

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def  __a    (				) ->       int:






    '''simple docstring'''
    def filter_log(SCREAMING_SNAKE_CASE ):
        return False if "This IS expected if you are initializing" in record.msg else True

    __UpperCAmelCase			   =     datasets.utils.logging.get_logger('''transformers.modeling_utils''' )
    logger.addFilter(SCREAMING_SNAKE_CASE )
    try:
        yield
    finally:
        logger.removeFilter(SCREAMING_SNAKE_CASE )


A_						:     str			  =				'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

A_						:     List[Any]			  =				'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

A_						:     str			  =				'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,       _KWARGS_DESCRIPTION  )
class    A_				( datasets.Metric  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> str:
        return datasets.MetricInfo(
            description=_DESCRIPTION       , citation=_CITATION       , homepage='''https://github.com/Tiiiger/bert_score'''       , inputs_description=_KWARGS_DESCRIPTION       , features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''       , id='''sequence'''					),
                    '''references''': datasets.Sequence(datasets.Value('''string'''       , id='''sequence'''					)       , id='''references'''					),
                }					)       , codebase_urls=['''https://github.com/Tiiiger/bert_score''']       , reference_urls=[
                '''https://github.com/Tiiiger/bert_score''',
                '''https://arxiv.org/abs/1904.09675''',
            ]       , )


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__=None       , lowercase__=None       , lowercase__=None       , lowercase__=False       , lowercase__=False       , lowercase__=None       , lowercase__=64       , lowercase__=4       , lowercase__=False       , lowercase__=False       , lowercase__=None       , lowercase__=False       , )       -> List[Any]:
        __UpperCAmelCase			   =     bert_score.utils.get_hash
        __UpperCAmelCase			   =     bert_score.BERTScorer

        if version.parse(bert_score.__version__					) >= version.parse('''0.3.10'''					):
            __UpperCAmelCase			   =     functools.partial(lowercase__       , use_fast_tokenizer=lowercase__					)
            __UpperCAmelCase			   =     functools.partial(lowercase__       , use_fast_tokenizer=lowercase__					)
        elif use_fast_tokenizer:
            raise ImportWarning(
                '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
                '''You can install it with `pip install "bert-score>=0.3.10"`.'''					)

        if model_type is None:
            assert lang is not None, "either lang or model_type should be specified"
            __UpperCAmelCase			   =     bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
            __UpperCAmelCase			   =     bert_score.utils.modelalayers[model_type]

        __UpperCAmelCase			   =     get_hash(
            model=lowercase__       , num_layers=lowercase__       , idf=lowercase__       , rescale_with_baseline=lowercase__       , use_custom_baseline=baseline_path is not None       , )

        with filter_logging_context():
            if not hasattr(self       , '''cached_bertscorer'''					) or self.cached_bertscorer.hash != hashcode:
                __UpperCAmelCase			   =     scorer(
                    model_type=lowercase__       , num_layers=lowercase__       , batch_size=lowercase__       , nthreads=lowercase__       , all_layers=lowercase__       , idf=lowercase__       , device=lowercase__       , lang=lowercase__       , rescale_with_baseline=lowercase__       , baseline_path=lowercase__       , )

        ((__UpperCAmelCase)   ,       (__UpperCAmelCase)   ,       (__UpperCAmelCase))			   =     self.cached_bertscorer.score(
            cands=lowercase__       , refs=lowercase__       , verbose=lowercase__       , batch_size=lowercase__       , )
        __UpperCAmelCase			   =     {
            '''precision''': P.tolist(),
            '''recall''': R.tolist(),
            '''f1''': F.tolist(),
            '''hashcode''': hashcode,
        }
        return output_dict


    def        lowerCAmelCase_  (self       , lowercase__=None       , lowercase__=None       , **lowercase__					)       -> int:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if references is not None:
            __UpperCAmelCase			   =     [[ref] if isinstance(lowercase__       , lowercase__					) else ref for ref in references]
        super().add_batch(predictions=lowercase__       , references=lowercase__       , **lowercase__					)






    def        lowerCAmelCase_  (self       , lowercase__=None       , lowercase__=None       , **lowercase__					)       -> Tuple:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if isinstance(lowercase__       , lowercase__					):
            __UpperCAmelCase			   =     [reference]
        super().add(prediction=lowercase__       , reference=lowercase__       , **lowercase__					)







import os

import requests

A_						:     Any			  =				'https://www.amdoren.com/api/currency.php'


# Currency and their description
A_						:     Dict			  =				'\nAED	United Arab Emirates Dirham\nAFN	Afghan Afghani\nALL	Albanian Lek\nAMD	Armenian Dram\nANG	Netherlands Antillean Guilder\nAOA	Angolan Kwanza\nARS	Argentine Peso\nAUD	Australian Dollar\nAWG	Aruban Florin\nAZN	Azerbaijani Manat\nBAM	Bosnia & Herzegovina Convertible Mark\nBBD	Barbadian Dollar\nBDT	Bangladeshi Taka\nBGN	Bulgarian Lev\nBHD	Bahraini Dinar\nBIF	Burundian Franc\nBMD	Bermudian Dollar\nBND	Brunei Dollar\nBOB	Bolivian Boliviano\nBRL	Brazilian Real\nBSD	Bahamian Dollar\nBTN	Bhutanese Ngultrum\nBWP	Botswana Pula\nBYN	Belarus Ruble\nBZD	Belize Dollar\nCAD	Canadian Dollar\nCDF	Congolese Franc\nCHF	Swiss Franc\nCLP	Chilean Peso\nCNY	Chinese Yuan\nCOP	Colombian Peso\nCRC	Costa Rican Colon\nCUC	Cuban Convertible Peso\nCVE	Cape Verdean Escudo\nCZK	Czech Republic Koruna\nDJF	Djiboutian Franc\nDKK	Danish Krone\nDOP	Dominican Peso\nDZD	Algerian Dinar\nEGP	Egyptian Pound\nERN	Eritrean Nakfa\nETB	Ethiopian Birr\nEUR	Euro\nFJD	Fiji Dollar\nGBP	British Pound Sterling\nGEL	Georgian Lari\nGHS	Ghanaian Cedi\nGIP	Gibraltar Pound\nGMD	Gambian Dalasi\nGNF	Guinea Franc\nGTQ	Guatemalan Quetzal\nGYD	Guyanaese Dollar\nHKD	Hong Kong Dollar\nHNL	Honduran Lempira\nHRK	Croatian Kuna\nHTG	Haiti Gourde\nHUF	Hungarian Forint\nIDR	Indonesian Rupiah\nILS	Israeli Shekel\nINR	Indian Rupee\nIQD	Iraqi Dinar\nIRR	Iranian Rial\nISK	Icelandic Krona\nJMD	Jamaican Dollar\nJOD	Jordanian Dinar\nJPY	Japanese Yen\nKES	Kenyan Shilling\nKGS	Kyrgystani Som\nKHR	Cambodian Riel\nKMF	Comorian Franc\nKPW	North Korean Won\nKRW	South Korean Won\nKWD	Kuwaiti Dinar\nKYD	Cayman Islands Dollar\nKZT	Kazakhstan Tenge\nLAK	Laotian Kip\nLBP	Lebanese Pound\nLKR	Sri Lankan Rupee\nLRD	Liberian Dollar\nLSL	Lesotho Loti\nLYD	Libyan Dinar\nMAD	Moroccan Dirham\nMDL	Moldovan Leu\nMGA	Malagasy Ariary\nMKD	Macedonian Denar\nMMK	Myanma Kyat\nMNT	Mongolian Tugrik\nMOP	Macau Pataca\nMRO	Mauritanian Ouguiya\nMUR	Mauritian Rupee\nMVR	Maldivian Rufiyaa\nMWK	Malawi Kwacha\nMXN	Mexican Peso\nMYR	Malaysian Ringgit\nMZN	Mozambican Metical\nNAD	Namibian Dollar\nNGN	Nigerian Naira\nNIO	Nicaragua Cordoba\nNOK	Norwegian Krone\nNPR	Nepalese Rupee\nNZD	New Zealand Dollar\nOMR	Omani Rial\nPAB	Panamanian Balboa\nPEN	Peruvian Nuevo Sol\nPGK	Papua New Guinean Kina\nPHP	Philippine Peso\nPKR	Pakistani Rupee\nPLN	Polish Zloty\nPYG	Paraguayan Guarani\nQAR	Qatari Riyal\nRON	Romanian Leu\nRSD	Serbian Dinar\nRUB	Russian Ruble\nRWF	Rwanda Franc\nSAR	Saudi Riyal\nSBD	Solomon Islands Dollar\nSCR	Seychellois Rupee\nSDG	Sudanese Pound\nSEK	Swedish Krona\nSGD	Singapore Dollar\nSHP	Saint Helena Pound\nSLL	Sierra Leonean Leone\nSOS	Somali Shilling\nSRD	Surinamese Dollar\nSSP	South Sudanese Pound\nSTD	Sao Tome and Principe Dobra\nSYP	Syrian Pound\nSZL	Swazi Lilangeni\nTHB	Thai Baht\nTJS	Tajikistan Somoni\nTMT	Turkmenistani Manat\nTND	Tunisian Dinar\nTOP	Tonga Paanga\nTRY	Turkish Lira\nTTD	Trinidad and Tobago Dollar\nTWD	New Taiwan Dollar\nTZS	Tanzanian Shilling\nUAH	Ukrainian Hryvnia\nUGX	Ugandan Shilling\nUSD	United States Dollar\nUYU	Uruguayan Peso\nUZS	Uzbekistan Som\nVEF	Venezuelan Bolivar\nVND	Vietnamese Dong\nVUV	Vanuatu Vatu\nWST	Samoan Tala\nXAF	Central African CFA franc\nXCD	East Caribbean Dollar\nXOF	West African CFA franc\nXPF	CFP Franc\nYER	Yemeni Rial\nZAR	South African Rand\nZMW	Zambian Kwacha\n'


def  __a    (				SCREAMING_SNAKE_CASE = "USD"    ,				SCREAMING_SNAKE_CASE = "INR"    ,				SCREAMING_SNAKE_CASE = 1.0    ,				SCREAMING_SNAKE_CASE = "" ) ->       str:






    '''simple docstring'''
    __UpperCAmelCase			   =     locals()
    # from is a reserved keyword
    __UpperCAmelCase			   =     params.pop('''from_''' )
    __UpperCAmelCase			   =     requests.get(SCREAMING_SNAKE_CASE    ,				params=SCREAMING_SNAKE_CASE ).json()
    return str(res['''amount'''] ) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
       A_						:     List[str]			  =				os.getenv('CI', '')
       A_						:     Optional[int]			  =				os.getenv('AMDOREN_API_KEY', '')

       if not API_KEY and not TESTING:
              raise KeyError(
                  'API key must be provided in the \'AMDOREN_API_KEY\' environment variable.'
              )

       print(
           convert_currency(
               input('Enter from currency: ').strip(),
               input('Enter to currency: ').strip(),
               float(input('Enter the amount: ').strip()),
               API_KEY,
           )
       )




