code: 
  
  
  '''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
  
  
  __lowercase  :      str      		=				logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  
  
  
  
  
  def 							a_       (						lowercase    )			->Optional[Any]:
  
  
  
  
  
  
  				'''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 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Tuple:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return max(metric_fn(lowercase							,						lowercase    ) for gt in ground_truths    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[str]											=					[line.strip() for line in open(lowercase							,						"r"    ).readlines()]
  				__lowerCamelCase		:      Optional[int]											=					[]
  
  				if args.gold_data_mode == "qa":
  								__lowerCamelCase		:      Tuple											=					pd.read_csv(lowercase							,						sep="\t"							,						header=lowercase    )
  								for answer_list in data[1]:
  												__lowerCamelCase		:      int											=					ast.literal_eval(lowercase    )
  												answers.append(lowercase    )
  				else:
  								__lowerCamelCase		:      Optional[Any]											=					[line.strip() for line in open(lowercase							,						"r"    ).readlines()]
  								__lowerCamelCase		:      str											=					[[reference] for reference in references]
  
  				__lowerCamelCase		:      List[str]											=					0
  				for prediction, ground_truths in zip(lowercase							,						lowercase    ):
  								total += 1
  								em += metric_max_over_ground_truths(lowercase							,						lowercase							,						lowercase    )
  								fa += metric_max_over_ground_truths(lowercase							,						lowercase							,						lowercase    )
  
  				__lowerCamelCase		:      Union[str, Any]											=					100.0 * em / total
  				__lowerCamelCase		:      Any											=					100.0 * fa / total
  
  				logger.info(F'F1: {fa:.2f}'    )
  				logger.info(F'EM: {em:.2f}'    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[int]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      str											=					args.k
  				__lowerCamelCase		:      List[Any]											=					[line.strip() for line in open(lowercase							,						"r"    ).readlines()]
  				__lowerCamelCase		:      Dict											=					[line.strip() for line in open(lowercase							,						"r"    ).readlines()]
  
  				__lowerCamelCase		:      List[str]											=					0
  				for hypo, reference in zip(lowercase							,						lowercase    ):
  								__lowerCamelCase		:      Optional[int]											=					set(hypo.split("\t"    )[:k]    )
  								__lowerCamelCase		:      Optional[int]											=					set(reference.split("\t"    )    )
  								total += 1
  								em += len(hypo_provenance & ref_provenance    ) / k
  
  				__lowerCamelCase		:      str											=					100.0 * em / total
  				logger.info(F'Precision@{k}: {em: .2f}'    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				def strip_title(lowercase    ):
  								if title.startswith("\""    ):
  												__lowerCamelCase		:      List[str]											=					title[1:]
  								if title.endswith("\""    ):
  												__lowerCamelCase		:      int											=					title[:-1]
  								return title
  
  				__lowerCamelCase		:      Dict											=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
  				    lowercase							,						return_tensors="pt"							,						padding=lowercase							,						truncation=lowercase							,						)["input_ids"].to(args.device    )
  
  				__lowerCamelCase		:      int											=					rag_model.rag.question_encoder(lowercase    )
  				__lowerCamelCase		:      str											=					question_enc_outputs[0]
  
  				__lowerCamelCase		:      Dict											=					rag_model.retriever(
  				    lowercase							,						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"							,						)
  				__lowerCamelCase		:      Optional[int]											=					rag_model.retriever.index.get_doc_dicts(result.doc_ids    )
  				__lowerCamelCase		:      List[Any]											=					[]
  				for docs in all_docs:
  								__lowerCamelCase		:      Any											=					[strip_title(lowercase    ) for title in docs["title"]]
  								provenance_strings.append("\t".join(lowercase    )    )
  				return provenance_strings
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				with torch.no_grad():
  								__lowerCamelCase		:      Union[str, Any]											=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
  								    lowercase							,						return_tensors="pt"							,						padding=lowercase							,						truncation=lowercase    )
  
  								__lowerCamelCase		:      str											=					inputs_dict.input_ids.to(args.device    )
  								__lowerCamelCase		:      Any											=					inputs_dict.attention_mask.to(args.device    )
  								__lowerCamelCase		:      Tuple											=					rag_model.generate(  # rag_model overwrites generate
  								    lowercase							,						attention_mask=lowercase							,						num_beams=args.num_beams							,						min_length=args.min_length							,						max_length=args.max_length							,						early_stopping=lowercase							,						num_return_sequences=1							,						bad_words_ids=[[0, 0]]							,						)
  								__lowerCamelCase		:      int											=					rag_model.retriever.generator_tokenizer.batch_decode(lowercase							,						skip_special_tokens=lowercase    )
  
  								if args.print_predictions:
  												for q, a in zip(lowercase							,						lowercase    ):
  																logger.info("Q: {} - A: {}".format(lowercase							,						lowercase    )    )
  
  								return answers
  
  
  
  
  
  def 							a_       (						)			->List[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Union[str, Any]											=					argparse.ArgumentParser()
  				parser.add_argument(
  				    "--model_type"							,						choices=["rag_sequence", "rag_token", "bart"]							,						type=lowercase							,						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=lowercase							,						choices=["exact", "compressed", "legacy"]							,						type=lowercase							,						help="RAG model retriever type"							,						)
  				parser.add_argument(
  				    "--index_path"							,						default=lowercase							,						type=lowercase							,						help="Path to the retrieval index"							,						)
  				parser.add_argument("--n_docs"							,						default=5							,						type=lowercase							,						help="Number of retrieved docs"    )
  				parser.add_argument(
  				    "--model_name_or_path"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						help="Path to pretrained checkpoints or model identifier from huggingface.co/models"							,						)
  				parser.add_argument(
  				    "--eval_mode"							,						choices=["e2e", "retrieval"]							,						default="e2e"							,						type=lowercase							,						help=(
  				        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
  				        " precision@k."
  				    )							,						)
  				parser.add_argument("--k"							,						default=1							,						type=lowercase							,						help="k for the precision@k calculation"    )
  				parser.add_argument(
  				    "--evaluation_set"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						help="Path to a file containing evaluation samples"							,						)
  				parser.add_argument(
  				    "--gold_data_path"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						help="Path to a tab-separated file with gold samples"							,						)
  				parser.add_argument(
  				    "--gold_data_mode"							,						default="qa"							,						type=lowercase							,						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=lowercase							,						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=lowercase							,						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=lowercase							,						help="Number of beams to be used when generating answers"							,						)
  				parser.add_argument("--min_length"							,						default=1							,						type=lowercase							,						help="Min length of the generated answers"    )
  				parser.add_argument("--max_length"							,						default=50							,						type=lowercase							,						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."							,						)
  				__lowerCamelCase		:      Any											=					parser.parse_args()
  				__lowerCamelCase		:      Dict											=					torch.device("cuda" if torch.cuda.is_available() else "cpu"    )
  				return args
  
  
  
  
  
  def 							a_       (						lowercase    )			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Tuple											=					{}
  				if args.model_type is None:
  								__lowerCamelCase		:      str											=					infer_model_type(args.model_name_or_path    )
  								assert args.model_type is not None
  				if args.model_type.startswith("rag"    ):
  								__lowerCamelCase		:      List[str]											=					RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
  								__lowerCamelCase		:      Dict											=					args.n_docs
  								if args.index_name is not None:
  												__lowerCamelCase		:      int											=					args.index_name
  								if args.index_path is not None:
  												__lowerCamelCase		:      int											=					args.index_path
  				else:
  								__lowerCamelCase		:      List[Any]											=					BartForConditionalGeneration
  
  				__lowerCamelCase		:      str											=					(
  				    [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"							,						lowercase    )
  
  				__lowerCamelCase		:      Dict											=					get_scores if args.eval_mode == "e2e" else get_precision_at_k
  				__lowerCamelCase		:      Union[str, Any]											=					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(lowercase							,						args.predictions_path							,						args.gold_data_path    )
  												continue
  
  								logger.info("***** Running evaluation for {} *****".format(lowercase    )    )
  								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"    ):
  												__lowerCamelCase		:      List[str]											=					RagRetriever.from_pretrained(lowercase							,						**lowercase    )
  												__lowerCamelCase		:      List[str]											=					model_class.from_pretrained(lowercase							,						retriever=lowercase							,						**lowercase    )
  												model.retriever.init_retrieval()
  								else:
  												__lowerCamelCase		:      Dict											=					model_class.from_pretrained(lowercase							,						**lowercase    )
  								model.to(args.device    )
  
  								with open(args.evaluation_set							,						"r"    ) as eval_file, open(args.predictions_path							,						"w"    ) as preds_file:
  												__lowerCamelCase		:      int											=					[]
  												for line in tqdm(lowercase    ):
  																questions.append(line.strip()    )
  																if len(lowercase    ) == args.eval_batch_size:
  																				__lowerCamelCase		:      str											=					evaluate_batch_fn(lowercase							,						lowercase							,						lowercase    )
  																				preds_file.write("\n".join(lowercase    ) + "\n"    )
  																				preds_file.flush()
  																				__lowerCamelCase		:      str											=					[]
  												if len(lowercase    ) > 0:
  																__lowerCamelCase		:      Tuple											=					evaluate_batch_fn(lowercase							,						lowercase							,						lowercase    )
  																preds_file.write("\n".join(lowercase    )    )
  																preds_file.flush()
  
  												score_fn(lowercase							,						args.predictions_path							,						args.gold_data_path    )
  
  
  if __name__ == "__main__":
  				__lowercase  :      List[Any]      		=				get_args()
  				main(args)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import os
  
  import requests
  
  __lowercase  :      int      		=				"""https://www.amdoren.com/api/currency.php"""
  
  
  # Currency and their description
  __lowercase  :      Optional[int]      		=				"""
  AED	United Arab Emirates Dirham
  AFN	Afghan Afghani
  ALL	Albanian Lek
  AMD	Armenian Dram
  ANG	Netherlands Antillean Guilder
  AOA	Angolan Kwanza
  ARS	Argentine Peso
  AUD	Australian Dollar
  AWG	Aruban Florin
  AZN	Azerbaijani Manat
  BAM	Bosnia & Herzegovina Convertible Mark
  BBD	Barbadian Dollar
  BDT	Bangladeshi Taka
  BGN	Bulgarian Lev
  BHD	Bahraini Dinar
  BIF	Burundian Franc
  BMD	Bermudian Dollar
  BND	Brunei Dollar
  BOB	Bolivian Boliviano
  BRL	Brazilian Real
  BSD	Bahamian Dollar
  BTN	Bhutanese Ngultrum
  BWP	Botswana Pula
  BYN	Belarus Ruble
  BZD	Belize Dollar
  CAD	Canadian Dollar
  CDF	Congolese Franc
  CHF	Swiss Franc
  CLP	Chilean Peso
  CNY	Chinese Yuan
  COP	Colombian Peso
  CRC	Costa Rican Colon
  CUC	Cuban Convertible Peso
  CVE	Cape Verdean Escudo
  CZK	Czech Republic Koruna
  DJF	Djiboutian Franc
  DKK	Danish Krone
  DOP	Dominican Peso
  DZD	Algerian Dinar
  EGP	Egyptian Pound
  ERN	Eritrean Nakfa
  ETB	Ethiopian Birr
  EUR	Euro
  FJD	Fiji Dollar
  GBP	British Pound Sterling
  GEL	Georgian Lari
  GHS	Ghanaian Cedi
  GIP	Gibraltar Pound
  GMD	Gambian Dalasi
  GNF	Guinea Franc
  GTQ	Guatemalan Quetzal
  GYD	Guyanaese Dollar
  HKD	Hong Kong Dollar
  HNL	Honduran Lempira
  HRK	Croatian Kuna
  HTG	Haiti Gourde
  HUF	Hungarian Forint
  IDR	Indonesian Rupiah
  ILS	Israeli Shekel
  INR	Indian Rupee
  IQD	Iraqi Dinar
  IRR	Iranian Rial
  ISK	Icelandic Krona
  JMD	Jamaican Dollar
  JOD	Jordanian Dinar
  JPY	Japanese Yen
  KES	Kenyan Shilling
  KGS	Kyrgystani Som
  KHR	Cambodian Riel
  KMF	Comorian Franc
  KPW	North Korean Won
  KRW	South Korean Won
  KWD	Kuwaiti Dinar
  KYD	Cayman Islands Dollar
  KZT	Kazakhstan Tenge
  LAK	Laotian Kip
  LBP	Lebanese Pound
  LKR	Sri Lankan Rupee
  LRD	Liberian Dollar
  LSL	Lesotho Loti
  LYD	Libyan Dinar
  MAD	Moroccan Dirham
  MDL	Moldovan Leu
  MGA	Malagasy Ariary
  MKD	Macedonian Denar
  MMK	Myanma Kyat
  MNT	Mongolian Tugrik
  MOP	Macau Pataca
  MRO	Mauritanian Ouguiya
  MUR	Mauritian Rupee
  MVR	Maldivian Rufiyaa
  MWK	Malawi Kwacha
  MXN	Mexican Peso
  MYR	Malaysian Ringgit
  MZN	Mozambican Metical
  NAD	Namibian Dollar
  NGN	Nigerian Naira
  NIO	Nicaragua Cordoba
  NOK	Norwegian Krone
  NPR	Nepalese Rupee
  NZD	New Zealand Dollar
  OMR	Omani Rial
  PAB	Panamanian Balboa
  PEN	Peruvian Nuevo Sol
  PGK	Papua New Guinean Kina
  PHP	Philippine Peso
  PKR	Pakistani Rupee
  PLN	Polish Zloty
  PYG	Paraguayan Guarani
  QAR	Qatari Riyal
  RON	Romanian Leu
  RSD	Serbian Dinar
  RUB	Russian Ruble
  RWF	Rwanda Franc
  SAR	Saudi Riyal
  SBD	Solomon Islands Dollar
  SCR	Seychellois Rupee
  SDG	Sudanese Pound
  SEK	Swedish Krona
  SGD	Singapore Dollar
  SHP	Saint Helena Pound
  SLL	Sierra Leonean Leone
  SOS	Somali Shilling
  SRD	Surinamese Dollar
  SSP	South Sudanese Pound
  STD	Sao Tome and Principe Dobra
  SYP	Syrian Pound
  SZL	Swazi Lilangeni
  THB	Thai Baht
  TJS	Tajikistan Somoni
  TMT	Turkmenistani Manat
  TND	Tunisian Dinar
  TOP	Tonga Paanga
  TRY	Turkish Lira
  TTD	Trinidad and Tobago Dollar
  TWD	New Taiwan Dollar
  TZS	Tanzanian Shilling
  UAH	Ukrainian Hryvnia
  UGX	Ugandan Shilling
  USD	United States Dollar
  UYU	Uruguayan Peso
  UZS	Uzbekistan Som
  VEF	Venezuelan Bolivar
  VND	Vietnamese Dong
  VUV	Vanuatu Vatu
  WST	Samoan Tala
  XAF	Central African CFA franc
  XCD	East Caribbean Dollar
  XOF	West African CFA franc
  XPF	CFP Franc
  YER	Yemeni Rial
  ZAR	South African Rand
  ZMW	Zambian Kwacha
  """
  
  
  
  
  
  def 							a_       (						lowercase = "USD"							,						lowercase = "INR"							,						lowercase = 1.0							,						lowercase = ""    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      str											=					locals()
  				# from is a reserved keyword
  				__lowerCamelCase		:      List[str]											=					params.pop("from_"    )
  				__lowerCamelCase		:      str											=					requests.get(lowercase							,						params=lowercase    ).json()
  				return str(res["amount"]    ) if res["error"] == 0 else res["error_message"]
  
  
  if __name__ == "__main__":
  				__lowercase  :      Any      		=				os.getenv("""CI""", """""")
  				__lowercase  :      List[str]      		=				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,
  				    )
  				)
  
  
  
  
  
  