






"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					42
    __magic_name__   					=					42





    def __init__(							self     : str    ,		A     : UNetaDModel    ,		A     : KarrasVeScheduler      ):


      '''simple docstring'''




      super().__init__()
      self.register_modules(unet=A    ,		scheduler=A      )





    @torch.no_grad()
    def __call__(							self     : Dict    ,		A     : int = 1    ,		A     : int = 5_0    ,		A     : Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,		A     : Optional[str] = "pil"    ,		A     : bool = True    ,		**A     : str    ,		):


      '''simple docstring'''




      a					:      Any    =       self.unet.config.sample_size
      a					:      Dict    =       (batch_size, 3, img_size, img_size)

      a					:      Optional[Any]    =       self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      a					:      Any    =       randn_tensor(A    ,		generator=A    ,		device=self.device      ) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(A      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
        # here sigma_t == t_i from the paper
        a					:      List[str]    =       self.scheduler.schedule[t]
        a					:      Tuple    =       self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        a					:      int    =       self.scheduler.add_noise_to_input(A    ,		A    ,		generator=A      )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        a					:      Optional[int]    =       (sigma_hat / 2) * model((sample_hat + 1) / 2    ,		sigma_hat / 2      ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        a					:      Dict    =       self.scheduler.step(A    ,		A    ,		A    ,		A      )

        if sigma_prev != 0:
          # 6. Apply 2nd order correction
          # The model inputs and output are adjusted by following eq. (213) in [1].
          a					:      List[Any]    =       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,		sigma_prev / 2      ).sample
          a					:      Union[str, Any]    =       self.scheduler.step_correct(
              A    ,		A    ,		A    ,		A    ,		step_output.prev_sample    ,		step_output['derivative']    ,		)
        a					:      Any    =       step_output.prev_sample

      a					:      Union[str, Any]    =       (sample / 2 + 0.5).clamp(0    ,		1      )
      a					:      str    =       sample.cpu().permute(0    ,		2    ,		3    ,		1      ).numpy()
      if output_type == "pil":
        a					:      Dict    =       self.numpy_to_pil(A      )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=A      )












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




