# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 		a__						(					A__					):

					def __init__(    self      :       Dict,_A      :       List[Any],_A      :       Any    ):







							"""simple docstring"""






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






					@torch.no_grad()
					def __call__(    self      :       Dict,_A      :       int = 1,_A      :       Optional[torch.Generator] = None,_A      :       int = 50,_A      :       Optional[str] = "pil",_A      :       bool = True,**_A      :       str,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),generator=_A,)
							SCREAMING_SNAKE_CASE_  :		int				      =    image.to(self.device    )

							# set step values
							self.scheduler.set_timesteps(_A    )

							for t in self.progress_bar(self.scheduler.timesteps    ):
									# 1. predict noise model_output
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.unet(_A,_A    ).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
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.scheduler.step(_A,_A,_A    ).prev_sample

							SCREAMING_SNAKE_CASE_  :		str				      =    (image / 2 + 0.5).clamp(0,1    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image.cpu().permute(0,2,3,1    ).numpy()
							if output_type == "pil":
									SCREAMING_SNAKE_CASE_  :		Dict				      =    self.numpy_to_pil(_A    )

							if not return_dict:
									return (image,), "This is a local test"

							return ImagePipelineOutput(images=_A    ), "This is a local test"

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

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [self.constructed_objects[key_node] for key_node, _ in node.value]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(_A    ) if isinstance(_A,_A    ) else key for key in keys]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Counter(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
									raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    list(readme_content.splitlines()	)
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    full_content[1:].index("---"	) + 1
				SCREAMING_SNAKE_CASE_  :		int				      =    "\n".join(full_content[1:sep_idx]	)
				return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

		return None, "\n".join(lowerCAmelCase	)






class 		a__						(					A__					):
					# class attributes
					A	       =							{'train_eval_index'}  # train-eval-index in the YAML metadata

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






							with open(_A,encoding="utf-8"    ) as readme_file:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    _split_yaml_from_readme(readme_file.read()    )
							if yaml_string is not None:
									return cls.from_yaml_string(_A    )
							else:
									return cls()

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






							if path.exists():
									with open(_A,encoding="utf-8"    ) as readme_file:
											SCREAMING_SNAKE_CASE_  :		int				      =    readme_file.read()
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		int				      =    self._to_readme(_A    )
							with open(_A,"w",encoding="utf-8"    ) as readme_file:
									readme_file.write(_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

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






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							return yaml.safe_dump(
							    {
							        (key.replace("_","-"    ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    },sort_keys=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	List[Any]       =  {
    '''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

		__lowerCamelCase					:	List[Any]       =  ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		__lowerCamelCase					:	Dict       =  ap.parse_args()

		__lowerCamelCase					:	List[Any]       =  Path(args.readme_filepath)
		__lowerCamelCase					:	Optional[int]       =  DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)
