



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

import yaml





class    lowerCamelCase__  (						yaml.SafeLoader):
     """simple docstring"""



     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a=False    ):






           '''simple docstring'''



           lowerCamelCase								=		super().construct_mapping(__a				,			deep=__a    )
           self._check_no_duplicates_on_constructed_node(__a    )
           return mapping



def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

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





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			{'train_eval_index'}  # train-eval-index in the YAML metadata







     @classmethod
     def 						_a      (cls				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a = None    ):






           '''simple docstring'''



           if readme_content is not None:
                 lowerCamelCase			,							lowerCamelCase								=		_split_yaml_from_readme(__a    )
                 lowerCamelCase								=		"---\n" + self.to_yaml_string() + "---\n" + content
           else:
                 lowerCamelCase								=		"---\n" + self.to_yaml_string() + "---\n"
           return full_content







     @classmethod
     def 						_a      (cls				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		yaml.load(__a				,			Loader=_NoDuplicateSafeLoader    ) or {}

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







     def 						_a      (self    ):






           '''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"    )



a_						:     int	     =		{
    '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

     a_						:     int	     =		ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
     ap.add_argument('readme_filepath')
     a_						:     Optional[int]	     =		ap.parse_args()

     a_						:     str	     =		Path(args.readme_filepath)
     a_						:     List[str]	     =		DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2