



'''simple docstring'''





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


UpperCamelCase__    :     Dict			  =   logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     42


class 							_lowerCAmelCase	(  __A,							__A							):


 """simple docstring"""

 lowerCamelCase									=     1







 @register_to_config
 def __init__(      self					,    _lowerCamelCase = 40					,    _lowerCamelCase = 0.002					,    _lowerCamelCase = 80.0					,    _lowerCamelCase = 0.5					,    _lowerCamelCase = 1.0					,    _lowerCamelCase = 7.0					,    _lowerCamelCase = True					,    )    ->    Optional[int]:
    # standard deviation of the initial noise distribution
    A_  :		Union[str, Any]						=					sigma_max

    A_  :		Any						=					np.linspace(0					,    1					,    _lowerCamelCase  )
    A_  :		Tuple						=					self._convert_to_karras(_lowerCamelCase  )
    A_  :		Tuple						=					self.sigma_to_t(_lowerCamelCase  )

    # setable values
    A_  :		Any						=					None
    A_  :		List[str]						=					torch.from_numpy(_lowerCamelCase  )
    A_  :		List[Any]						=					torch.from_numpy(_lowerCamelCase  )
    A_  :		List[Any]						=					False
    A_  :		str						=					False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=None  )    ->    str:
    if schedule_timesteps is None:
       A_  :		Tuple						=					self.timesteps

    A_  :		Dict						=					(schedule_timesteps == timestep).nonzero()
    return indices.item()







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    torch.FloatTensor:
    # Get sigma corresponding to timestep
    if isinstance(_lowerCamelCase					,    torch.Tensor  ):
       A_  :		str						=					timestep.to(self.timesteps.device  )
    A_  :		str						=					self.index_for_timestep(_lowerCamelCase  )
    A_  :		List[Any]						=					self.sigmas[step_idx]

    A_  :		str						=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

    A_  :		Union[str, Any]						=					True
    return sample







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Any:
    if not isinstance(_lowerCamelCase					,    np.ndarray  ):
       A_  :		str						=					np.array(_lowerCamelCase					,    dtype=np.floataa  )

    A_  :		Tuple						=					1000 * 0.25 * np.log(sigmas + 1e-44  )

    return timesteps







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    )    ->    Optional[Any]:
    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(_lowerCamelCase  )  ):
          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}."  )

       A_  :		List[Any]						=					np.array(_lowerCamelCase					,    dtype=np.intaa  )
       A_  :		Union[str, Any]						=					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."  )

       A_  :		Dict						=					num_inference_steps

       A_  :		Union[str, Any]						=					self.config.num_train_timesteps // self.num_inference_steps
       A_  :		Dict						=					(np.arange(0					,    _lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
       A_  :		Tuple						=					False

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

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

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







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    A_  :		float						=					self.config.sigma_min
    A_  :		float						=					self.config.sigma_max

    A_  :		Optional[Any]						=					self.config.rho
    A_  :		Dict						=					sigma_min ** (1 / rho)
    A_  :		Dict						=					sigma_max ** (1 / rho)
    A_  :		Optional[int]						=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    int:
    A_  :		Any						=					self.config.sigma_data

    A_  :		Dict						=					sigma_data**2 / (sigma**2 + sigma_data**2)
    A_  :		int						=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    A_  :		List[Any]						=					self.config.sigma_min
    A_  :		Any						=					self.config.sigma_data

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







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = True					,    )    ->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

    if (
        isinstance(_lowerCamelCase					,    _lowerCamelCase  )
        or isinstance(_lowerCamelCase					,    torch.IntTensor  )
        or isinstance(_lowerCamelCase					,    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(_lowerCamelCase					,    torch.Tensor  ):
       A_  :		List[str]						=					timestep.to(self.timesteps.device  )

    A_  :		Dict						=					self.config.sigma_min
    A_  :		Dict						=					self.config.sigma_max

    A_  :		Any						=					self.index_for_timestep(_lowerCamelCase  )

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

    # Get scalings for boundary conditions
    A_						,  A_  :		List[str]						=					self.get_scalings_for_boundary_condition(_lowerCamelCase  )

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

    A_  :		Dict						=					sigma_next.clamp(min=_lowerCamelCase					,    max=_lowerCamelCase  )

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

    if not return_dict:
       return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    A_  :		Tuple						=					self.sigmas.to(device=original_samples.device					,    dtype=original_samples.dtype  )
    if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase  ):
       # mps does not support float64
       A_  :		Any						=					self.timesteps.to(original_samples.device					,    dtype=torch.floataa  )
       A_  :		Optional[int]						=					timesteps.to(original_samples.device					,    dtype=torch.floataa  )
    else:
       A_  :		Dict						=					self.timesteps.to(original_samples.device  )
       A_  :		Tuple						=					timesteps.to(original_samples.device  )

    A_  :		Optional[int]						=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    A_  :		Union[str, Any]						=					sigmas[step_indices].flatten()
    while len(sigma.shape  ) < len(original_samples.shape  ):
       A_  :		Optional[Any]						=					sigma.unsqueeze(-1  )

    A_  :		Optional[Any]						=					original_samples + noise * sigma
    return noisy_samples







 def __len__(      self  )    ->    Tuple:
    return self.config.num_train_timesteps





'''simple docstring'''





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 ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       A_  :		Union[str, Any]						=					[]
       for image in image_inputs:
          A_						,  A_  :		Any						=					self.get_expected_values([image]  )
          expected_values.append((expected_height, expected_width)  )
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[0]  )[0]
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[1]  )[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Tuple						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )
