dict						=    np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
     a   :	int						=    torch.from_numpy(snake_case_						)

     # setable values
     a   :	int						=    None
     a   :	int						=    np.linspace(0  ,					num_train_timesteps - 1  ,					snake_case_  ,					dtype=snake_case_						)[::-1].copy()
     a   :	Any						=    torch.from_numpy(snake_case_						)
     a   :	Dict						=    False
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     # standard deviation of the initial noise distribution
     if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Any:
     if isinstance(snake_case_  ,					torch.Tensor						):
        a   :	Optional[int]						=    timestep.to(self.timesteps.device						)
     a   :	Optional[int]						=    (self.timesteps == timestep).nonzero().item()
     a   :	Any						=    self.sigmas[step_index]
     a   :	Tuple						=    sample / ((sigma**2 + 1) ** 0.5)
     a   :	int						=    True
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None						)				->					Optional[Any]:
     a   :	int						=    num_inference_steps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
        a   :	List[Any]						=    np.linspace(0  ,					self.config.num_train_timesteps - 1  ,					snake_case_  ,					dtype=snake_case_						)[
            ::-1
        ].copy()
     elif self.config.timestep_spacing == "leading":
        a   :	Dict						=    self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        a   :	Any						=    (np.arange(0  ,					snake_case_						) * step_ratio).round()[::-1].copy().astype(snake_case_						)
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        a   :	Union[str, Any]						=    self.config.num_train_timesteps / self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        a   :	Optional[Any]						=    (np.arange(self.config.num_train_timesteps  ,					0  ,					-step_ratio						)).round().copy().astype(snake_case_						)
        timesteps -= 1
     else:
        raise ValueError(
            f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'						)

     a   :	List[Any]						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     a   :	Tuple						=    np.interp(snake_case_  ,					np.arange(0  ,					len(snake_case_						)						)  ,					snake_case_						)
     a   :	Any						=    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
     a   :	Union[str, Any]						=    torch.from_numpy(snake_case_						).to(device=snake_case_						)
     if str(snake_case_						).startswith('mps'						):
        # mps does not support float64
        a   :	Dict						=    torch.from_numpy(snake_case_						).to(snake_case_  ,					dtype=torch.floataa						)
     else:
        a   :	Union[str, Any]						=    torch.from_numpy(snake_case_						).to(device=snake_case_						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					)				->					Dict:

     if (
         isinstance(snake_case_  ,					snake_case_						)
         or isinstance(snake_case_  ,					torch.IntTensor						)
         or isinstance(snake_case_  ,					torch.LongTensor						)
     ):
        raise ValueError(
            (
                'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                ' `EulerDiscreteScheduler.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(snake_case_  ,					torch.Tensor						):
        a   :	Any						=    timestep.to(self.timesteps.device						)

     a   :	List[str]						=    (self.timesteps == timestep).nonzero().item()
     a   :	List[Any]						=    self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Dict						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	Dict						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
        raise NotImplementedError('prediction_type not implemented yet: sample'						)
     else:
        raise ValueError(
            f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

     a   :	List[str]						=    self.sigmas[step_index]
     a   :	Any						=    self.sigmas[step_index + 1]
     a   :	Union[str, Any]						=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
     a   :	Optional[Any]						=    (sigma_to**2 - sigma_up**2) ** 0.5

     # 2. Convert to an ODE derivative
     a   :	str						=    (sample - pred_original_sample) / sigma

     a   :	Optional[int]						=    sigma_down - sigma

     a   :	Dict						=    sample + derivative * dt

     a   :	Any						=    model_output.device
     a   :	List[Any]						=    randn_tensor(model_output.shape  ,					dtype=model_output.dtype  ,					device=snake_case_  ,					generator=snake_case_						)

     a   :	Union[str, Any]						=    prev_sample + noise * sigma_up

     if not return_dict:
        return (prev_sample,)

     return EulerAncestralDiscreteSchedulerOutput(
         prev_sample=snake_case_  ,					pred_original_sample=snake_case_						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					Any:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     a   :	str						=    self.sigmas.to(device=original_samples.device  ,					dtype=original_samples.dtype						)
     if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_						):
        # mps does not support float64
        a   :	int						=    self.timesteps.to(original_samples.device  ,					dtype=torch.floataa						)
        a   :	Dict						=    timesteps.to(original_samples.device  ,					dtype=torch.floataa						)
     else:
        a   :	Any						=    self.timesteps.to(original_samples.device						)
        a   :	List[str]						=    timesteps.to(original_samples.device						)

     a   :	Dict						=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

     a   :	List[Any]						=    original_samples + noise * sigma
     return noisy_samples






  def __len__(						self						)				->					Optional[int]:
     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 SegformerImageProcessor






class   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=False    ,     ):
      _snake_case  :    List[str]            = size if size is not None else {"height": 30, "width": 30}
      _snake_case  :    Optional[int]            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    Any            = num_channels
      _snake_case  :    Optional[Any]            = min_resolution
      _snake_case  :    int            = max_resolution
      _snake_case  :    int            = do_resize
      _snake_case  :    List[Any]            = size
      _snake_case  :    int            = do_normalize
      _snake_case  :    Any            = image_mean
      _snake_case  :    str            = image_std
      _snake_case  :    List[Any]            = do_reduce_labels


    def        lowerCamelCase__						(     self  ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }


def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    List[str]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Any            = Image.open(dataset[0]["file"]		)
  _snake_case  :    Optional[int]            = Image.open(dataset[1]["file"]		)

  return image, map
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Optional[int]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Optional[int]            = Image.open(dataset[0]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[1]["file"]		)
  _snake_case  :    Dict            = Image.open(dataset[2]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[3]["file"]		)

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






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Dict	=       SegformerImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = SegformerImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = 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_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_reduce_labels"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size    ,     {"height": 30, "width": 30}  )
      self.assertEqual(image_processor.do_reduce_labels    ,     snake_case_  )

      _snake_case  :    int            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     reduce_labels=snake_case_  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.do_reduce_labels    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    List[str]            = 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
      _snake_case  :    Any            = 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
      _snake_case  :    List[Any]            = 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"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Any            = 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
      _snake_case  :    Optional[int]            = 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
      _snake_case  :    Optional[int]            = 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"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[int]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[Any]            = 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
      _snake_case  :    Dict            = 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
      _snake_case  :    List[Any]            = 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"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    int            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      _snake_case  :    Optional[int]            = []
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )
        maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _snake_case  :    Optional[Any]            = 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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test batched
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test not batched input (PIL images)
      _snake_case      ,       _snake_case  :    int            = prepare_semantic_single_inputs()

      _snake_case  :    List[str]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test batched input (PIL images)
      _snake_case      ,       _snake_case  :    str            = prepare_semantic_batch_inputs()

      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              2,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )


    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = 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
      _snake_case      ,       _snake_case  :    List[Any]            = prepare_semantic_single_inputs()
      _snake_case  :    Dict            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 1_50  )

      _snake_case  :    Tuple            = True
      _snake_case  :    Union[str, Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

