



'''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


A__  :					str				= logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class       snake_case__		(  _SCREAMING_SNAKE_CASE							):
 A__							=      42
 A__							=      None





def    a_					(						_UpperCAmelCase  :	Optional[int]      ,_UpperCAmelCase  :	Dict=0.9_9_9      ,_UpperCAmelCase  :	Optional[Any]="cosine"      ,)  ->   Dict:


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase  :	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  :	Union[str, Any]							):
                  return math.exp(t * -1_2.0							)

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

      __snake_case      :					Tuple						=  []
      for i in range(lowercase_							):
            __snake_case      :					Union[str, Any]						=  i / num_diffusion_timesteps
            __snake_case      :					List[Any]						=  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(lowercase_							) / alpha_bar_fn(lowercase_							)      ,lowercase_							)							)
      return torch.tensor(lowercase_      ,dtype=torch.floataa							)





class       snake_case__		(  _SCREAMING_SNAKE_CASE   ,			_SCREAMING_SNAKE_CASE							):
 A__							=      [e.name for e in KarrasDiffusionSchedulers]
 A__							=      1
 @register_to_config
 def __init__(					self		:				Tuple	,			__a		:				int = 1000	,			__a		:				float = 0.0_0_0_1	,			__a		:				float = 0.0_2	,			__a		:				str = "linear"	,			__a		:				Optional[Union[np.ndarray, List[float]]] = None	,			__a		:				str = "epsilon"	,			__a		:				str = "linspace"	,			__a		:				int = 0	,			)	->					Any:







       '''simple docstring'''
       if trained_betas is not None:
             __snake_case      :					Tuple						=  torch.tensor(__a	,			dtype=torch.floataa   )
       elif beta_schedule == "linear":
             __snake_case      :					Optional[int]						=  torch.linspace(__a	,			__a	,			__a	,			dtype=torch.floataa   )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __snake_case      :					Dict						=  (
                 torch.linspace(beta_start**0.5	,			beta_end**0.5	,			__a	,			dtype=torch.floataa   ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __snake_case      :					int						=  betas_for_alpha_bar(__a   )
       else:
             raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

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

       __snake_case      :					Optional[Any]						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       __snake_case      :					int						=  np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
       __snake_case      :					str						=  torch.from_numpy(__a   )

       # setable values
       __snake_case      :					Any						=  None
       __snake_case      :					List[Any]						=  np.linspace(0	,			num_train_timesteps - 1	,			__a	,			dtype=__a   )[::-1].copy()
       __snake_case      :					Union[str, Any]						=  torch.from_numpy(__a   )
       __snake_case      :					str						=  False
 @property
 def A_					(					self		:				Tuple   )	->					Union[str, Any]:







       '''simple docstring'''
       # 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		:				int	,			__a		:				torch.FloatTensor	,			__a		:				Union[float, torch.FloatTensor]   )	->					Tuple:







       '''simple docstring'''
       if isinstance(__a	,			torch.Tensor   ):
             __snake_case      :					int						=  timestep.to(self.timesteps.device   )
       __snake_case      :					int						=  (self.timesteps == timestep).nonzero().item()
       __snake_case      :					Optional[Any]						=  self.sigmas[step_index]
       __snake_case      :					List[str]						=  sample / ((sigma**2 + 1) ** 0.5)
       __snake_case      :					Dict						=  True
       return sample
 def A_					(					self		:				Any	,			__a		:				int	,			__a		:				Union[str, torch.device] = None   )	->					Optional[int]:







       '''simple docstring'''
       __snake_case      :					List[str]						=  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":
             __snake_case      :					List[str]						=  np.linspace(0	,			self.config.num_train_timesteps - 1	,			__a	,			dtype=__a   )[
                 ::-1
             ].copy()
       elif self.config.timestep_spacing == "leading":
             __snake_case      :					Optional[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
             __snake_case      :					Dict						=  (np.arange(0	,			__a   ) * step_ratio).round()[::-1].copy().astype(__a   )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             __snake_case      :					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
             __snake_case      :					Tuple						=  (np.arange(self.config.num_train_timesteps	,			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\'.'''   )

       __snake_case      :					Optional[Any]						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       __snake_case      :					List[Any]						=  np.interp(__a	,			np.arange(0	,			len(__a   )   )	,			__a   )
       __snake_case      :					int						=  np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       __snake_case      :					Dict						=  torch.from_numpy(__a   ).to(device=__a   )
       if str(__a   ).startswith('mps'   ):
             # mps does not support float64
             __snake_case      :					Optional[Any]						=  torch.from_numpy(__a   ).to(__a	,			dtype=torch.floataa   )
       else:
             __snake_case      :					Optional[Any]						=  torch.from_numpy(__a   ).to(device=__a   )
 def A_					(					self		:				int	,			__a		:				torch.FloatTensor	,			__a		:				Union[float, torch.FloatTensor]	,			__a		:				torch.FloatTensor	,			__a		:				Optional[torch.Generator] = None	,			__a		:				bool = True	,			)	->					int:







       '''simple docstring'''

       if (
           isinstance(__a	,			__a   )
           or isinstance(__a	,			torch.IntTensor   )
           or isinstance(__a	,			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(__a	,			torch.Tensor   ):
             __snake_case      :					Optional[int]						=  timestep.to(self.timesteps.device   )

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

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             __snake_case      :					str						=  sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             __snake_case      :					List[Any]						=  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`'''   )

       __snake_case      :					str						=  self.sigmas[step_index]
       __snake_case      :					str						=  self.sigmas[step_index + 1]
       __snake_case      :					int						=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       __snake_case      :					List[Any]						=  (sigma_to**2 - sigma_up**2) ** 0.5

       # 2. Convert to an ODE derivative
       __snake_case      :					Optional[int]						=  (sample - pred_original_sample) / sigma

       __snake_case      :					Tuple						=  sigma_down - sigma

       __snake_case      :					int						=  sample + derivative * dt

       __snake_case      :					int						=  model_output.device
       __snake_case      :					Any						=  randn_tensor(model_output.shape	,			dtype=model_output.dtype	,			device=__a	,			generator=__a   )

       __snake_case      :					Tuple						=  prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=__a	,			pred_original_sample=__a   )
 def A_					(					self		:				List[Any]	,			__a		:				torch.FloatTensor	,			__a		:				torch.FloatTensor	,			__a		:				torch.FloatTensor	,			)	->					str:







       '''simple docstring'''
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       __snake_case      :					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(__a   ):
             # mps does not support float64
             __snake_case      :					Union[str, Any]						=  self.timesteps.to(original_samples.device	,			dtype=torch.floataa   )
             __snake_case      :					str						=  timesteps.to(original_samples.device	,			dtype=torch.floataa   )
       else:
             __snake_case      :					int						=  self.timesteps.to(original_samples.device   )
             __snake_case      :					Dict						=  timesteps.to(original_samples.device   )

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

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

       __snake_case      :					str						=  original_samples + noise * sigma
       return noisy_samples





 def __len__(					self		:				Optional[Any]   )	->					List[Any]:







       '''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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       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,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







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







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case       ,				'''image_mean''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''image_std''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_resize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)






