



from __future__ import annotations

import random
from collections.abc import Iterable







class      __lowerCAmelCase       :



			def __init__(		self  :    List[str]					,						A  :    list[str])  ->       None:



						"""simple docstring"""






						_UpperCAmelCase									=  {literal: None for literal in literals}



			def __str__(		self  :    List[Any])  ->       str:



						"""simple docstring"""






						return "{" + " , ".join(self.literals) + "}"



			def __len__(		self  :    Any)  ->       int:



						"""simple docstring"""






						return len(self.literals)



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    dict[str, bool | None])  ->       None:



						"""simple docstring"""






						for literal in self.literals:
									_UpperCAmelCase									=  literal[:2]
									if symbol in model:
												_UpperCAmelCase									=  model[symbol]
									else:
												continue
									if value is not None:
												# Complement assignment if literal is in complemented form
												if literal.endswith('\''):
															_UpperCAmelCase									=  not value
									_UpperCAmelCase									=  value



			def        _lowerCamelCase			(		self  :    List[str]					,						A  :    dict[str, bool | None])  ->       bool | None:



						"""simple docstring"""






						for literal in self.literals:
									_UpperCAmelCase									=  literal.rstrip('\'') if literal.endswith('\'') else literal + '\''
									if symbol in self.literals:
												return True

						self.assign(A)
						for value in self.literals.values():
									if value in (True, None):
												return value
						return any(self.literals.values())







class      __lowerCAmelCase       :



			def __init__(		self  :    int					,						A  :    Iterable[Clause])  ->       None:



						"""simple docstring"""






						_UpperCAmelCase									=  list(A)



			def __str__(		self  :    int)  ->       str:



						"""simple docstring"""






						return "{" + " , ".join(str(A) for clause in self.clauses) + "}"





def 				A      (	)							->					Clause:







			'''simple docstring'''
			_UpperCAmelCase									=  []
			_UpperCAmelCase									=  random.randint(1				,  5       )
			_UpperCAmelCase									=  'A'
			_UpperCAmelCase									=  0
			while i < no_of_literals:
						_UpperCAmelCase									=  random.randint(1				,  5       )
						_UpperCAmelCase									=  base_var + str(_UpperCAmelCase       )
						_UpperCAmelCase									=  random.randint(0				,  1       )
						if var_complement == 1:
									var_name += "'"
						if var_name in literals:
									i -= 1
						else:
									literals.append(_UpperCAmelCase       )
						i += 1
			return Clause(_UpperCAmelCase       )





def 				A      (	)							->					Formula:







			'''simple docstring'''
			_UpperCAmelCase									=  set()
			_UpperCAmelCase									=  random.randint(1				,  10       )
			while len(_UpperCAmelCase       ) < no_of_clauses:
						clauses.add(generate_clause()       )
			return Formula(_UpperCAmelCase       )





def 				A      (	_UpperCAmelCase							:  Formula       )							->					tuple[list[Clause], list[str]]:







			'''simple docstring'''
			_UpperCAmelCase									=  formula.clauses
			_UpperCAmelCase									=  []
			for clause in formula.clauses:
						for literal in clause.literals:
									_UpperCAmelCase									=  literal[:2]
									if symbol not in symbols_set:
												symbols_set.append(_UpperCAmelCase       )
			return clauses, symbols_set





def 				A      (	_UpperCAmelCase							:  list[Clause]				,  _UpperCAmelCase							:  list[str]				,  _UpperCAmelCase							:  dict[str, bool | None]       )							->					tuple[list[str], dict[str, bool | None]]:







			'''simple docstring'''
			_UpperCAmelCase									=  []
			_UpperCAmelCase									=  {}
			_UpperCAmelCase									=  []

			for clause in clauses:
						if clause.evaluate(_UpperCAmelCase       ):
									continue
						for literal in clause.literals:
									literals.append(_UpperCAmelCase       )

			for s in symbols:
						_UpperCAmelCase									=  s + '\''
						if (s in literals and sym not in literals) or (
						    s not in literals and sym in literals
						):
									pure_symbols.append(_UpperCAmelCase       )
			for p in pure_symbols:
						_UpperCAmelCase									=  None
			for s in pure_symbols:
						_UpperCAmelCase									=  s + '\''
						if s in literals:
									_UpperCAmelCase									=  True
						elif sym in literals:
									_UpperCAmelCase									=  False
			return pure_symbols, assignment





def 				A      (	_UpperCAmelCase							:  list[Clause]				,  _UpperCAmelCase							:  dict[str, bool | None]       )							->					tuple[list[str], dict[str, bool | None]]:







			'''simple docstring'''
			_UpperCAmelCase									=  []
			for clause in clauses:
						if len(_UpperCAmelCase       ) == 1:
									unit_symbols.append(list(clause.literals.keys()       )[0]       )
						else:
									_UpperCAmelCase			,	_UpperCAmelCase									=  0, 0
									for literal, value in clause.literals.items():
												if value is False:
															f_count += 1
												elif value is None:
															_UpperCAmelCase									=  literal
															n_count += 1
									if f_count == len(_UpperCAmelCase       ) - 1 and n_count == 1:
												unit_symbols.append(_UpperCAmelCase       )
			_UpperCAmelCase									=  {}
			for i in unit_symbols:
						_UpperCAmelCase									=  i[:2]
						_UpperCAmelCase									=  len(_UpperCAmelCase       ) == 2
			_UpperCAmelCase									=  [i[:2] for i in unit_symbols]

			return unit_symbols, assignment





def 				A      (	_UpperCAmelCase							:  list[Clause]				,  _UpperCAmelCase							:  list[str]				,  _UpperCAmelCase							:  dict[str, bool | None]       )							->					tuple[bool | None, dict[str, bool | None] | None]:







			'''simple docstring'''
			_UpperCAmelCase									=  True
			for clause in clauses:
						_UpperCAmelCase									=  clause.evaluate(_UpperCAmelCase       )
						if clause_check is False:
									return False, None
						elif clause_check is None:
									_UpperCAmelCase									=  False
									continue

			if check_clause_all_true:
						return True, model

			try:
						_UpperCAmelCase			,	_UpperCAmelCase									=  find_pure_symbols(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
			except RecursionError:
						print('raises a RecursionError and is'       )
						return None, {}
			_UpperCAmelCase									=  None
			if len(_UpperCAmelCase       ) > 0:
						_UpperCAmelCase			,	_UpperCAmelCase									=  pure_symbols[0], assignment[pure_symbols[0]]

			if p:
						_UpperCAmelCase									=  model
						_UpperCAmelCase									=  value
						_UpperCAmelCase									=  list(_UpperCAmelCase       )
						if p in tmp_symbols:
									tmp_symbols.remove(_UpperCAmelCase       )
						return dpll_algorithm(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			_UpperCAmelCase			,	_UpperCAmelCase									=  find_unit_clauses(_UpperCAmelCase				,  _UpperCAmelCase       )
			_UpperCAmelCase									=  None
			if len(_UpperCAmelCase       ) > 0:
						_UpperCAmelCase			,	_UpperCAmelCase									=  unit_symbols[0], assignment[unit_symbols[0]]
			if p:
						_UpperCAmelCase									=  model
						_UpperCAmelCase									=  value
						_UpperCAmelCase									=  list(_UpperCAmelCase       )
						if p in tmp_symbols:
									tmp_symbols.remove(_UpperCAmelCase       )
						return dpll_algorithm(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
			_UpperCAmelCase									=  symbols[0]
			_UpperCAmelCase									=  symbols[1:]
			_UpperCAmelCase			,	_UpperCAmelCase									=  model, model
			_UpperCAmelCase			,	_UpperCAmelCase									=  True, False

			return dpll_algorithm(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       ) or dpll_algorithm(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase__  =			generate_formula()
							print(f"""The formula {formula} is""", end=" ")

							UpperCAmelCase__  ,    UpperCAmelCase__  =			generate_parameters(formula)
							UpperCAmelCase__  ,    UpperCAmelCase__  =			dpll_algorithm(clauses, symbols, {})

							if solution:
														print(f"""satisfiable with the assignment {model}.""")
							else:
														print("not satisfiable.")





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


UpperCAmelCase__  =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Optional[Any]       )							->					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 				A      (	_UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Optional[Any]       )							->					Dict:







			'''simple docstring'''
			return max(metric_fn(_UpperCAmelCase				,  _UpperCAmelCase       ) for gt in ground_truths       )





def 				A      (	_UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Optional[Any]				,  _UpperCAmelCase							:  str       )							->					Any:







			'''simple docstring'''
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  []

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

			_UpperCAmelCase									=  _UpperCAmelCase									=  _UpperCAmelCase									=  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       )

			_UpperCAmelCase									=  100.0 * em / total
			_UpperCAmelCase									=  100.0 * fa / total

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





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Any       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  args.k
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]

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

			_UpperCAmelCase									=  100.0 * em / total
			logger.info(F"Precision@{k}: {em: .2f}"       )





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Union[str, Any]       )							->					List[Any]:







			'''simple docstring'''
			def strip_title(_UpperCAmelCase							:  List[Any]       ):
						if title.startswith('"'       ):
									_UpperCAmelCase									=  title[1:]
						if title.endswith('"'       ):
									_UpperCAmelCase									=  title[:-1]
						return title

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

			_UpperCAmelCase									=  rag_model.rag.question_encoder(_UpperCAmelCase       )
			_UpperCAmelCase									=  question_enc_outputs[0]

			_UpperCAmelCase									=  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'				,  )
			_UpperCAmelCase									=  rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
			_UpperCAmelCase									=  []
			for docs in all_docs:
						_UpperCAmelCase									=  [strip_title(_UpperCAmelCase       ) for title in docs['title']]
						provenance_strings.append('\t'.join(_UpperCAmelCase       )       )
			return provenance_strings





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[str]				,  _UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''simple docstring'''
			with torch.no_grad():
						_UpperCAmelCase									=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    _UpperCAmelCase				,  return_tensors='pt'				,  padding=_UpperCAmelCase				,  truncation=_UpperCAmelCase       )

						_UpperCAmelCase									=  inputs_dict.input_ids.to(args.device       )
						_UpperCAmelCase									=  inputs_dict.attention_mask.to(args.device       )
						_UpperCAmelCase									=  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]]				,  )
						_UpperCAmelCase									=  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 				A      (	)							->					Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  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.'				,  )
			_UpperCAmelCase									=  parser.parse_args()
			_UpperCAmelCase									=  torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
			return args





def 				A      (	_UpperCAmelCase							:  Any       )							->					int:







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

			_UpperCAmelCase									=  (
			    [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       )

			_UpperCAmelCase									=  get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			_UpperCAmelCase									=  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'       ):
									_UpperCAmelCase									=  RagRetriever.from_pretrained(_UpperCAmelCase				,  **_UpperCAmelCase       )
									_UpperCAmelCase									=  model_class.from_pretrained(_UpperCAmelCase				,  retriever=_UpperCAmelCase				,  **_UpperCAmelCase       )
									model.retriever.init_retrieval()
						else:
									_UpperCAmelCase									=  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:
									_UpperCAmelCase									=  []
									for line in tqdm(_UpperCAmelCase       ):
												questions.append(line.strip()       )
												if len(_UpperCAmelCase       ) == args.eval_batch_size:
															_UpperCAmelCase									=  evaluate_batch_fn(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
															preds_file.write('\n'.join(_UpperCAmelCase       ) + '\n'       )
															preds_file.flush()
															_UpperCAmelCase									=  []
									if len(_UpperCAmelCase       ) > 0:
												_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			get_args()
							main(args)
