


"""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_			     =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Optional[Any]:
		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        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str			) ->      List[str]:
		return max(metric_fn(lowerCAmelCase__    ,					lowerCAmelCase__			) for gt in ground_truths			)

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Dict			) ->      int:
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]
		__a           =			[]

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

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

		__a           =			1_00.0 * em / total
		__a           =			1_00.0 * fa / total

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

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Any			) ->      Any:
		__a           =			args.k
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]

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

		__a           =			1_00.0 * em / total
		logger.info(f'''Precision@{k}: {em: .2f}'''			)

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      Dict:
		def strip_title(lowerCAmelCase__ :     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(
		    lowerCAmelCase__    ,					return_tensors='''pt'''    ,					padding=lowerCAmelCase__    ,					truncation=lowerCAmelCase__    ,					)['''input_ids'''].to(args.device			)

		__a           =			rag_model.rag.question_encoder(lowerCAmelCase__			)
		__a           =			question_enc_outputs[0]

		__a           =			rag_model.retriever(
		    lowerCAmelCase__    ,					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(lowerCAmelCase__			) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(lowerCAmelCase__			)			)
		return provenance_strings

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Any			) ->      Any:
		with torch.no_grad():
				__a           =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCAmelCase__    ,					return_tensors='''pt'''    ,					padding=lowerCAmelCase__    ,					truncation=lowerCAmelCase__			)

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

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

				return answers

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

		__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(lowerCAmelCase__    ,					args.predictions_path    ,					args.gold_data_path			)
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(lowerCAmelCase__			)			)
				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(lowerCAmelCase__    ,					**lowerCAmelCase__			)
						__a           =			model_class.from_pretrained(lowerCAmelCase__    ,					retriever=lowerCAmelCase__    ,					**lowerCAmelCase__			)
						model.retriever.init_retrieval()
				else:
						__a           =			model_class.from_pretrained(lowerCAmelCase__    ,					**lowerCAmelCase__			)
				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(lowerCAmelCase__			):
								questions.append(line.strip()			)
								if len(lowerCAmelCase__			) == args.eval_batch_size:
										__a           =			evaluate_batch_fn(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
										preds_file.write('''\n'''.join(lowerCAmelCase__			) + '''\n'''			)
										preds_file.flush()
										__a           =			[]
						if len(lowerCAmelCase__			) > 0:
								__a           =			evaluate_batch_fn(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
								preds_file.write('''\n'''.join(lowerCAmelCase__			)			)
								preds_file.flush()

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


if __name__ == "__main__":
				lowercase_			     =  get_args()
				main(args)










"""simple docstring"""




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowercase_			     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_			     =  1_0
lowercase_			     =  2_5_6

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Optional[MinHash]:
		if len(lowerCAmelCase__			) < MIN_NUM_TOKENS:
				return None
		__a           =			MinHash(num_perm=lowerCAmelCase__			)
		for token in set(lowerCAmelCase__			):
				min_hash.update(token.encode()			)
		return min_hash

def        lowercase							(		lowerCAmelCase__ :     str			) ->      Set[str]:
		return {t for t in NON_ALPHA.split(lowerCAmelCase__			) if len(t.strip()			) > 0}
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, *,
		    _a = 0.85							, ):
				__a           =			duplication_jaccard_threshold
				__a           =			NUM_PERM
				__a           =			MinHashLSH(threshold=self._duplication_jaccard_threshold							, num_perm=self._num_perm      )

				__a           =			defaultdict(_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			self._index.query(_a      )
				if code_key in self._index.keys:
						print(f'''Duplicate key {code_key}'''      )
						return

				self._index.insert(_a							, _a      )
				if len(_a      ) > 0:
						for base_duplicate in close_duplicates:
								if base_duplicate in self._duplicate_clusters:
										self._duplicate_clusters[base_duplicate].add(_a      )
										break
						else:
								self._duplicate_clusters[close_duplicates[0]].add(_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			[]
				for base, duplicates in self._duplicate_clusters.items():
						__a           =			[base] + list(_a      )
						# reformat the cluster to be a list of dict
						__a           =			[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
						duplicate_clusters.append(_a      )
				return duplicate_clusters





		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.get_duplicate_clusters()
				with open(_a							, '''w'''      ) as f:
						json.dump(_a							, _a      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      int:
		__a      ,     __a           =			element
		__a           =			get_min_hash([t for t in NON_ALPHA.split(data['''content''']			) if len(t.strip()			) > 0]			)
		if min_hash is not None:
				return (index, data["repo_name"], data["path"]), min_hash

def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]			) ->      str:
		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash    ,					ThreadedIterator(lowerCAmelCase__    ,					max_queue_size=10000			)    ,					chunksize=100    ,					):
						if data is not None:
								yield data

def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float			) ->      Dict:
		__a           =			DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__			)

		for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase__			)			)    ,					max_queue_size=100			)			):
				di.add(lowerCAmelCase__    ,					lowerCAmelCase__			)

		# Returns a List[Cluster] where Cluster is List[str] with the filenames.
		return di.get_duplicate_clusters()

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      float:
		__a           =			get_tokens(lowerCAmelCase__			)
		__a           =			get_tokens(lowerCAmelCase__			)
		return len(tokensa & tokensa			) / len(tokensa | tokensa			)


lowercase_			     =  None

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Any:
		__a           =			[]
		for elementa in cluster:
				__a           =			_shared_dataset[elementa['''base_index''']]['''content''']
				for elementa in extremes:
						__a           =			_shared_dataset[elementa['''base_index''']]['''content''']
						if jaccard_similarity(lowerCAmelCase__    ,					lowerCAmelCase__			) >= jaccard_threshold:
								elementa["copies"] += 1
								break
				else:
						__a           =			1
						extremes.append(lowerCAmelCase__			)
		return extremes

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]			) ->      Optional[int]:
		global _shared_dataset
		__a           =			dataset
		__a           =			[]
		__a           =			partial(_find_cluster_extremes_shared    ,					jaccard_threshold=lowerCAmelCase__			)
		with mp.Pool() as pool:
				for extremes in tqdm(
				    pool.imap_unordered(
				        lowerCAmelCase__    ,					lowerCAmelCase__    ,					)    ,					total=len(lowerCAmelCase__			)    ,					):
						extremes_list.append(lowerCAmelCase__			)
		return extremes_list




def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float = 0.85			) ->      Tuple[Type[Dataset], List[List[Dict]]]:
		__a           =			make_duplicate_clusters(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
		__a           =			{}
		__a           =			find_extremes(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		for extremes in extremes_clusters:
				for element in extremes:
						__a           =			element
		__a           =			duplicate_indices - set(extreme_dict.keys()			)
		__a           =			dataset.filter(lambda lowerCAmelCase__    ,					lowerCAmelCase__			: idx not in remove_indices    ,					with_indices=lowerCAmelCase__			)

		# update duplicate_clusters
		for cluster in duplicate_clusters:
				for element in cluster:
						__a           =			element['''base_index'''] in extreme_dict
						if element["is_extreme"]:
								__a           =			extreme_dict[element['''base_index''']]['''copies''']

		print(f'''Original dataset size: {len(lowerCAmelCase__			)}'''			)
		print(f'''Number of duplicate clusters: {len(lowerCAmelCase__			)}'''			)
		print(f'''Files in duplicate cluster: {len(lowerCAmelCase__			)}'''			)
		print(f'''Unique files in duplicate cluster: {len(lowerCAmelCase__			)}'''			)
		print(f'''Filtered dataset size: {len(lowerCAmelCase__			)}'''			)

		return ds_filter, duplicate_clusters






