'''simple docstring'''




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      List[str]=0.9_9_9			,	_UpperCAmelCase  :      Tuple="cosine"			,	)    ->    int:


       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(_UpperCAmelCase  :      Optional[int]    ):
                     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  :      Optional[int]    ):
                     return math.exp(t * -1_2.0    )

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

       _UpperCAmelCase     :  str							      =    []
       for i in range(_UpperCAmelCase    ):
              _UpperCAmelCase     :  int							      =    i / num_diffusion_timesteps
              _UpperCAmelCase     :  Union[str, Any]							      =    (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 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						[e.name for e in KarrasDiffusionSchedulers]
      __UpperCamelCase:  List[Any]						=						2




      @register_to_config
      def __init__(       self						:				str						,					A						:				int = 1000						,					A						:				float = 0.00_085						,					A						:				float = 0.012						,					A						:				str = "linear"						,					A						:				Optional[Union[np.ndarray, List[float]]] = None						,					A						:				str = "epsilon"						,					A						:				Optional[bool] = False						,					A						:				Optional[bool] = False						,					A						:				float = 1.0						,					A						:				str = "linspace"						,					A						:				int = 0						,					):
             if trained_betas is not None:
                    _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(A						,					dtype=torch.floataa       )
             elif beta_schedule == "linear":
                    _UpperCAmelCase     :  Dict							      =    torch.linspace(A						,					A						,					A						,					dtype=torch.floataa       )
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    _UpperCAmelCase     :  int							      =    (
                        torch.linspace(beta_start**0.5						,					beta_end**0.5						,					A						,					dtype=torch.floataa       ) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    _UpperCAmelCase     :  Optional[Any]							      =    betas_for_alpha_bar(A						,					alpha_transform_type="cosine"       )
             elif beta_schedule == "exp":
                    _UpperCAmelCase     :  List[str]							      =    betas_for_alpha_bar(A						,					alpha_transform_type="exp"       )
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

             _UpperCAmelCase     :  Optional[int]							      =    1.0 - self.betas
             _UpperCAmelCase     :  Tuple							      =    torch.cumprod(self.alphas						,					dim=0       )

             #  set all values
             self.set_timesteps(A						,					A						,					A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    use_karras_sigmas




      def      _A	(       self						:				Tuple						,					A						:				List[Any]						,					A						:				str=None       ):
             if schedule_timesteps is None:
                    _UpperCAmelCase     :  Dict							      =    self.timesteps

             _UpperCAmelCase     :  Union[str, Any]							      =    (schedule_timesteps == timestep).nonzero()

             # The sigma index that is taken for the **very** first `step`
             # is always the second index (or the last index if there is only 1)
             # This way we can ensure we don't accidentally skip a sigma in
             # case we start in the middle of the denoising schedule (e.g. for image-to-image)
             if len(self._index_counter       ) == 0:
                    _UpperCAmelCase     :  Optional[int]							      =    1 if len(A       ) > 1 else 0
             else:
                    _UpperCAmelCase     :  List[str]							      =    timestep.cpu().item() if torch.is_tensor(A       ) else timestep
                    _UpperCAmelCase     :  Any							      =    self._index_counter[timestep_int]

             return indices[pos].item()




      @property
      def      _A	(       self						:				List[str]       ):
             # 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      _A	(       self						:				Optional[Any]						,					A						:				torch.FloatTensor						,					A						:				Union[float, torch.FloatTensor]						,					):
             _UpperCAmelCase     :  List[str]							      =    self.index_for_timestep(A       )

             _UpperCAmelCase     :  int							      =    self.sigmas[step_index]
             _UpperCAmelCase     :  Union[str, Any]							      =    sample / ((sigma**2 + 1) ** 0.5)
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				int						,					A						:				Union[str, torch.device] = None						,					A						:				Optional[int] = None						,					):
             _UpperCAmelCase     :  str							      =    num_inference_steps

             _UpperCAmelCase     :  Union[str, Any]							      =    num_train_timesteps or self.config.num_train_timesteps

             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                    _UpperCAmelCase     :  List[str]							      =    np.linspace(0						,					num_train_timesteps - 1						,					A						,					dtype=A       )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                    _UpperCAmelCase     :  Union[str, Any]							      =    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
                    _UpperCAmelCase     :  Tuple							      =    (np.arange(0						,					A       ) * step_ratio).round()[::-1].copy().astype(A       )
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _UpperCAmelCase     :  Tuple							      =    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
                    _UpperCAmelCase     :  Optional[Any]							      =    (np.arange(A						,					0						,					-step_ratio       )).round().copy().astype(A       )
                    timesteps -= 1
             else:
                    raise ValueError(
                        F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""       )

             _UpperCAmelCase     :  Dict							      =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
             _UpperCAmelCase     :  List[Any]							      =    np.log(A       )
             _UpperCAmelCase     :  int							      =    np.interp(A						,					np.arange(0						,					len(A       )       )						,					A       )

             if self.config.use_karras_sigmas:
                    _UpperCAmelCase     :  List[str]							      =    self._convert_to_karras(in_sigmas=A						,					num_inference_steps=self.num_inference_steps       )
                    _UpperCAmelCase     :  str							      =    np.array([self._sigma_to_t(A						,					A       ) for sigma in sigmas]       )

             _UpperCAmelCase     :  Optional[Any]							      =    np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
             _UpperCAmelCase     :  List[str]							      =    torch.from_numpy(A       ).to(device=A       )
             _UpperCAmelCase     :  Tuple							      =    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

             _UpperCAmelCase     :  Optional[Any]							      =    torch.from_numpy(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

             if str(A       ).startswith("mps"       ):
                    # mps does not support float64
                    _UpperCAmelCase     :  Union[str, Any]							      =    timesteps.to(A						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  List[str]							      =    timesteps.to(device=A       )

             # empty dt and derivative
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Optional[Any]							      =    None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             _UpperCAmelCase     :  int							      =    defaultdict(A       )




      def      _A	(       self						:				Tuple						,					A						:				Optional[int]						,					A						:				Tuple       ):
             # get log sigma
             _UpperCAmelCase     :  Dict							      =    np.log(A       )

             # get distribution
             _UpperCAmelCase     :  Union[str, Any]							      =    log_sigma - log_sigmas[:, np.newaxis]

             # get sigmas range
             _UpperCAmelCase     :  Union[str, Any]							      =    np.cumsum((dists >= 0)						,					axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
             _UpperCAmelCase     :  List[Any]							      =    low_idx + 1

             _UpperCAmelCase     :  List[Any]							      =    log_sigmas[low_idx]
             _UpperCAmelCase     :  Optional[Any]							      =    log_sigmas[high_idx]

             # interpolate sigmas
             _UpperCAmelCase     :  Optional[Any]							      =    (low - log_sigma) / (low - high)
             _UpperCAmelCase     :  List[str]							      =    np.clip(A						,					0						,					1       )

             # transform interpolation to time range
             _UpperCAmelCase     :  Tuple							      =    (1 - w) * low_idx + w * high_idx
             _UpperCAmelCase     :  Optional[Any]							      =    t.reshape(sigma.shape       )
             return t




      def      _A	(       self						:				Tuple						,					A						:				torch.FloatTensor						,					A						:				List[str]       ):
             _UpperCAmelCase     :  float							      =    in_sigmas[-1].item()
             _UpperCAmelCase     :  float							      =    in_sigmas[0].item()

             _UpperCAmelCase     :  Union[str, Any]							      =    7.0  # 7.0 is the value used in the paper
             _UpperCAmelCase     :  List[Any]							      =    np.linspace(0						,					1						,					A       )
             _UpperCAmelCase     :  List[Any]							      =    sigma_min ** (1 / rho)
             _UpperCAmelCase     :  Tuple							      =    sigma_max ** (1 / rho)
             _UpperCAmelCase     :  List[Any]							      =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
             return sigmas




      @property
      def      _A	(       self						:				List[Any]       ):
             return self.dt is None




      def      _A	(       self						:				Tuple						,					A						:				Union[torch.FloatTensor, np.ndarray]						,					A						:				Union[float, torch.FloatTensor]						,					A						:				Union[torch.FloatTensor, np.ndarray]						,					A						:				bool = True						,					):
             _UpperCAmelCase     :  Dict							      =    self.index_for_timestep(A       )

             # advance index counter by 1
             _UpperCAmelCase     :  int							      =    timestep.cpu().item() if torch.is_tensor(A       ) else timestep
             self._index_counter[timestep_int] += 1

             if self.state_in_first_order:
                    _UpperCAmelCase     :  Any							      =    self.sigmas[step_index]
                    _UpperCAmelCase     :  Any							      =    self.sigmas[step_index + 1]
             else:
                    # 2nd order / Heun's method
                    _UpperCAmelCase     :  Optional[int]							      =    self.sigmas[step_index - 1]
                    _UpperCAmelCase     :  List[str]							      =    self.sigmas[step_index]

             # currently only gamma=0 is supported. This usually works best anyways.
             # We can support gamma in the future but then need to scale the timestep before
             # passing it to the model which requires a change in API
             _UpperCAmelCase     :  Union[str, Any]							      =    0
             _UpperCAmelCase     :  str							      =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _UpperCAmelCase     :  str							      =    sigma_hat if self.state_in_first_order else sigma_next
                    _UpperCAmelCase     :  Tuple							      =    sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                    _UpperCAmelCase     :  int							      =    sigma_hat if self.state_in_first_order else sigma_next
                    _UpperCAmelCase     :  str							      =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                        sample / (sigma_input**2 + 1)
                    )
             elif self.config.prediction_type == "sample":
                    _UpperCAmelCase     :  Tuple							      =    model_output
             else:
                    raise ValueError(
                        F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

             if self.config.clip_sample:
                    _UpperCAmelCase     :  Optional[int]							      =    pred_original_sample.clamp(
                        -self.config.clip_sample_range						,					self.config.clip_sample_range       )

             if self.state_in_first_order:
                    # 2. Convert to an ODE derivative for 1st order
                    _UpperCAmelCase     :  Optional[Any]							      =    (sample - pred_original_sample) / sigma_hat
                    # 3. delta timestep
                    _UpperCAmelCase     :  List[str]							      =    sigma_next - sigma_hat

                    # store for 2nd order step
                    _UpperCAmelCase     :  Optional[int]							      =    derivative
                    _UpperCAmelCase     :  List[str]							      =    dt
                    _UpperCAmelCase     :  int							      =    sample
             else:
                    # 2. 2nd order / Heun's method
                    _UpperCAmelCase     :  str							      =    (sample - pred_original_sample) / sigma_next
                    _UpperCAmelCase     :  Optional[int]							      =    (self.prev_derivative + derivative) / 2

                    # 3. take prev timestep & sample
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.dt
                    _UpperCAmelCase     :  Optional[Any]							      =    self.sample

                    # free dt and derivative
                    # Note, this puts the scheduler in "first order mode"
                    _UpperCAmelCase     :  List[str]							      =    None
                    _UpperCAmelCase     :  str							      =    None
                    _UpperCAmelCase     :  List[str]							      =    None

             _UpperCAmelCase     :  Any							      =    sample + derivative * dt

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




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

             _UpperCAmelCase     :  Optional[Any]							      =    [self.index_for_timestep(A						,					A       ) for t in timesteps]

             _UpperCAmelCase     :  Any							      =    sigmas[step_indices].flatten()
             while len(sigma.shape       ) < len(original_samples.shape       ):
                    _UpperCAmelCase     :  Dict							      =    sigma.unsqueeze(-1       )

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





      def __len__(       self						:				List[str]       ):
             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 MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    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_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    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      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    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
             _UpperCAmelCase     :  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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    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
             _UpperCAmelCase     :  List[str]							      =    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
             _UpperCAmelCase     :  Any							      =    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"],
                 )						,					)

