




import os
import sys
import tempfile

import torch

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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=()	,     lowerCamelCase=None	,     lowerCamelCase="no"	,     lowerCamelCase="29500"   ):
				__magic_name__	:	Any								=False
				__magic_name__	:	Optional[Any]								=False
				if any(key.startswith("""KAGGLE"""   ) for key in os.environ.keys()   ):
								__magic_name__	:	Tuple								=True
				elif "IPython" in sys.modules:
								__magic_name__	:	int								="""google.colab""" in str(sys.modules["""IPython"""].get_ipython()   )

				try:
								__magic_name__	:	List[str]								=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:
												__magic_name__	:	Optional[Any]								=8

								__magic_name__	:	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   ):
																__magic_name__	:	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():
																__magic_name__	:	int								="""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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=()	,     lowerCamelCase=2   ):
				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"""	,     ):
												__magic_name__	:	List[Any]								=PrepareForLaunch(lowerCamelCase	,     debug=lowerCamelCase   )
												start_processes(lowerCamelCase	,     args=lowerCamelCase	,     nprocs=lowerCamelCase	,     start_method="""fork"""   )










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """title""": """Title""",
				    """publish_date""": """Publish date""",
				    """authors""": """Authors""",
				    """number_of_pages""": """Number of pages:""",
				    """first_sentence""": """First sentence""",
				    """isbn_10""": """ISBN (10)""",
				    """isbn_13""": """ISBN (13)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				List[Any]									=     input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
														if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																					break

														if len(isbn) not in (10, 13) or not isbn.isdigit():
																					print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
																					continue

														print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

														try:
																					UpperCAmelCase_		:				str									=     summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
																					print("\n".join(F"""{key}: {value}""" for key, value in book_summary.items()))
														except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																					print(F"""Sorry, there are no results for ISBN: {isbn}.""")




