import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   int=()   , lowerCAmelCase							:   List[Any]=None   , lowerCAmelCase							:   Optional[Any]="no"   , lowerCAmelCase							:   Optional[int]="29500"	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    False
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    False
		if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
				SCREAMING_SNAKE_CASE_  :		int				      =    True
		elif "IPython" in sys.modules:
				SCREAMING_SNAKE_CASE_  :		Any				      =    "google.colab" in str(sys.modules["IPython"].get_ipython()	)

		try:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    PrecisionType(mixed_precision.lower()	)
		except ValueError:
				raise ValueError(
				    f'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

		if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"   , lowerCAmelCase	) is not None):
				# TPU launch
				import torch_xla.distributed.xla_multiprocessing as xmp

				if len(AcceleratorState._shared_state	) > 0:
						raise ValueError(
						    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
						    "your training function. Restart your notebook and make sure no cells initializes an "
						    "`Accelerator`."	)
				if num_processes is None:
						SCREAMING_SNAKE_CASE_  :		str				      =    8

				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    PrepareForLaunch(lowerCAmelCase   , distributed_type="TPU"	)
				print(f'Launching a training on {num_processes} TPU cores.'	)
				xmp.spawn(lowerCAmelCase   , args=lowerCAmelCase   , nprocs=lowerCAmelCase   , start_method="fork"	)
		elif in_colab:
				# No need for a distributed launch otherwise as it's either CPU or one GPU.
				if torch.cuda.is_available():
						print("Launching training on one GPU."	)
				else:
						print("Launching training on one CPU."	)
				function(*lowerCAmelCase	)
		else:
				if num_processes is None:
						raise ValueError(
						    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

				if num_processes > 1:
						# Multi-GPU launch
						from torch.multiprocessing import start_processes
						from torch.multiprocessing.spawn import ProcessRaisedException

						if len(AcceleratorState._shared_state	) > 0:
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
								    "inside your training function. Restart your notebook and make sure no cells initializes an "
								    "`Accelerator`."	)

						if torch.cuda.is_initialized():
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
								    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
								    "function."	)

						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=lowerCAmelCase   , master_addr="127.0.01"   , master_port=lowerCAmelCase   , mixed_precision=lowerCAmelCase	):
								SCREAMING_SNAKE_CASE_  :		str				      =    PrepareForLaunch(lowerCAmelCase   , distributed_type="MULTI_GPU"	)
								print(f'Launching training on {num_processes} GPUs.'	)
								try:
										start_processes(lowerCAmelCase   , args=lowerCAmelCase   , nprocs=lowerCAmelCase   , start_method="fork"	)
								except ProcessRaisedException as e:
										if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
												raise RuntimeError(
												    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
												    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
												    "Please review your imports and test them when running the `notebook_launcher()` to identify "
												    "which one is problematic."	) from e

				else:
						# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
						if is_mps_available():
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "1"
								print("Launching training on MPS."	)
						elif torch.cuda.is_available():
								print("Launching training on one GPU."	)
						else:
								print("Launching training on CPU."	)
						function(*lowerCAmelCase	)







def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Union[str, Any]=()   , lowerCAmelCase							:   Any=2	):
		"""simple docstring"""







		from torch.multiprocessing import start_processes

		with tempfile.NamedTemporaryFile() as tmp_file:
				# torch.distributed will expect a few environment variable to be here. We set the ones common to each
				# process here (the other ones will be set be the launcher).
				with patch_environment(
				    world_size=lowerCAmelCase   , master_addr="127.0.01"   , master_port="29500"   , accelerate_mixed_precision="no"   , accelerate_debug_rdv_file=tmp_file.name   , accelerate_use_cpu="yes"   , ):
						SCREAMING_SNAKE_CASE_  :		Any				      =    PrepareForLaunch(lowerCAmelCase   , debug=lowerCAmelCase	)
						start_processes(lowerCAmelCase   , args=lowerCAmelCase   , nprocs=lowerCAmelCase   , start_method="fork"	)

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)
