




'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		(PNDMScheduler,)
				snake_case_      =		(("num_inference_steps", 50),)


				def   A_ (		self    :					Tuple       ,				**lowercase_    :					Tuple					):
									snake_case_									=				{
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**lowercase_					)
									return config


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[int]=0       ,				**lowercase_    :					int					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config(**lowercase_					)
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)
														# copy over dummy past residuals
														snake_case_									=				dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase_					)
																			snake_case_									=				scheduler_class.from_pretrained(lowercase_					)
																			new_scheduler.set_timesteps(lowercase_					)
																			# copy over dummy past residuals
																			snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

														assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

														assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"


				def   A_ (		self    :					Any					):
									pass


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict=0       ,				**lowercase_    :					Optional[int]					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)

														# copy over dummy past residuals (must be after setting timesteps)
														snake_case_									=				dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase_					)
																			snake_case_									=				scheduler_class.from_pretrained(lowercase_					)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(lowercase_					)

																			# copy over dummy past residual (must be after setting timesteps)
																			snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

														assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

														assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"


				def   A_ (		self    :					Dict       ,				**lowercase_    :					str					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(**lowercase_					)
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				10
									snake_case_									=				self.dummy_model()
									snake_case_									=				self.dummy_sample_deter
									scheduler.set_timesteps(lowercase_					)

									for i, t in enumerate(scheduler.prk_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									return sample


				def   A_ (		self    :					Dict					):
									snake_case_									=				dict(self.forward_default_kwargs					)

									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														snake_case_									=				self.dummy_sample
														snake_case_									=				0.1 * sample

														if num_inference_steps is not None and hasattr(lowercase_       ,				'''set_timesteps'''					):
																			scheduler.set_timesteps(lowercase_					)
														elif num_inference_steps is not None and not hasattr(lowercase_       ,				'''set_timesteps'''					):
																			snake_case_									=				num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_prk(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

														self.assertEqual(output_a.shape       ,				sample.shape					)
														self.assertEqual(output_a.shape       ,				output_a.shape					)

														snake_case_									=				scheduler.step_plms(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_plms(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

														self.assertEqual(output_a.shape       ,				sample.shape					)
														self.assertEqual(output_a.shape       ,				output_a.shape					)


				def   A_ (		self    :					List[Any]					):
									for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=lowercase_					)

									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(steps_offset=1					)
									snake_case_									=				scheduler_class(**lowercase_					)
									scheduler.set_timesteps(10					)
									assert torch.equal(
									    scheduler.timesteps       ,				torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)       ,				)


				def   A_ (		self    :					Any					):
									for beta_start, beta_end in zip([0.0001, 0.001]       ,				[0.002, 0.02]					):
														self.check_over_configs(beta_start=lowercase_       ,				beta_end=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=lowercase_					)


				def   A_ (		self    :					str					):
									for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=lowercase_					)


				def   A_ (		self    :					str					):
									for t in [1, 5, 10]:
														self.check_over_forward(time_step=lowercase_					)


				def   A_ (		self    :					str					):
									for t, num_inference_steps in zip([1, 5, 10]       ,				[10, 50, 100]					):
														self.check_over_forward(num_inference_steps=lowercase_					)


				def   A_ (		self    :					Any					):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									snake_case_									=				27

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.dummy_sample
														snake_case_									=				0.1 * sample

														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														scheduler.set_timesteps(lowercase_					)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]					):
																			snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample


				def   A_ (		self    :					Tuple					):
									with self.assertRaises(lowercase_					):
														snake_case_									=				self.scheduler_classes[0]
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														scheduler.step_plms(self.dummy_sample       ,				1       ,				self.dummy_sample					).prev_sample


				def   A_ (		self    :					str					):
									snake_case_									=				self.full_loop()
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 198.1318					) < 1e-2
									assert abs(result_mean.item() - 0.2580					) < 1e-3


				def   A_ (		self    :					Any					):
									snake_case_									=				self.full_loop(prediction_type='''v_prediction'''					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 67.3986					) < 1e-2
									assert abs(result_mean.item() - 0.0878					) < 1e-3


				def   A_ (		self    :					Optional[Any]					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 230.0399					) < 1e-2
									assert abs(result_mean.item() - 0.2995					) < 1e-3


				def   A_ (		self    :					str					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 186.9482					) < 1e-2
									assert abs(result_mean.item() - 0.2434					) < 1e-3


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)
