
"""simple docstring"""

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class      _UpperCAmelCase							(		yaml.SafeLoader							):




      def     a_      (							self   ,       lowercase_							)						->					Dict:
          UpperCAmelCase									=					[self.constructed_objects[key_node] for key_node, _ in node.value]
          UpperCAmelCase									=					[tuple(lowercase_							) if isinstance(lowercase_   ,       lowercase_							) else key for key in keys]
          UpperCAmelCase									=					Counter(lowercase_							)
          UpperCAmelCase									=					[key for key in counter if counter[key] > 1]
          if duplicate_keys:
              raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_=False							)						->					Union[str, Any]:
          UpperCAmelCase									=					super().construct_mapping(lowercase_   ,       deep=lowercase_							)
          self._check_no_duplicates_on_constructed_node(lowercase_							)
          return mapping




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Tuple[Optional[str], str]:






    """simple docstring"""




    UpperCAmelCase									=					list(readme_content.splitlines() )
    if full_content and full_content[0] == "---" and "---" in full_content[1:]:
        UpperCAmelCase									=					full_content[1:].index('---' ) + 1
        UpperCAmelCase									=					'\n'.join(full_content[1:sep_idx] )
        return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

    return None, "\n".join(lowerCAmelCase )
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      # class attributes
      __SCREAMING_SNAKE_CASE   :							str							      =	{"train_eval_index"}  # train-eval-index in the YAML metadata




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					"DatasetMetadata":
          with open(lowercase_   ,       encoding='utf-8'							) as readme_file:
              UpperCAmelCase			,							UpperCAmelCase									=					_split_yaml_from_readme(readme_file.read()							)
          if yaml_string is not None:
              return cls.from_yaml_string(lowercase_							)
          else:
              return cls()




      def     a_      (							self   ,       lowercase_							)						->					Optional[Any]:
          if path.exists():
              with open(lowercase_   ,       encoding='utf-8'							) as readme_file:
                  UpperCAmelCase									=					readme_file.read()
          else:
              UpperCAmelCase									=					None
          UpperCAmelCase									=					self._to_readme(lowercase_							)
          with open(lowercase_   ,       'w'   ,       encoding='utf-8'							) as readme_file:
              readme_file.write(lowercase_							)




      def     a_      (							self   ,       lowercase_ = None							)						->					str:
          if readme_content is not None:
              UpperCAmelCase			,							UpperCAmelCase									=					_split_yaml_from_readme(lowercase_							)
              UpperCAmelCase									=					'---\n' + self.to_yaml_string() + '---\n' + content
          else:
              UpperCAmelCase									=					'---\n' + self.to_yaml_string() + '---\n'
          return full_content




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					"DatasetMetadata":
          UpperCAmelCase									=					yaml.load(lowercase_   ,       Loader=_NoDuplicateSafeLoader							) or {}

          # Convert the YAML keys to DatasetMetadata fields
          UpperCAmelCase									=					{
              (key.replace('-'   ,       '_'							) if key.replace('-'   ,       '_'							) in cls._FIELDS_WITH_DASHES else key): value
              for key, value in metadata_dict.items()
          }
          return cls(**lowercase_							)




      def     a_      (							self							)						->					str:
          return yaml.safe_dump(
              {
                  (key.replace('_'   ,       '-'							) if key in self._FIELDS_WITH_DASHES else key): value
                  for key, value in self.items()
              }   ,       sort_keys=lowercase_   ,       allow_unicode=lowercase_   ,       encoding='utf-8'   ,       ).decode('utf-8'							)

SCREAMING_SNAKE_CASE_    					=						{
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   SCREAMING_SNAKE_CASE_    					=						ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
   ap.add_argument('''readme_filepath''')
   SCREAMING_SNAKE_CASE_    					=						ap.parse_args()

   SCREAMING_SNAKE_CASE_    					=						Path(args.readme_filepath)
   SCREAMING_SNAKE_CASE_    					=						DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    					=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCAmelCase									=					DDIMScheduler.from_config(scheduler.config							)

          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          if not isinstance(lowercase_   ,       (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase									=					image.to(device=lowercase_   ,       dtype=lowercase_							)

          if isinstance(lowercase_   ,       lowercase_							) and len(lowercase_							) != batch_size:
              raise ValueError(
                  F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                  F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

          UpperCAmelCase									=					(image / 2 + 0.5).clamp(0   ,       1							)
          UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).numpy()
          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image, latent_timestep.item())

          return ImagePipelineOutput(images=lowercase_							)



