


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


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

transformers_logging.set_verbosity_info()
def 							_UpperCAmelCase    (UpperCamelCase__					:  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 							_UpperCAmelCase    (UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  int						):
       return max(metric_fn(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						) for gt in ground_truths						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Optional[Any]						):
       _A				:	int       =			[line.strip() for line in open(SCREAMING_SNAKE_CASE__    ,							"r"						).readlines()]
       _A				:	List[str]       =			[]

       if args.gold_data_mode == "qa":
              _A				:	Optional[int]       =			pd.read_csv(SCREAMING_SNAKE_CASE__    ,							sep="\t"    ,							header=SCREAMING_SNAKE_CASE__						)
              for answer_list in data[1]:
                     _A				:	List[Any]       =			ast.literal_eval(SCREAMING_SNAKE_CASE__						)
                     answers.append(SCREAMING_SNAKE_CASE__						)
       else:
              _A				:	str       =			[line.strip() for line in open(SCREAMING_SNAKE_CASE__    ,							"r"						).readlines()]
              _A				:	int       =			[[reference] for reference in references]

       _A				:	Tuple       =			0
       for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						):
              total += 1
              em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						)
              fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						)

       _A				:	int       =			100.0 * em / total
       _A				:	List[Any]       =			100.0 * fa / total

       logger.info(f"F1: {fa:.2f}"						)
       logger.info(f"EM: {em:.2f}"						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Tuple						):
       _A				:	List[Any]       =			args.k
       _A				:	Union[str, Any]       =			[line.strip() for line in open(SCREAMING_SNAKE_CASE__    ,							"r"						).readlines()]
       _A				:	Any       =			[line.strip() for line in open(SCREAMING_SNAKE_CASE__    ,							"r"						).readlines()]

       _A				:	Any       =			0
       for hypo, reference in zip(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						):
              _A				:	Any       =			set(hypo.split("\t"						)[:k]						)
              _A				:	List[str]       =			set(reference.split("\t"						)						)
              total += 1
              em += len(hypo_provenance & ref_provenance						) / k

       _A				:	Tuple       =			100.0 * em / total
       logger.info(f"Precision@{k}: {em: .2f}"						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  str    ,							UpperCamelCase__					:  Dict						):





       def strip_title(UpperCamelCase__					:  str						):
              if title.startswith("\""						):
                     _A				:	List[Any]       =			title[1:]
              if title.endswith("\""						):
                     _A				:	str       =			title[:-1]
              return title

       _A				:	Any       =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           SCREAMING_SNAKE_CASE__    ,							return_tensors="pt"    ,							padding=SCREAMING_SNAKE_CASE__    ,							truncation=SCREAMING_SNAKE_CASE__    ,							)['input_ids'].to(args.device						)

       _A				:	Optional[int]       =			rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__						)
       _A				:	List[str]       =			question_enc_outputs[0]

       _A				:	Dict       =			rag_model.retriever(
           SCREAMING_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"    ,							)
       _A				:	Union[str, Any]       =			rag_model.retriever.index.get_doc_dicts(result.doc_ids						)
       _A				:	Tuple       =			[]
       for docs in all_docs:
              _A				:	List[Any]       =			[strip_title(SCREAMING_SNAKE_CASE__						) for title in docs['title']]
              provenance_strings.append("\t".join(SCREAMING_SNAKE_CASE__						)						)
       return provenance_strings
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  Tuple						):





       with torch.no_grad():
              _A				:	Tuple       =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  SCREAMING_SNAKE_CASE__    ,							return_tensors="pt"    ,							padding=SCREAMING_SNAKE_CASE__    ,							truncation=SCREAMING_SNAKE_CASE__						)

              _A				:	Dict       =			inputs_dict.input_ids.to(args.device						)
              _A				:	str       =			inputs_dict.attention_mask.to(args.device						)
              _A				:	Any       =			rag_model.generate(  # rag_model overwrites generate
                  SCREAMING_SNAKE_CASE__    ,							attention_mask=SCREAMING_SNAKE_CASE__    ,							num_beams=args.num_beams    ,							min_length=args.min_length    ,							max_length=args.max_length    ,							early_stopping=SCREAMING_SNAKE_CASE__    ,							num_return_sequences=1    ,							bad_words_ids=[[0, 0]]    ,							)
              _A				:	Tuple       =			rag_model.retriever.generator_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__    ,							skip_special_tokens=SCREAMING_SNAKE_CASE__						)

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

              return answers
def 							_UpperCAmelCase    ():
       _A				:	List[Any]       =			argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"    ,							choices=["rag_sequence", "rag_token", "bart"]    ,							type=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,							choices=["exact", "compressed", "legacy"]    ,							type=SCREAMING_SNAKE_CASE__    ,							help="RAG model retriever type"    ,							)
       parser.add_argument(
           "--index_path"    ,							default=SCREAMING_SNAKE_CASE__    ,							type=SCREAMING_SNAKE_CASE__    ,							help="Path to the retrieval index"    ,							)
       parser.add_argument("--n_docs"    ,							default=5    ,							type=SCREAMING_SNAKE_CASE__    ,							help="Number of retrieved docs"						)
       parser.add_argument(
           "--model_name_or_path"    ,							default=SCREAMING_SNAKE_CASE__    ,							type=SCREAMING_SNAKE_CASE__    ,							required=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,							help="k for the precision@k calculation"						)
       parser.add_argument(
           "--evaluation_set"    ,							default=SCREAMING_SNAKE_CASE__    ,							type=SCREAMING_SNAKE_CASE__    ,							required=SCREAMING_SNAKE_CASE__    ,							help="Path to a file containing evaluation samples"    ,							)
       parser.add_argument(
           "--gold_data_path"    ,							default=SCREAMING_SNAKE_CASE__    ,							type=SCREAMING_SNAKE_CASE__    ,							required=SCREAMING_SNAKE_CASE__    ,							help="Path to a tab-separated file with gold samples"    ,							)
       parser.add_argument(
           "--gold_data_mode"    ,							default="qa"    ,							type=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,							help="Number of beams to be used when generating answers"    ,							)
       parser.add_argument("--min_length"    ,							default=1    ,							type=SCREAMING_SNAKE_CASE__    ,							help="Min length of the generated answers"						)
       parser.add_argument("--max_length"    ,							default=50    ,							type=SCREAMING_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."    ,							)
       _A				:	List[str]       =			parser.parse_args()
       _A				:	Dict       =			torch.device("cuda" if torch.cuda.is_available() else "cpu"						)
       return args
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple						):
       _A				:	Any       =			{}
       if args.model_type is None:
              _A				:	Tuple       =			infer_model_type(args.model_name_or_path						)
              assert args.model_type is not None
       if args.model_type.startswith("rag"						):
              _A				:	Any       =			RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
              _A				:	Tuple       =			args.n_docs
              if args.index_name is not None:
                     _A				:	List[str]       =			args.index_name
              if args.index_path is not None:
                     _A				:	str       =			args.index_path
       else:
              _A				:	List[Any]       =			BartForConditionalGeneration

       _A				:	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"    ,							SCREAMING_SNAKE_CASE__						)

       _A				:	Dict       =			get_scores if args.eval_mode == 'e2e' else get_precision_at_k
       _A				:	List[str]       =			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(SCREAMING_SNAKE_CASE__    ,							args.predictions_path    ,							args.gold_data_path						)
                     continue

              logger.info("***** Running evaluation for {} *****".format(SCREAMING_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"						):
                     _A				:	Union[str, Any]       =			RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__    ,							**SCREAMING_SNAKE_CASE__						)
                     _A				:	Tuple       =			model_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,							retriever=SCREAMING_SNAKE_CASE__    ,							**SCREAMING_SNAKE_CASE__						)
                     model.retriever.init_retrieval()
              else:
                     _A				:	List[str]       =			model_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,							**SCREAMING_SNAKE_CASE__						)
              model.to(args.device						)

              with open(args.evaluation_set    ,							"r"						) as eval_file, open(args.predictions_path    ,							"w"						) as preds_file:
                     _A				:	Optional[Any]       =			[]
                     for line in tqdm(SCREAMING_SNAKE_CASE__						):
                            questions.append(line.strip()						)
                            if len(SCREAMING_SNAKE_CASE__						) == args.eval_batch_size:
                                   _A				:	str       =			evaluate_batch_fn(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						)
                                   preds_file.write("\n".join(SCREAMING_SNAKE_CASE__						) + "\n"						)
                                   preds_file.flush()
                                   _A				:	int       =			[]
                     if len(SCREAMING_SNAKE_CASE__						) > 0:
                            _A				:	Optional[int]       =			evaluate_batch_fn(SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__    ,							SCREAMING_SNAKE_CASE__						)
                            preds_file.write("\n".join(SCREAMING_SNAKE_CASE__						)						)
                            preds_file.flush()

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


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




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
