


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 A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						torch.FloatTensor




class A						(     UpperCamelCase_					,							UpperCamelCase_  ):
      UpperCamelCase__							:						List[str]   =1






      @register_to_config
      def __init__(      self		:  List[str]    ,     lowercase_		:  int = 40    ,     lowercase_		:  float = 0.002    ,     lowercase_		:  float = 80.0    ,     lowercase_		:  float = 0.5    ,     lowercase_		:  float = 1.0    ,     lowercase_		:  float = 7.0    ,     lowercase_		:  bool = True    ,     )      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=sigma_max

             _lowerCamelCase :	Tuple						=np.linspace(0    ,     1    ,     lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=self._convert_to_karras(lowercase_				)
             _lowerCamelCase :	Any						=self.sigma_to_t(lowercase_				)

             # setable values
             _lowerCamelCase :	Any						=None
             _lowerCamelCase :	Union[str, Any]						=torch.from_numpy(lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=torch.from_numpy(lowercase_				)
             _lowerCamelCase :	str						=False
             _lowerCamelCase :	Union[str, Any]						=False






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[str]    ,     lowercase_		:  Tuple=None				)      ->		Union[str, Any]:
             """simple docstring"""




             if schedule_timesteps is None:
                    _lowerCamelCase :	List[Any]						=self.timesteps

             _lowerCamelCase :	int						=(schedule_timesteps == timestep).nonzero()
             return indices.item()






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Union[float, torch.FloatTensor]				)      ->		torch.FloatTensor:
             """simple docstring"""




             if isinstance(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	Optional[Any]						=timestep.to(self.timesteps.device				)
             _lowerCamelCase :	int						=self.index_for_timestep(lowercase_				)
             _lowerCamelCase :	int						=self.sigmas[step_idx]

             _lowerCamelCase :	Optional[int]						=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

             _lowerCamelCase :	str						=True
             return sample






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Union[float, np.ndarray]				)      ->		Optional[int]:
             """simple docstring"""




             if not isinstance(lowercase_    ,     np.ndarray				):
                    _lowerCamelCase :	List[str]						=np.array(lowercase_    ,     dtype=np.floataa				)

             _lowerCamelCase :	Any						=1000 * 0.25 * np.log(sigmas + 1E-44				)

             return timesteps






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Optional[int] = None    ,     lowercase_		:  Union[str, torch.device] = None    ,     lowercase_		:  Optional[List[int]] = None    ,     )      ->		Optional[Any]:
             """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(lowercase_				)				):
                           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}.'''				)

                    _lowerCamelCase :	int						=np.array(lowercase_    ,     dtype=np.intaa				)
                    _lowerCamelCase :	str						=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.'''				)

                    _lowerCamelCase :	Any						=num_inference_steps

                    _lowerCamelCase :	Optional[int]						=self.config.num_train_timesteps // self.num_inference_steps
                    _lowerCamelCase :	Optional[Any]						=(np.arange(0    ,     lowercase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
                    _lowerCamelCase :	List[Any]						=False

             # Map timesteps to Karras sigmas directly for multistep sampling
             # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
             _lowerCamelCase :	Union[str, Any]						=self.config.num_train_timesteps
             _lowerCamelCase :	List[Any]						=timesteps[::-1].copy()
             _lowerCamelCase :	Optional[Any]						=ramp / (num_train_timesteps - 1)
             _lowerCamelCase :	int						=self._convert_to_karras(lowercase_				)
             _lowerCamelCase :	Tuple						=self.sigma_to_t(lowercase_				)

             _lowerCamelCase :	int						=np.concatenate([sigmas, [self.sigma_min]]				).astype(np.floataa				)
             _lowerCamelCase :	List[Any]						=torch.from_numpy(lowercase_				).to(device=lowercase_				)

             if str(lowercase_				).startswith('mps'				):
                    # mps does not support float64
                    _lowerCamelCase :	str						=torch.from_numpy(lowercase_				).to(lowercase_    ,     dtype=torch.floataa				)
             else:
                    _lowerCamelCase :	Optional[Any]						=torch.from_numpy(lowercase_				).to(device=lowercase_				)






      def  lowerCamelCase (      self		:  List[Any]    ,     lowercase_		:  Optional[Any]				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	float						=self.config.sigma_min
             _lowerCamelCase :	float						=self.config.sigma_max

             _lowerCamelCase :	List[str]						=self.config.rho
             _lowerCamelCase :	Union[str, Any]						=sigma_min ** (1 / rho)
             _lowerCamelCase :	Tuple						=sigma_max ** (1 / rho)
             _lowerCamelCase :	Tuple						=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
             return sigmas






      def  lowerCamelCase (      self		:  Tuple    ,     lowercase_		:  Union[str, Any]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.config.sigma_data

             _lowerCamelCase :	List[Any]						=sigma_data**2 / (sigma**2 + sigma_data**2)
             _lowerCamelCase :	Union[str, Any]						=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out






      def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  List[Any]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	str						=self.config.sigma_min
             _lowerCamelCase :	Tuple						=self.config.sigma_data

             _lowerCamelCase :	Union[str, Any]						=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
             _lowerCamelCase :	Optional[Any]						=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Union[float, torch.FloatTensor]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  Optional[torch.Generator] = None    ,     lowercase_		:  bool = True    ,     )      ->		Union[CMStochasticIterativeSchedulerOutput, Tuple]:
             """simple docstring"""




             if (
                 isinstance(lowercase_    ,     lowercase_				)
                 or isinstance(lowercase_    ,     torch.IntTensor				)
                 or isinstance(lowercase_    ,     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(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	Tuple						=timestep.to(self.timesteps.device				)

             _lowerCamelCase :	List[str]						=self.config.sigma_min
             _lowerCamelCase :	Dict						=self.config.sigma_max

             _lowerCamelCase :	Union[str, Any]						=self.index_for_timestep(lowercase_				)

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

             # Get scalings for boundary conditions
             _lowerCamelCase :	Dict						=self.get_scalings_for_boundary_condition(lowercase_				)

             # 1. Denoise model output using boundary conditions
             _lowerCamelCase :	str						=c_out * model_output + c_skip * sample
             if self.config.clip_denoised:
                    _lowerCamelCase :	Tuple						=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:
                    _lowerCamelCase :	str						=randn_tensor(
                        model_output.shape    ,     dtype=model_output.dtype    ,     device=model_output.device    ,     generator=lowercase_				)
             else:
                    _lowerCamelCase :	str						=torch.zeros_like(lowercase_				)
             _lowerCamelCase :	List[str]						=noise * self.config.s_noise

             _lowerCamelCase :	List[str]						=sigma_next.clamp(min=lowercase_    ,     max=lowercase_				)

             # 3. Return noisy sample
             # tau = sigma_hat, eps = sigma_min
             _lowerCamelCase :	Optional[int]						=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

             if not return_dict:
                    return (prev_sample,)

             return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[Any]    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  torch.FloatTensor    ,     lowercase_		:  torch.FloatTensor    ,     )      ->		torch.FloatTensor:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=self.sigmas.to(device=original_samples.device    ,     dtype=original_samples.dtype				)
             if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_				):
                    # mps does not support float64
                    _lowerCamelCase :	Tuple						=self.timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
                    _lowerCamelCase :	Tuple						=timesteps.to(original_samples.device    ,     dtype=torch.floataa				)
             else:
                    _lowerCamelCase :	Any						=self.timesteps.to(original_samples.device				)
                    _lowerCamelCase :	List[str]						=timesteps.to(original_samples.device				)

             _lowerCamelCase :	Any						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

             _lowerCamelCase :	Optional[int]						=original_samples + noise * sigma
             return noisy_samples






      def __len__(      self		:  int				)      ->		Optional[int]:
             """simple docstring"""




             return self.config.num_train_timesteps






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 ConvNextImageProcessor




class A						(     unittest.TestCase  ):






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  str    ,     lowercase_		:  Tuple=7    ,     lowercase_		:  int=3    ,     lowercase_		:  Union[str, Any]=18    ,     lowercase_		:  Optional[int]=30    ,     lowercase_		:  Tuple=400    ,     lowercase_		:  str=True    ,     lowercase_		:  Dict=None    ,     lowercase_		:  Tuple=0.875    ,     lowercase_		:  List[Any]=True    ,     lowercase_		:  Union[str, Any]=[0.5, 0.5, 0.5]    ,     lowercase_		:  List[str]=[0.5, 0.5, 0.5]    ,     )      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=size if size is not None else {'shortest_edge': 20}
             _lowerCamelCase :	Optional[Any]						=parent
             _lowerCamelCase :	Union[str, Any]						=batch_size
             _lowerCamelCase :	Dict						=num_channels
             _lowerCamelCase :	Union[str, Any]						=image_size
             _lowerCamelCase :	Optional[int]						=min_resolution
             _lowerCamelCase :	Tuple						=max_resolution
             _lowerCamelCase :	Any						=do_resize
             _lowerCamelCase :	Dict						=size
             _lowerCamelCase :	int						=crop_pct
             _lowerCamelCase :	Dict						=do_normalize
             _lowerCamelCase :	int						=image_mean
             _lowerCamelCase :	Union[str, Any]						=image_std






      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """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,
                 "crop_pct": self.crop_pct,
             }




@require_torch
@require_vision
class A						(     UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						List[Any]   =ConvNextImageProcessor if is_vision_available() else None






      def  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=ConvNextImageProcessingTester(self				)






      @property
      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.image_processing_class(**self.image_processor_dict				)
             self.assertTrue(hasattr(lowercase_    ,     'do_resize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'size'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'crop_pct'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'do_normalize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_mean'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_std'				)				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.image_processing_class.from_dict(self.image_processor_dict				)
             self.assertEqual(image_processor.size    ,     {'shortest_edge': 20}				)

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






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             pass






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PIL images
             _lowerCamelCase :	Any						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     Image.Image				)

             # Test not batched input
             _lowerCamelCase :	Tuple						=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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	List[str]						=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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )






      def  lowerCamelCase (      self		:  int				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.image_processing_class(**self.image_processor_dict				)
             # create random numpy tensors
             _lowerCamelCase :	List[Any]						=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
             _lowerCamelCase :	Optional[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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	Any						=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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )






      def  lowerCamelCase (      self		:  str				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             _lowerCamelCase :	Any						=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
             _lowerCamelCase :	Optional[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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	Union[str, Any]						=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['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )


