




import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase_		:				List[str]									=     datasets.logging.get_logger(__name__)

UpperCAmelCase_		:				str									=     "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

UpperCAmelCase_		:				Any									=     "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

UpperCAmelCase_		:				Any									=     "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION			)
class 		__A      (			datasets.Metric			):

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION    , citation=_CITATION    , homepage="""https://unbabel.github.io/COMET/html/index.html"""    , inputs_description=_KWARGS_DESCRIPTION    , features=datasets.Features(
										        {
										            """sources""": datasets.Value("""string"""    , id="""sequence""" ),
										            """predictions""": datasets.Value("""string"""    , id="""sequence""" ),
										            """references""": datasets.Value("""string"""    , id="""sequence""" ),
										        } )    , codebase_urls=["""https://github.com/Unbabel/COMET"""]    , reference_urls=[
										        """https://github.com/Unbabel/COMET""",
										        """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
										        """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
										    ]    , )

						def     A__						(		self   :str    , __snake_case   :Dict ):
										'''simple docstring'''

										if self.config_name == "default":
														__magic_name__	:	Any								=comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da""" ) )
										else:
														__magic_name__	:	Optional[Any]								=comet.load_from_checkpoint(comet.download_model(self.config_name ) )


						def     A__						(		self   :List[str]    , __snake_case   :str    , __snake_case   :Tuple    , __snake_case   :List[Any]    , __snake_case   :int=None    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										if gpus is None:
														__magic_name__	:	List[str]								=1 if torch.cuda.is_available() else 0
										__magic_name__	:	Tuple								={"""src""": sources, """mt""": predictions, """ref""": references}
										__magic_name__	:	Optional[Any]								=[dict(zip(UpperCamelCase__    , UpperCamelCase__ ) ) for t in zip(*data.values() )]
										__magic_name__				, __magic_name__	:	Optional[int]								=self.scorer.predict(UpperCamelCase__    , gpus=UpperCamelCase__    , progress_bar=UpperCamelCase__ )
										return {"mean_score": mean_score, "scores": scores}










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}.""")




