"""simple docstring"""




import os

import requests

__lowerCamelCase									=	"https://www.amdoren.com/api/currency.php"


# Currency and their description
__lowerCamelCase									=	"\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 	UpperCAmelCase       (						UpperCamelCase__ = "USD"							,     UpperCamelCase__ = "INR"							,     UpperCamelCase__ = 1.0							,     UpperCamelCase__ = ""					):






							"""simple docstring"""





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


if __name__ == "__main__":
				__lowerCamelCase									=	os.getenv("CI", "")
				__lowerCamelCase									=	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,
				    )
				)






"""simple docstring"""




import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


__lowerCamelCase									=	"block_records.npy"


__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							import tensorflow.compat.va as tf

							A__						  =				tf.data.TFRecordDataset(UpperCamelCase__							,     buffer_size=512 * 1_024 * 1_024					)
							A__						  =				blocks_dataset.batch(UpperCamelCase__							,     drop_remainder=UpperCamelCase__					)
							A__						  =				next(blocks_dataset.take(1					).as_numpy_iterator()					)

							return np_record
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=2		,__UpperCAmelCase=10_00		,__UpperCAmelCase=1_00		,__UpperCAmelCase=10_00_00		,)       ->					Optional[int]:
													from scann.scann_ops.py.scann_ops_pybind import builder as Builder

													A__						  =				Builder(db=__UpperCAmelCase		,num_neighbors=__UpperCAmelCase		,distance_measure='dot_product'  )
													A__						  =				builder.tree(
													    num_leaves=__UpperCAmelCase		,num_leaves_to_search=__UpperCAmelCase		,training_sample_size=__UpperCAmelCase  )
													A__						  =				builder.score_ah(dimensions_per_block=__UpperCAmelCase  )

													A__						  =				builder.build()





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													A__  ,	A__						  =				self.searcher.search_batched(question_projection.detach().cpu()  )
													return retrieved_block_ids.astype('int64'  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[str]:
													super().__init__()
													A__						  =				block_records
													A__						  =				tokenizer





						def __call__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase="pt"  )       ->					str:
													A__						  =				np.take(self.block_records		,indices=__UpperCAmelCase		,axis=0  )

													A__						  =				self.tokenizer.decode(question_input_ids[0]		,skip_special_tokens=__UpperCAmelCase  )

													A__						  =				[]
													A__						  =				[]
													for retrieved_block in retrieved_blocks:
																				text.append(__UpperCAmelCase  )
																				text_pair.append(retrieved_block.decode()  )

													A__						  =				self.tokenizer(
													    __UpperCAmelCase		,__UpperCAmelCase		,padding=__UpperCAmelCase		,truncation=__UpperCAmelCase		,return_special_tokens_mask=__UpperCAmelCase		,max_length=__UpperCAmelCase  )
													A__						  =				concat_inputs.convert_to_tensors(__UpperCAmelCase  )

													if answer_ids is not None:
																				return self.block_has_answer(__UpperCAmelCase		,__UpperCAmelCase  ) + (concat_inputs_tensors,)
													else:
																				return (None, None, None, concat_inputs_tensors)





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					List[Any]:
													if os.path.isdir(__UpperCAmelCase  ):
																				A__						  =				os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )
													else:
																				A__						  =				hf_hub_download(
																				    repo_id=__UpperCAmelCase		,filename=_REALM_BLOCK_RECORDS_FILENAME		,**__UpperCAmelCase  )
													A__						  =				np.load(__UpperCAmelCase		,allow_pickle=__UpperCAmelCase  )

													A__						  =				AutoTokenizer.from_pretrained(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )

													return cls(__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													# save block records
													np.save(os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )		,self.block_records  )
													# save tokenizer
													self.tokenizer.save_pretrained(__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				[]
													A__						  =				[]
													A__						  =				[]
													A__						  =				0

													for input_id in concat_inputs.input_ids:
																				A__						  =				input_id.tolist()
																				# Check answers between two [SEP] tokens
																				A__						  =				input_id_list.index(self.tokenizer.sep_token_id  )
																				A__						  =				first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id  )

																				start_pos.append([]  )
																				end_pos.append([]  )
																				for answer in answer_ids:
																											for idx in range(first_sep_idx + 1		,__UpperCAmelCase  ):
																																		if answer[0] == input_id_list[idx]:
																																									if input_id_list[idx : idx + len(__UpperCAmelCase  )] == answer:
																																																start_pos[-1].append(__UpperCAmelCase  )
																																																end_pos[-1].append(idx + len(__UpperCAmelCase  ) - 1  )

																				if len(start_pos[-1]  ) == 0:
																											has_answers.append(__UpperCAmelCase  )
																				else:
																											has_answers.append(__UpperCAmelCase  )
																											if len(start_pos[-1]  ) > max_answers:
																																		A__						  =				len(start_pos[-1]  )

        # Pad -1 to max_answers
													for start_pos_, end_pos_ in zip(__UpperCAmelCase		,__UpperCAmelCase  ):
																				if len(start_pos_  ) < max_answers:
																											A__						  =				[-1] * (max_answers - len(start_pos_  ))
																											start_pos_ += padded
																											end_pos_ += padded
													return has_answers, start_pos, end_pos





