




'''simple docstring'''





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def   UpperCamelCase_(			snake_case							: int  ):

							'''simple docstring'''






							if "token" in model_name_or_path:
														return "rag_token"
							if "sequence" in model_name_or_path:
														return "rag_sequence"
							if "bart" in model_name_or_path:
														return "bart"
							return None


def   UpperCamelCase_(			snake_case							: Any							,       snake_case							: int							,       snake_case							: int  ):

							'''simple docstring'''






							return max(metric_fn(snake_case							,       snake_case  ) for gt in ground_truths  )


def   UpperCamelCase_(			snake_case							: str							,       snake_case							: str							,       snake_case							: List[Any]  ):

							'''simple docstring'''






							snake_case_			      =   [line.strip() for line in open(snake_case							,       "r"  ).readlines()]
							snake_case_			      =   []

							if args.gold_data_mode == "qa":
														snake_case_			      =   pd.read_csv(snake_case							,       sep="\t"							,       header=snake_case  )
														for answer_list in data[1]:
																					snake_case_			      =   ast.literal_eval(snake_case  )
																					answers.append(snake_case  )
							else:
														snake_case_			      =   [line.strip() for line in open(snake_case							,       "r"  ).readlines()]
														snake_case_			      =   [[reference] for reference in references]

							snake_case_			      =   snake_case_			      =   snake_case_			      =   0
							for prediction, ground_truths in zip(snake_case							,       snake_case  ):
														total += 1
														em += metric_max_over_ground_truths(snake_case							,       snake_case							,       snake_case  )
														fa += metric_max_over_ground_truths(snake_case							,       snake_case							,       snake_case  )

							snake_case_			      =   100.0 * em / total
							snake_case_			      =   100.0 * fa / total

							logger.info(f'F1: {fa:.2f}'  )
							logger.info(f'EM: {em:.2f}'  )


def   UpperCamelCase_(			snake_case							: Optional[Any]							,       snake_case							: int							,       snake_case							: List[str]  ):

							'''simple docstring'''






							snake_case_			      =   args.k
							snake_case_			      =   [line.strip() for line in open(snake_case							,       "r"  ).readlines()]
							snake_case_			      =   [line.strip() for line in open(snake_case							,       "r"  ).readlines()]

							snake_case_			      =   snake_case_			      =   0
							for hypo, reference in zip(snake_case							,       snake_case  ):
														snake_case_			      =   set(hypo.split("\t"  )[:k]  )
														snake_case_			      =   set(reference.split("\t"  )  )
														total += 1
														em += len(hypo_provenance & ref_provenance  ) / k

							snake_case_			      =   100.0 * em / total
							logger.info(f'Precision@{k}: {em: .2f}'  )


def   UpperCamelCase_(			snake_case							: Tuple							,       snake_case							: str							,       snake_case							: Dict  ):

							'''simple docstring'''






							def strip_title(snake_case							: Dict  ):
														if title.startswith("\""  ):
																					snake_case_			      =   title[1:]
														if title.endswith("\""  ):
																					snake_case_			      =   title[:-1]
														return title

							snake_case_			      =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    snake_case							,       return_tensors="pt"							,       padding=snake_case							,       truncation=snake_case							,       )["input_ids"].to(args.device  )

							snake_case_			      =   rag_model.rag.question_encoder(snake_case  )
							snake_case_			      =   question_enc_outputs[0]

							snake_case_			      =   rag_model.retriever(
							    snake_case							,       question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()							,       prefix=rag_model.rag.generator.config.prefix							,       n_docs=rag_model.config.n_docs							,       return_tensors="pt"							,       )
							snake_case_			      =   rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							snake_case_			      =   []
							for docs in all_docs:
														snake_case_			      =   [strip_title(snake_case  ) for title in docs["title"]]
														provenance_strings.append("\t".join(snake_case  )  )
							return provenance_strings


def   UpperCamelCase_(			snake_case							: Dict							,       snake_case							: Any							,       snake_case							: Any  ):

							'''simple docstring'''






							with torch.no_grad():
														snake_case_			      =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    snake_case							,       return_tensors="pt"							,       padding=snake_case							,       truncation=snake_case  )

														snake_case_			      =   inputs_dict.input_ids.to(args.device  )
														snake_case_			      =   inputs_dict.attention_mask.to(args.device  )
														snake_case_			      =   rag_model.generate(  # rag_model overwrites generate
														    snake_case							,       attention_mask=snake_case							,       num_beams=args.num_beams							,       min_length=args.min_length							,       max_length=args.max_length							,       early_stopping=snake_case							,       num_return_sequences=1							,       bad_words_ids=[[0, 0]]							,       )
														snake_case_			      =   rag_model.retriever.generator_tokenizer.batch_decode(snake_case							,       skip_special_tokens=snake_case  )

														if args.print_predictions:
																					for q, a in zip(snake_case							,       snake_case  ):
																												logger.info("Q: {} - A: {}".format(snake_case							,       snake_case  )  )

														return answers


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   argparse.ArgumentParser()
							parser.add_argument(
							    "--model_type"							,       choices=["rag_sequence", "rag_token", "bart"]							,       type=snake_case							,       help=(
							        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
							        " model_name_or_path"
							    )							,       )
							parser.add_argument(
							    "--index_name"							,       default=snake_case							,       choices=["exact", "compressed", "legacy"]							,       type=snake_case							,       help="RAG model retriever type"							,       )
							parser.add_argument(
							    "--index_path"							,       default=snake_case							,       type=snake_case							,       help="Path to the retrieval index"							,       )
							parser.add_argument("--n_docs"							,       default=5							,       type=snake_case							,       help="Number of retrieved docs"  )
							parser.add_argument(
							    "--model_name_or_path"							,       default=snake_case							,       type=snake_case							,       required=snake_case							,       help="Path to pretrained checkpoints or model identifier from huggingface.co/models"							,       )
							parser.add_argument(
							    "--eval_mode"							,       choices=["e2e", "retrieval"]							,       default="e2e"							,       type=snake_case							,       help=(
							        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
							        " precision@k."
							    )							,       )
							parser.add_argument("--k"							,       default=1							,       type=snake_case							,       help="k for the precision@k calculation"  )
							parser.add_argument(
							    "--evaluation_set"							,       default=snake_case							,       type=snake_case							,       required=snake_case							,       help="Path to a file containing evaluation samples"							,       )
							parser.add_argument(
							    "--gold_data_path"							,       default=snake_case							,       type=snake_case							,       required=snake_case							,       help="Path to a tab-separated file with gold samples"							,       )
							parser.add_argument(
							    "--gold_data_mode"							,       default="qa"							,       type=snake_case							,       choices=["qa", "ans"]							,       help=(
							        "Format of the gold data file"
							        "qa - a single line in the following format: question [tab] answer_list"
							        "ans - a single line of the gold file contains the expected answer string"
							    )							,       )
							parser.add_argument(
							    "--predictions_path"							,       type=snake_case							,       default="predictions.txt"							,       help="Name of the predictions file, to be stored in the checkpoints directory"							,       )
							parser.add_argument(
							    "--eval_all_checkpoints"							,       action="store_true"							,       help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"							,       )
							parser.add_argument(
							    "--eval_batch_size"							,       default=8							,       type=snake_case							,       help="Batch size per GPU/CPU for evaluation."							,       )
							parser.add_argument(
							    "--recalculate"							,       help="Recalculate predictions even if the prediction file exists"							,       action="store_true"							,       )
							parser.add_argument(
							    "--num_beams"							,       default=4							,       type=snake_case							,       help="Number of beams to be used when generating answers"							,       )
							parser.add_argument("--min_length"							,       default=1							,       type=snake_case							,       help="Min length of the generated answers"  )
							parser.add_argument("--max_length"							,       default=5_0							,       type=snake_case							,       help="Max length of the generated answers"  )

							parser.add_argument(
							    "--print_predictions"							,       action="store_true"							,       help="If True, prints predictions while evaluating."							,       )
							parser.add_argument(
							    "--print_docs"							,       action="store_true"							,       help="If True, prints docs retried while generating."							,       )
							snake_case_			      =   parser.parse_args()
							snake_case_			      =   torch.device("cuda" if torch.cuda.is_available() else "cpu"  )
							return args


def   UpperCamelCase_(			snake_case							: Optional[int]  ):

							'''simple docstring'''






							snake_case_			      =   {}
							if args.model_type is None:
														snake_case_			      =   infer_model_type(args.model_name_or_path  )
														assert args.model_type is not None
							if args.model_type.startswith("rag"  ):
														snake_case_			      =   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
														snake_case_			      =   args.n_docs
														if args.index_name is not None:
																					snake_case_			      =   args.index_name
														if args.index_path is not None:
																					snake_case_			      =   args.index_path
							else:
														snake_case_			      =   BartForConditionalGeneration

							snake_case_			      =   (
							    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
							    if args.eval_all_checkpoints
							    else [args.model_name_or_path]
							)

							logger.info("Evaluate the following checkpoints: %s"							,       snake_case  )

							snake_case_			      =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
							snake_case_			      =   evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

							for checkpoint in checkpoints:
														if os.path.exists(args.predictions_path  ) and (not args.recalculate):
																					logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path  )  )
																					score_fn(snake_case							,       args.predictions_path							,       args.gold_data_path  )
																					continue

														logger.info("***** Running evaluation for {} *****".format(snake_case  )  )
														logger.info("  Batch size = %d"							,       args.eval_batch_size  )
														logger.info("  Predictions will be stored under {}".format(args.predictions_path  )  )

														if args.model_type.startswith("rag"  ):
																					snake_case_			      =   RagRetriever.from_pretrained(snake_case							,       **snake_case  )
																					snake_case_			      =   model_class.from_pretrained(snake_case							,       retriever=snake_case							,       **snake_case  )
																					model.retriever.init_retrieval()
														else:
																					snake_case_			      =   model_class.from_pretrained(snake_case							,       **snake_case  )
														model.to(args.device  )

														with open(args.evaluation_set							,       "r"  ) as eval_file, open(args.predictions_path							,       "w"  ) as preds_file:
																					snake_case_			      =   []
																					for line in tqdm(snake_case  ):
																												questions.append(line.strip()  )
																												if len(snake_case  ) == args.eval_batch_size:
																																			snake_case_			      =   evaluate_batch_fn(snake_case							,       snake_case							,       snake_case  )
																																			preds_file.write("\n".join(snake_case  ) + "\n"  )
																																			preds_file.flush()
																																			snake_case_			      =   []
																					if len(snake_case  ) > 0:
																												snake_case_			      =   evaluate_batch_fn(snake_case							,       snake_case							,       snake_case  )
																												preds_file.write("\n".join(snake_case  )  )
																												preds_file.flush()

																					score_fn(snake_case							,       args.predictions_path							,       args.gold_data_path  )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     Any   	=				get_args()
		main(args)










'''simple docstring'''





import os

import requests

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


# Currency and their description
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				"\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_(			snake_case							: str = "USD"							,       snake_case							: str = "INR"							,       snake_case							: float = 1.0							,       snake_case							: str = ""  ):

							'''simple docstring'''






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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     Any   	=				os.getenv("CI", "")
		_SCREAMING_SNAKE_CASE							:     List[Any]   	=				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,
		    )
		)




