





'''simple docstring'''







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					(DDIMParallelScheduler,)
     _UpperCAmelCase										=					(("eta", 0.0), ("num_inference_steps", 50))







     def     UpperCamelCase			( self    ,   **A__    ) ->	Dict:
      snake_case				=			{
          '''num_train_timesteps''': 10_00,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
          '''clip_sample''': True,
      }

      config.update(**A__    )
      return config







     def     UpperCamelCase			( self    ,   **A__    ) ->	int:
      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config(**A__    )
      snake_case				=			scheduler_class(**A__    )

      snake_case				,		snake_case				=			10, 0.0

      snake_case				=			self.dummy_model()
      snake_case				=			self.dummy_sample_deter

      scheduler.set_timesteps(A__    )

      for t in scheduler.timesteps:
       snake_case				=			model(A__    ,   A__    )
       snake_case				=			scheduler.step(A__    ,   A__    ,   A__    ,   A__    ).prev_sample

      return sample







     def     UpperCamelCase			( self    ) ->	Tuple:




      for timesteps in [1_00, 5_00, 10_00]:
       self.check_over_configs(num_train_timesteps=A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:




      for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=A__    )

      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config(steps_offset=1    )
      snake_case				=			scheduler_class(**A__    )
      scheduler.set_timesteps(5    )
      assert torch.equal(scheduler.timesteps    ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )







     def     UpperCamelCase			( self    ) ->	Tuple:




      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,   [0.0_0_2, 0.0_2, 0.2, 2]    ):
       self.check_over_configs(beta_start=A__    ,   beta_end=A__    )







     def     UpperCamelCase			( self    ) ->	List[str]:




      for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=A__    )







     def     UpperCamelCase			( self    ) ->	str:




      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:




      for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=A__    )







     def     UpperCamelCase			( self    ) ->	Any:




      for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=A__    )







     def     UpperCamelCase			( self    ) ->	str:




      for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=A__    )







     def     UpperCamelCase			( self    ) ->	int:
      self.check_over_configs(thresholding=A__    )
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(
            thresholding=A__    ,   prediction_type=A__    ,   sample_max_value=A__    ,   )







     def     UpperCamelCase			( self    ) ->	List[Any]:




      for t in [1, 10, 49]:
       self.check_over_forward(time_step=A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:




      for t, num_inference_steps in zip([1, 10, 50]    ,   [10, 50, 5_00]    ):
       self.check_over_forward(time_step=A__    ,   num_inference_steps=A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:




      for t, eta in zip([1, 10, 49]    ,   [0.0, 0.5, 1.0]    ):
       self.check_over_forward(time_step=A__    ,   eta=A__    )







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config()
      snake_case				=			scheduler_class(**A__    )

      assert torch.sum(torch.abs(scheduler._get_variance(0    ,   0    ) - 0.0    )    ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,   4_00    ) - 0.1_4_7_7_1    )    ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,   9_60    ) - 0.3_2_4_6_0    )    ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(0    ,   0    ) - 0.0    )    ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,   4_86    ) - 0.0_0_9_7_9    )    ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,   9_98    ) - 0.0_2    )    ) < 1e-5







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config()
      snake_case				=			scheduler_class(**A__    )

      snake_case				,		snake_case				=			10, 0.0
      scheduler.set_timesteps(A__    )

      snake_case				=			self.dummy_model()
      snake_case				=			self.dummy_sample_deter
      snake_case				=			self.dummy_sample_deter + 0.1
      snake_case				=			self.dummy_sample_deter - 0.1

      snake_case				=			samplea.shape[0]
      snake_case				=			torch.stack([samplea, samplea, samplea]    ,   dim=0    )
      snake_case				=			torch.arange(A__    )[0:3, None].repeat(1    ,   A__    )

      snake_case				=			model(samples.flatten(0    ,   1    )    ,   timesteps.flatten(0    ,   1    )    )
      snake_case				=			scheduler.batch_step_no_noise(A__    ,   timesteps.flatten(0    ,   1    )    ,   samples.flatten(0    ,   1    )    ,   A__    )

      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_1_4_7.7_9_0_4    ) < 1e-2
      assert abs(result_mean.item() - 0.4_9_8_2    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.full_loop()

      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_7_2.0_0_6_7    ) < 1e-2
      assert abs(result_mean.item() - 0.2_2_3_9_6_7    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.full_loop(prediction_type='''v_prediction'''    )

      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 5_2.5_3_0_2    ) < 1e-2
      assert abs(result_mean.item() - 0.0_6_8_4    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.full_loop(set_alpha_to_one=A__    ,   beta_start=0.0_1    )
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_4_9.8_2_9_5    ) < 1e-2
      assert abs(result_mean.item() - 0.1_9_5_1    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.full_loop(set_alpha_to_one=A__    ,   beta_start=0.0_1    )
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_4_9.0_7_8_4    ) < 1e-2
      assert abs(result_mean.item() - 0.1_9_4_1    ) < 1e-3








'''simple docstring'''







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

import yaml
class 			_lowercase						(       yaml.SafeLoader					):







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







     def     UpperCamelCase			( self    ,   A__    ,   A__=False    ) ->	List[Any]:
      snake_case				=			super().construct_mapping(A__    ,   deep=A__    )
      self._check_no_duplicates_on_constructed_node(A__    )
      return mapping




def    __UpperCamelCase	(	a    : str     ) ->Tuple[Optional[str], str]:
 snake_case				=			list(readme_content.splitlines()     )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  snake_case				=			full_content[1:].index('''---'''     ) + 1
  snake_case				=			'''\n'''.join(full_content[1:sep_idx]     )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

 return None, "\n".join(a     )
class 			_lowercase						(       __a					):
     # class attributes
     _UpperCAmelCase										=					{'''train_eval_index'''}  # train-eval-index in the YAML metadata







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
       snake_case				,		snake_case				=			_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    ,   A__    ) ->	str:
      if path.exists():
       with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
        snake_case				=			readme_file.read()
      else:
       snake_case				=			None
      snake_case				=			self._to_readme(A__    )
      with open(A__    ,   '''w'''    ,   encoding='''utf-8'''    ) as readme_file:
       readme_file.write(A__    )







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







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      snake_case				=			yaml.load(A__    ,   Loader=_NoDuplicateSafeLoader    ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      snake_case				=			{
          (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    ) ->	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=A__    ,   allow_unicode=A__    ,   encoding='''utf-8'''    ,   ).decode('''utf-8'''    )




_lowercase       						=			{
    '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

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

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

