
"""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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=0.999 ,   UpperCAmelCase="cosine" ,   )						->Dict:

   """simple docstring"""



   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase     ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCAmelCase     ):
         return math.exp(t * -12.0     )

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

   a_						   =     []
   for i in range(UpperCAmelCase     ):
      a_						   =     i / num_diffusion_timesteps
      a_						   =     (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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
   a_					:		Any     					=     [e.name for e in KarrasDiffusionSchedulers]
   a_					:		str     					=     2







   @register_to_config
   def __init__( self       ,							__UpperCAmelCase = 10_00       ,							__UpperCAmelCase = 0.00_085       ,							__UpperCAmelCase = 0.012       ,							__UpperCAmelCase = "linear"       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = "epsilon"       ,							__UpperCAmelCase = "linspace"       ,							__UpperCAmelCase = 0       ,							)   ->Tuple:
      if trained_betas is not None:
         a_						   =     torch.tensor(__UpperCAmelCase       ,							dtype=torch.floataa)
      elif beta_schedule == "linear":
         a_						   =     torch.linspace(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							dtype=torch.floataa)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         a_						   =     (
             torch.linspace(beta_start**0.5       ,							beta_end**0.5       ,							__UpperCAmelCase       ,							dtype=torch.floataa) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         a_						   =     betas_for_alpha_bar(__UpperCAmelCase)
      else:
         raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''')

      a_						   =     1.0 - self.betas
      a_						   =     torch.cumprod(self.alphas       ,							dim=0)

      #  set all values
      self.set_timesteps(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=None)   ->Union[str, Any]:
      if schedule_timesteps is None:
         a_						   =     self.timesteps

      a_						   =     (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:
         a_						   =     1 if len(__UpperCAmelCase) > 1 else 0
      else:
         a_						   =     timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase) else timestep
         a_						   =     self._index_counter[timestep_int]

      return indices[pos].item()







   @property
   def    UpperCAmelCase__	( 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    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							)   ->torch.FloatTensor:
      a_						   =     self.index_for_timestep(__UpperCAmelCase)

      if self.state_in_first_order:
         a_						   =     self.sigmas[step_index]
      else:
         a_						   =     self.sigmas_interpol[step_index - 1]

      a_						   =     sample / ((sigma**2 + 1) ** 0.5)
      return sample







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							)   ->List[Any]:
      a_						   =     num_inference_steps

      a_						   =     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":
         a_						   =     np.linspace(0       ,							num_train_timesteps - 1       ,							__UpperCAmelCase       ,							dtype=__UpperCAmelCase)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         a_						   =     num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         a_						   =     (np.arange(0       ,							__UpperCAmelCase) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase)
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         a_						   =     num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         a_						   =     (np.arange(__UpperCAmelCase       ,							0       ,							-step_ratio)).round().copy().astype(__UpperCAmelCase)
         timesteps -= 1
      else:
         raise ValueError(
             F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

      a_						   =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
      a_						   =     torch.from_numpy(np.log(__UpperCAmelCase)).to(__UpperCAmelCase)

      a_						   =     np.interp(__UpperCAmelCase       ,							np.arange(0       ,							len(__UpperCAmelCase))       ,							__UpperCAmelCase)
      a_						   =     np.concatenate([sigmas, [0.0]]).astype(np.floataa)
      a_						   =     torch.from_numpy(__UpperCAmelCase).to(device=__UpperCAmelCase)

      # compute up and down sigmas
      a_						   =     sigmas.roll(-1)
      a_						   =     0.0
      a_						   =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      a_						   =     (sigmas_next**2 - sigmas_up**2) ** 0.5
      a_						   =     0.0

      # compute interpolated sigmas
      a_						   =     sigmas.log().lerp(sigmas_down.log()       ,							0.5).exp()
      a_						   =     0.0

      # set sigmas
      a_						   =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
      a_						   =     torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
      a_						   =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
      a_						   =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

      if str(__UpperCAmelCase).startswith("mps"):
         # mps does not support float64
         a_						   =     torch.from_numpy(__UpperCAmelCase).to(__UpperCAmelCase       ,							dtype=torch.floataa)
      else:
         a_						   =     torch.from_numpy(__UpperCAmelCase).to(__UpperCAmelCase)

      a_						   =     self.sigma_to_t(__UpperCAmelCase).to(__UpperCAmelCase       ,							dtype=timesteps.dtype)
      a_						   =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])       ,							dim=-1).flatten()

      a_						   =     torch.cat([timesteps[:1], interleaved_timesteps])

      a_						   =     None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      a_						   =     defaultdict(__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->int:
      # get log sigma
      a_						   =     sigma.log()

      # get distribution
      a_						   =     log_sigma - self.log_sigmas[:, None]

      # get sigmas range
      a_						   =     dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
      a_						   =     low_idx + 1

      a_						   =     self.log_sigmas[low_idx]
      a_						   =     self.log_sigmas[high_idx]

      # interpolate sigmas
      a_						   =     (low - log_sigma) / (low - high)
      a_						   =     w.clamp(0       ,							1)

      # transform interpolation to time range
      a_						   =     (1 - w) * low_idx + w * high_idx
      a_						   =     t.view(sigma.shape)
      return t







   @property
   def    UpperCAmelCase__	( self)   ->Any:
      return self.sample is None







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = True       ,							)   ->Union[SchedulerOutput, Tuple]:
      a_						   =     self.index_for_timestep(__UpperCAmelCase)

      # advance index counter by 1
      a_						   =     timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
         a_						   =     self.sigmas[step_index]
         a_						   =     self.sigmas_interpol[step_index]
         a_						   =     self.sigmas_up[step_index]
         a_						   =     self.sigmas_down[step_index - 1]
      else:
         # 2nd order / KPDM2's method
         a_						   =     self.sigmas[step_index - 1]
         a_						   =     self.sigmas_interpol[step_index - 1]
         a_						   =     self.sigmas_up[step_index - 1]
         a_						   =     self.sigmas_down[step_index - 1]

      # 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
      a_						   =     0
      a_						   =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      a_						   =     model_output.device
      a_						   =     randn_tensor(model_output.shape       ,							dtype=model_output.dtype       ,							device=__UpperCAmelCase       ,							generator=__UpperCAmelCase)

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         a_						   =     sigma_hat if self.state_in_first_order else sigma_interpol
         a_						   =     sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         a_						   =     sigma_hat if self.state_in_first_order else sigma_interpol
         a_						   =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**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`''')

      if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         a_						   =     (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         a_						   =     sigma_interpol - sigma_hat

         # store for 2nd order step
         a_						   =     sample
         a_						   =     dt
         a_						   =     sample + derivative * dt
      else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         a_						   =     (sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         a_						   =     sigma_down - sigma_hat

         a_						   =     self.sample
         a_						   =     None

         a_						   =     sample + derivative * dt
         a_						   =     prev_sample + noise * sigma_up

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							)   ->torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      a_						   =     self.sigmas.to(device=original_samples.device       ,							dtype=original_samples.dtype)
      if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase):
         # mps does not support float64
         a_						   =     self.timesteps.to(original_samples.device       ,							dtype=torch.floataa)
         a_						   =     timesteps.to(original_samples.device       ,							dtype=torch.floataa)
      else:
         a_						   =     self.timesteps.to(original_samples.device)
         a_						   =     timesteps.to(original_samples.device)

      a_						   =     [self.index_for_timestep(__UpperCAmelCase       ,							__UpperCAmelCase) for t in timesteps]

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

      a_						   =     original_samples + noise * sigma
      return noisy_samples







   def __len__( self)   ->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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=10 ,   UpperCAmelCase=10 ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Union[str, Any]:

   """simple docstring"""



   a_						   =     []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(255 ,   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
      a_						   =     [Image.fromarray(np.moveaxis(UpperCAmelCase ,   0 ,   -1     )     ) for frame in video]

   if torchify:
      a_						   =     [torch.from_numpy(UpperCAmelCase     ) for frame in video]

   return video
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=False ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Optional[Any]:

   """simple docstring"""



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

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

   return video_inputs





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							)   ->str:
      a_						   =     size if size is not None else {"shortest_edge": 18}
      a_						   =     crop_size if crop_size is not None else {"height": 18, "width": 18}

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







   def    UpperCAmelCase__	( self)   ->Tuple:
      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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		int     					=     TvltImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     TvltImageProcessorTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->int:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_center_crop"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))







   def    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							Image.Image)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processor(__UpperCAmelCase       ,							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    UpperCAmelCase__	( self)   ->int:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							np.ndarray)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processor(__UpperCAmelCase       ,							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    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							torch.Tensor)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processor(__UpperCAmelCase       ,							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"],
          )       ,							)