


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

import yaml






class     UpperCamelCase(  yaml.SafeLoader   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		super().construct_mapping(SCREAMING_SNAKE_CASE     ,    deep=SCREAMING_SNAKE_CASE						)
											self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE						)
											return mapping





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Tuple[Optional[str], str]:


						'''simple docstring'''






						__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(_lowerCAmelCase )






class     UpperCamelCase(  _a   ):
					# class attributes
					snake_case_		:				Union[str, Any]										=	{"""train_eval_index"""}  # train-eval-index in the YAML metadata


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Path						)			->	"DatasetMetadata":




											'''simple docstring'''

											with open(SCREAMING_SNAKE_CASE     ,    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(SCREAMING_SNAKE_CASE						)
											else:
																	return cls()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Path						)			->	Optional[int]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	str:




											'''simple docstring'''

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


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	"DatasetMetadata":




											'''simple docstring'''

											__snake_case				=		yaml.load(SCREAMING_SNAKE_CASE     ,    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(**SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	str:




											'''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=SCREAMING_SNAKE_CASE     ,    allow_unicode=SCREAMING_SNAKE_CASE     ,    encoding="utf-8"     ,    ).decode("utf-8"						)




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

	A							:					Any     						=     Path(args.readme_filepath)
	A							:					Union[str, Any]     						=     DatasetMetadata.from_readme(readme_filepath)
	print(dataset_metadata)
	dataset_metadata.to_readme(readme_filepath)









import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class     UpperCamelCase:


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	__snake_case				=		deepcopy(SCREAMING_SNAKE_CASE						)
											elif os.path.exists(SCREAMING_SNAKE_CASE						):
																	with io.open(SCREAMING_SNAKE_CASE     ,    "r"     ,    encoding="utf-8"						) as f:
																							__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)
											else:
																	try:
																							__snake_case				=		baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE						).decode("utf-8"						)
																							__snake_case				=		json.loads(SCREAMING_SNAKE_CASE						)
																	except (UnicodeDecodeError, AttributeError, ValueError):
																							raise ValueError(
																							    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''						)

											__snake_case				=		config

											self.set_stage_and_offload()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_value("zero_optimization.stage"     ,    -1						)

											# offload
											__snake_case				=		False
											if self.is_zeroa() or self.is_zeroa():
																	__snake_case				=		set(["cpu", "nvme"]						)
																	__snake_case				=		set(
																	    [
																	        self.get_value("zero_optimization.offload_optimizer.device"						),
																	        self.get_value("zero_optimization.offload_param.device"						),
																	    ]						)
																	if len(offload_devices & offload_devices_valid						) > 0:
																							__snake_case				=		True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											__snake_case				=		nodes.pop()
											for node in nodes:
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							return None, ds_key

											return config, ds_key


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Tuple=None						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.find_config_node(SCREAMING_SNAKE_CASE						)
											if config is None:
																	return default
											return config.get(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											for node in nodes:
																	__snake_case				=		config
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							if must_exist:
																													raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''						)
																							else:
																													return

        # if found remove it
											if parent_config is not None:
																	parent_config.pop(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else bool(SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else not bool(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											return self._stage == 2


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											return self._stage == 3






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											return self._offload






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		engine






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											self.engine.backward(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											# Deepspeed's `engine.step` performs the following operations:
											# - gradient accumulation check
											# - gradient clipping
											# - optimizer step
											# - zero grad
											# - checking overflow
											# - lr_scheduler step (only if engine.lr_scheduler is not None)
											self.engine.step()
											# and this plugin overrides the above calls with no-ops when Accelerate runs under
											# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
											# training loop that works transparently under many training regimes.






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    device_placement=SCREAMING_SNAKE_CASE     ,    scaler=SCREAMING_SNAKE_CASE						)
											__snake_case				=		hasattr(self.optimizer     ,    "overflow"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	str:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Any:




											'''simple docstring'''

											if self.__has_overflow__:
																	return self.optimizer.overflow
											return False






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.001     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		params
											__snake_case				=		lr
											__snake_case				=		weight_decay
											__snake_case				=		kwargs






class     UpperCamelCase:


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		optimizer
											__snake_case				=		total_num_steps
											__snake_case				=		warmup_num_steps
											__snake_case				=		kwargs





