




'''simple docstring'''


from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  float ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							seed(1      )
							return [gauss(UpperCamelCase ,						UpperCamelCase      ) for _ in range(UpperCamelCase      )]
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  list      ):





							'''simple docstring'''


							return [k for k in range(UpperCamelCase      ) for _ in range(instance_count[k]      )]
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  list      ):





							'''simple docstring'''


							return sum(UpperCamelCase      ) / instance_count
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return instance_count / total_count
def 		snake_case_    (UpperCamelCase       :  list ,						UpperCamelCase       :  list ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[]  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(UpperCamelCase      )      ):
														# for loop iterates over number of elements in inner layer of items
														for j in range(len(items[i]      )      ):
																					# appending squared differences to 'squared_diff' list
																					squared_diff.append((items[i][j] - means[i]) ** 2      )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
							_a	     =				len(UpperCamelCase      )  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  list ,						UpperCamelCase       :  list ,						UpperCamelCase       :  float ,						UpperCamelCase       :  list      ):





							'''simple docstring'''


							_a	     =				[]
							# for loop iterates over number of elements in list
							for i in range(len(UpperCamelCase      )      ):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i]      )      ):
																					_a	     =				[]  # to store all discriminant values of each item as a list
																					# for loop iterates over number of classes we have in our dataset
																					for k in range(len(UpperCamelCase      )      ):
																												# appending values of discriminants for each class to 'temp' list
																												temp.append(
																												    x_items[i][j] * (means[k] / variance)
																												    - (means[k] ** 2 / (2 * variance))
																												    + log(probabilities[k]      )      )
																					# appending discriminant values of each item to 'results' list
																					results.append(UpperCamelCase      )

							return [result.index(max(UpperCamelCase      )      ) for result in results]
def 		snake_case_    (UpperCamelCase       :  list ,						UpperCamelCase       :  list      ):





							'''simple docstring'''


							_a	     =				sum(1 for i, j in zip(UpperCamelCase ,						UpperCamelCase      ) if i == j      )
							# percentage of accuracy equals to number of correct predictions divided by number
							# of all data and multiplied by 100
							return (correct / len(UpperCamelCase      )) * 100


_snake_case      :      Dict   							=		TypeVar('num')
def 		snake_case_    (UpperCamelCase       :  Callable[[object], num] ,						UpperCamelCase       :  str ,						UpperCamelCase       :  str ,						UpperCamelCase       :  Callable[[num], bool] = lambda UpperCamelCase      : True ,						UpperCamelCase       :  str | None = None ,						):





							'''simple docstring'''


							while True:
														try:
																					_a	     =				input_type(input(UpperCamelCase      ).strip() or default      )
																					if condition(UpperCamelCase      ):
																												return user_input
																					else:
																												print(f'{user_input}: {err_msg}'      )
																												continue
														except ValueError:
																					print(
																					    f'{user_input}: Incorrect input type, expected {input_type.__name__!r}'      )
def 		snake_case_    ():





							'''simple docstring'''


							while True:
														print(''' Linear Discriminant Analysis '''.center(50 ,						'''*'''      )      )
														print('''*''' * 50 ,						'''\n'''      )
														print('''First of all we should specify the number of classes that'''      )
														print('''we want to generate as training dataset'''      )
														# Trying to get number of classes
														_a	     =				valid_input(
														    input_type=UpperCamelCase ,						condition=lambda UpperCamelCase      : x > 0 ,						input_msg='''Enter the number of classes (Data Groupings): ''' ,						err_msg='''Number of classes should be positive!''' ,						)

														print('''-''' * 100      )

														# Trying to get the value of standard deviation
														_a	     =				valid_input(
														    input_type=UpperCamelCase ,						condition=lambda UpperCamelCase      : x >= 0 ,						input_msg=(
														        '''Enter the value of standard deviation'''
														        '''(Default value is 1.0 for all classes): '''
														    ) ,						err_msg='''Standard deviation should not be negative!''' ,						default='''1.0''' ,						)

														print('''-''' * 100      )

														# Trying to get number of instances in classes and theirs means to generate
														# dataset
														_a	     =				[]  # An empty list to store instance counts of classes in dataset
														for i in range(UpperCamelCase      ):
																					_a	     =				valid_input(
																					    input_type=UpperCamelCase ,						condition=lambda UpperCamelCase      : x > 0 ,						input_msg=(f'Enter The number of instances for class_{i+1}: ') ,						err_msg='''Number of instances should be positive!''' ,						)
																					counts.append(UpperCamelCase      )
														print('''-''' * 100      )

														# An empty list to store values of user-entered means of classes
														_a	     =				[]
														for a in range(UpperCamelCase      ):
																					_a	     =				valid_input(
																					    input_type=UpperCamelCase ,						input_msg=(f'Enter the value of mean for class_{a+1}: ') ,						err_msg='''This is an invalid value.''' ,						)
																					user_means.append(UpperCamelCase      )
														print('''-''' * 100      )

														print('''Standard deviation: ''' ,						UpperCamelCase      )
														# print out the number of instances in classes in separated line
														for i, count in enumerate(UpperCamelCase ,						1      ):
																					print(f'Number of instances in class_{i} is: {count}'      )
														print('''-''' * 100      )

														# print out mean values of classes separated line
														for i, user_mean in enumerate(UpperCamelCase ,						1      ):
																					print(f'Mean of class_{i} is: {user_mean}'      )
														print('''-''' * 100      )

														# Generating training dataset drawn from gaussian distribution
														_a	     =				[
														    gaussian_distribution(user_means[j] ,						UpperCamelCase ,						counts[j]      )
														    for j in range(UpperCamelCase      )
														]
														print('''Generated Normal Distribution: \n''' ,						UpperCamelCase      )
														print('''-''' * 100      )

														# Generating Ys to detecting corresponding classes
														_a	     =				y_generator(UpperCamelCase ,						UpperCamelCase      )
														print('''Generated Corresponding Ys: \n''' ,						UpperCamelCase      )
														print('''-''' * 100      )

														# Calculating the value of actual mean for each class
														_a	     =				[calculate_mean(counts[k] ,						x[k]      ) for k in range(UpperCamelCase      )]
														# for loop iterates over number of elements in 'actual_means' list and print
														# out them in separated line
														for i, actual_mean in enumerate(UpperCamelCase ,						1      ):
																					print(f'Actual(Real) mean of class_{i} is: {actual_mean}'      )
														print('''-''' * 100      )

														# Calculating the value of probabilities for each class
														_a	     =				[
														    calculate_probabilities(counts[i] ,						sum(UpperCamelCase      )      ) for i in range(UpperCamelCase      )
														]

														# for loop iterates over number of elements in 'probabilities' list and print
														# out them in separated line
														for i, probability in enumerate(UpperCamelCase ,						1      ):
																					print(f'Probability of class_{i} is: {probability}'      )
														print('''-''' * 100      )

														# Calculating the values of variance for each class
														_a	     =				calculate_variance(UpperCamelCase ,						UpperCamelCase ,						sum(UpperCamelCase      )      )
														print('''Variance: ''' ,						UpperCamelCase      )
														print('''-''' * 100      )

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														_a	     =				predict_y_values(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
														print('''-''' * 100      )

														# Calculating Accuracy of the model
														print(f'Accuracy: {accuracy(UpperCamelCase ,						UpperCamelCase      )}'      )
														print('''-''' * 100      )
														print(''' DONE '''.center(100 ,						'''+'''      )      )

														if input('''Press any key to restart or \'q\' for quit: '''      ).strip().lower() == "q":
																					print('''\n''' + '''GoodBye!'''.center(100 ,						'''-'''      ) + '''\n'''      )
																					break
														system('''cls''' if name == '''nt''' else '''clear'''      )  # noqa: S605


if __name__ == "__main__":
				main()







'''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


_snake_case      :      Optional[Any]   							=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 		snake_case_    (UpperCamelCase       :  Optional[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 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Optional[Any]      ):





							'''simple docstring'''


							return max(metric_fn(UpperCamelCase ,						UpperCamelCase      ) for gt in ground_truths      )
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  str      ):





							'''simple docstring'''


							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
							_a	     =				[]

							if args.gold_data_mode == "qa":
														_a	     =				pd.read_csv(UpperCamelCase ,						sep='''\t''' ,						header=UpperCamelCase      )
														for answer_list in data[1]:
																					_a	     =				ast.literal_eval(UpperCamelCase      )
																					answers.append(UpperCamelCase      )
							else:
														_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
														_a	     =				[[reference] for reference in references]

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

							_a	     =				100.0 * em / total
							_a	     =				100.0 * fa / total

							logger.info(f'F1: {fa:.2f}'      )
							logger.info(f'EM: {em:.2f}'      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


							_a	     =				args.k
							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]

							_a	     =				_a	     =				0
							for hypo, reference in zip(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				set(hypo.split('''\t'''      )[:k]      )
														_a	     =				set(reference.split('''\t'''      )      )
														total += 1
														em += len(hypo_provenance & ref_provenance      ) / k

							_a	     =				100.0 * em / total
							logger.info(f'Precision@{k}: {em: .2f}'      )
def 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  Dict      ):





							'''simple docstring'''


							def strip_title(UpperCamelCase       :  Tuple      ):
														if title.startswith('''"'''      ):
																					_a	     =				title[1:]
														if title.endswith('''"'''      ):
																					_a	     =				title[:-1]
														return title

							_a	     =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    UpperCamelCase ,						return_tensors='''pt''' ,						padding=UpperCamelCase ,						truncation=UpperCamelCase ,						)['''input_ids'''].to(args.device      )

							_a	     =				rag_model.rag.question_encoder(UpperCamelCase      )
							_a	     =				question_enc_outputs[0]

							_a	     =				rag_model.retriever(
							    UpperCamelCase ,						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''' ,						)
							_a	     =				rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
							_a	     =				[]
							for docs in all_docs:
														_a	     =				[strip_title(UpperCamelCase      ) for title in docs['''title''']]
														provenance_strings.append('''\t'''.join(UpperCamelCase      )      )
							return provenance_strings
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  Tuple      ):





							'''simple docstring'''


							with torch.no_grad():
														_a	     =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    UpperCamelCase ,						return_tensors='''pt''' ,						padding=UpperCamelCase ,						truncation=UpperCamelCase      )

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

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

														return answers
def 		snake_case_    ():





							'''simple docstring'''


							_a	     =				argparse.ArgumentParser()
							parser.add_argument(
							    '''--model_type''' ,						choices=['''rag_sequence''', '''rag_token''', '''bart'''] ,						type=UpperCamelCase ,						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=UpperCamelCase ,						choices=['''exact''', '''compressed''', '''legacy'''] ,						type=UpperCamelCase ,						help='''RAG model retriever type''' ,						)
							parser.add_argument(
							    '''--index_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						help='''Path to the retrieval index''' ,						)
							parser.add_argument('''--n_docs''' ,						default=5 ,						type=UpperCamelCase ,						help='''Number of retrieved docs'''      )
							parser.add_argument(
							    '''--model_name_or_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' ,						)
							parser.add_argument(
							    '''--eval_mode''' ,						choices=['''e2e''', '''retrieval'''] ,						default='''e2e''' ,						type=UpperCamelCase ,						help=(
							        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
							        ''' precision@k.'''
							    ) ,						)
							parser.add_argument('''--k''' ,						default=1 ,						type=UpperCamelCase ,						help='''k for the precision@k calculation'''      )
							parser.add_argument(
							    '''--evaluation_set''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to a file containing evaluation samples''' ,						)
							parser.add_argument(
							    '''--gold_data_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to a tab-separated file with gold samples''' ,						)
							parser.add_argument(
							    '''--gold_data_mode''' ,						default='''qa''' ,						type=UpperCamelCase ,						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=UpperCamelCase ,						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=UpperCamelCase ,						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=UpperCamelCase ,						help='''Number of beams to be used when generating answers''' ,						)
							parser.add_argument('''--min_length''' ,						default=1 ,						type=UpperCamelCase ,						help='''Min length of the generated answers'''      )
							parser.add_argument('''--max_length''' ,						default=50 ,						type=UpperCamelCase ,						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.''' ,						)
							_a	     =				parser.parse_args()
							_a	     =				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )
							return args
def 		snake_case_    (UpperCamelCase       :  Dict      ):





							'''simple docstring'''


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

							_a	     =				(
							    [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''' ,						UpperCamelCase      )

							_a	     =				get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
							_a	     =				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(UpperCamelCase ,						args.predictions_path ,						args.gold_data_path      )
																					continue

														logger.info('''***** Running evaluation for {} *****'''.format(UpperCamelCase      )      )
														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'''      ):
																					_a	     =				RagRetriever.from_pretrained(UpperCamelCase ,						**UpperCamelCase      )
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						retriever=UpperCamelCase ,						**UpperCamelCase      )
																					model.retriever.init_retrieval()
														else:
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						**UpperCamelCase      )
														model.to(args.device      )

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

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


if __name__ == "__main__":
				_snake_case      :      Optional[int]   							=		get_args()
				main(args)

