code: 
  
  
  
  
  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
  
  
  _A							    =	logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  def 						lowerCAmelCase_       (	__a )       ->			str:
  
  
  
  
  
         """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 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Any:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         return max(metric_fn(__snake_case	,	__snake_case ) for gt in ground_truths )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         SCREAMING_SNAKE_CASE			: Dict	     =[line.strip() for line in open(__snake_case	,	'''r''' ).readlines()]
         SCREAMING_SNAKE_CASE			: Any	     =[]
  
         if args.gold_data_mode == "qa":
                SCREAMING_SNAKE_CASE			: Dict	     =pd.read_csv(__snake_case	,	sep='''\t'''	,	header=__snake_case )
                for answer_list in data[1]:
                       SCREAMING_SNAKE_CASE			: Dict	     =ast.literal_eval(__snake_case )
                       answers.append(__snake_case )
         else:
                SCREAMING_SNAKE_CASE			: List[str]	     =[line.strip() for line in open(__snake_case	,	'''r''' ).readlines()]
                SCREAMING_SNAKE_CASE			: List[Any]	     =[[reference] for reference in references]
  
         SCREAMING_SNAKE_CASE			: str	     =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 )
  
         SCREAMING_SNAKE_CASE			: int	     =100.0 * em / total
         SCREAMING_SNAKE_CASE			: Optional[Any]	     =100.0 * fa / total
  
         logger.info(f'F1: {fa:.2f}' )
         logger.info(f'EM: {em:.2f}' )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         SCREAMING_SNAKE_CASE			: List[str]	     =args.k
         SCREAMING_SNAKE_CASE			: Tuple	     =[line.strip() for line in open(__snake_case	,	'''r''' ).readlines()]
         SCREAMING_SNAKE_CASE			: Optional[Any]	     =[line.strip() for line in open(__snake_case	,	'''r''' ).readlines()]
  
         SCREAMING_SNAKE_CASE			: Dict	     =0
         for hypo, reference in zip(__snake_case	,	__snake_case ):
                SCREAMING_SNAKE_CASE			: int	     =set(hypo.split('''\t''' )[:k] )
                SCREAMING_SNAKE_CASE			: List[Any]	     =set(reference.split('''\t''' ) )
                total += 1
                em += len(hypo_provenance & ref_provenance ) / k
  
         SCREAMING_SNAKE_CASE			: List[Any]	     =100.0 * em / total
         logger.info(f'Precision@{k}: {em: .2f}' )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[int]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         def strip_title(__a ):
                if title.startswith('''"''' ):
                       SCREAMING_SNAKE_CASE			: int	     =title[1:]
                if title.endswith('''"''' ):
                       SCREAMING_SNAKE_CASE			: List[Any]	     =title[:-1]
                return title
  
         SCREAMING_SNAKE_CASE			: Any	     =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 )
  
         SCREAMING_SNAKE_CASE			: int	     =rag_model.rag.question_encoder(__snake_case )
         SCREAMING_SNAKE_CASE			: Optional[int]	     =question_enc_outputs[0]
  
         SCREAMING_SNAKE_CASE			: int	     =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'''	,	)
         SCREAMING_SNAKE_CASE			: Union[str, Any]	     =rag_model.retriever.index.get_doc_dicts(result.doc_ids )
         SCREAMING_SNAKE_CASE			: Tuple	     =[]
         for docs in all_docs:
                SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[strip_title(__snake_case ) for title in docs['''title''']]
                provenance_strings.append('''\t'''.join(__snake_case ) )
         return provenance_strings
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         with torch.no_grad():
                SCREAMING_SNAKE_CASE			: int	     =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                    __snake_case	,	return_tensors='''pt'''	,	padding=__snake_case	,	truncation=__snake_case )
  
                SCREAMING_SNAKE_CASE			: Optional[int]	     =inputs_dict.input_ids.to(args.device )
                SCREAMING_SNAKE_CASE			: Any	     =inputs_dict.attention_mask.to(args.device )
                SCREAMING_SNAKE_CASE			: List[str]	     =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]]	,	)
                SCREAMING_SNAKE_CASE			: str	     =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 						lowerCAmelCase_       (	)       ->			List[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         SCREAMING_SNAKE_CASE			: str	     =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=50	,	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.'''	,	)
         SCREAMING_SNAKE_CASE			: List[Any]	     =parser.parse_args()
         SCREAMING_SNAKE_CASE			: List[Any]	     =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
         return args
  def 						lowerCAmelCase_       (	__a )       ->			int:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
         SCREAMING_SNAKE_CASE			: List[str]	     ={}
         if args.model_type is None:
                SCREAMING_SNAKE_CASE			: Any	     =infer_model_type(args.model_name_or_path )
                assert args.model_type is not None
         if args.model_type.startswith('''rag''' ):
                SCREAMING_SNAKE_CASE			: List[str]	     =RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
                SCREAMING_SNAKE_CASE			: Tuple	     =args.n_docs
                if args.index_name is not None:
                       SCREAMING_SNAKE_CASE			: int	     =args.index_name
                if args.index_path is not None:
                       SCREAMING_SNAKE_CASE			: Tuple	     =args.index_path
         else:
                SCREAMING_SNAKE_CASE			: Union[str, Any]	     =BartForConditionalGeneration
  
         SCREAMING_SNAKE_CASE			: Tuple	     =(
             [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 )
  
         SCREAMING_SNAKE_CASE			: Tuple	     =get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
         SCREAMING_SNAKE_CASE			: Optional[int]	     =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''' ):
                       SCREAMING_SNAKE_CASE			: Tuple	     =RagRetriever.from_pretrained(__snake_case	,	**__snake_case )
                       SCREAMING_SNAKE_CASE			: Any	     =model_class.from_pretrained(__snake_case	,	retriever=__snake_case	,	**__snake_case )
                       model.retriever.init_retrieval()
                else:
                       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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:
                       SCREAMING_SNAKE_CASE			: List[Any]	     =[]
                       for line in tqdm(__snake_case ):
                              questions.append(line.strip() )
                              if len(__snake_case ) == args.eval_batch_size:
                                     SCREAMING_SNAKE_CASE			: Tuple	     =evaluate_batch_fn(__snake_case	,	__snake_case	,	__snake_case )
                                     preds_file.write('''\n'''.join(__snake_case ) + '''\n''' )
                                     preds_file.flush()
                                     SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
                       if len(__snake_case ) > 0:
                              SCREAMING_SNAKE_CASE			: List[str]	     =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__":
         _A							    =	get_args()
         main(args)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)
  
  											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1
  
  											# Number of slack variables added to make inequalities into equalities
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages
  
  											# Objectives for each stage
  											SCREAMING_SNAKE_CASE			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: int	     =[]
  											for i in range(3							):
  																		for j in range(args[i]							):
  																									titles.append(string_starts[i] + str(j + 1							)							)
  											titles.append('''RHS'''							)
  											return titles
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.argmax(sign * tableau[0, : self.n_vars]							)
  
  											# Choice is only valid if below 0 for maximise, and above for minimise
  											if sign * self.tableau[0, col_idx] <= 0:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =True
  																		return 0, 0
  
  											# Pivot row is chosen as having the lowest quotient when elements of
  											# the pivot column divide the right-hand side
  
  											# Slice excluding the objective rows
  											SCREAMING_SNAKE_CASE			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.full(self.n_rows - self.n_stages		,		np.nan							)
  
  											# If element in pivot column is greater than zeron_stages, return
  											# quotient or nan otherwise
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=snake_case_		,		where=divisor > 0							)
  
  											# Arg of minimum quotient excluding the nan values. n_stages is added
  											# to compensate for earlier exclusion of objective columns
  											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =piv_row[col_idx]
  
  											# Entry becomes 1
  											piv_row *= 1 / piv_val
  
  											# Variable in pivot column becomes basic, ie the only non-zero entry
  											for idx, coeff in enumerate(tableau[:, col_idx]							):
  																		tableau[idx] += -coeff * piv_row
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( self							)    ->      dict[Any, Any]:
  											# Stop simplex algorithm from cycling.
  											for _ in range(100							):
  																		# Completion of each stage removes an objective. If both stages
  																		# are complete, then no objectives are left
  																		if not self.objectives:
  																									SCREAMING_SNAKE_CASE			: int	     =self.generate_col_titles(
  																									    self.n_vars		,		self.n_slack		,		self.n_art_vars							)
  
  																									# Find the values of each variable at optimal solution
  																									return self.interpret_tableau(self.tableau		,		self.col_titles							)
  
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =self.find_pivot(self.tableau							)
  
  																		# If there are no more negative values in objective row
  																		if self.stop_iter:
  																									# Delete artificial variable columns and rows. Update attributes
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: Any	     =tableau[nonzero_rowidx, i]
  
  																		# If there is only one nonzero value in column, which is one
  																		if n_nonzero == nonzero_val == 1:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =rhs_val
  
          # Check for basic variables
  											for title in col_titles:
  																		# Don't add RHS or slack variables to output dict
  																		if title[0] not in "R-s-a":
  																									output_dict.setdefault(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  