




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps









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

import yaml


class 							__UpperCamelCase			( yaml.SafeLoader  ):





				'''simple docstring'''



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






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											lowerCAmelCase   =		super().construct_mapping(UpperCAmelCase_       , deep=UpperCAmelCase_						)
											self._check_no_duplicates_on_constructed_node(UpperCAmelCase_						)
											return mapping




def       UpperCAmelCase       (     _snake_case							):
							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(_snake_case							)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										={"""train_eval_index"""}  # train-eval-index in the YAML metadata



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											with open(UpperCAmelCase_       , 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(UpperCAmelCase_						)
											else:
																		return cls()



				def       __snake_case		( self       , UpperCAmelCase_						):
											if path.exists():
																		with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																									lowerCAmelCase   =		readme_file.read()
											else:
																		lowerCAmelCase   =		None
											lowerCAmelCase   =		self._to_readme(UpperCAmelCase_						)
											with open(UpperCAmelCase_       , '''w'''       , encoding='''utf-8'''						) as readme_file:
																		readme_file.write(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_ = None						):
											if readme_content is not None:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(UpperCAmelCase_						)
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n''' + content
											else:
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n'''
											return full_content



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											lowerCAmelCase   =		yaml.load(UpperCAmelCase_       , 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(**UpperCAmelCase_						)






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




UpperCAmelCase_						={
    """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

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

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



