






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

transformers_logging.set_verbosity_info()






def  UpperCAmelCase__						(    lowerCamelCase	:			str    ):
   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__						(    lowerCamelCase	:			Optional[Any]			,lowerCamelCase	:			Optional[Any]			,lowerCamelCase	:			str    ):
   return max(metric_fn(lowerCamelCase			,lowerCamelCase    ) for gt in ground_truths    )






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict			,lowerCamelCase	:			Any			,lowerCamelCase	:			List[str]    ):
   lowerCamelCase									=[line.strip() for line in open(lowerCamelCase			,"""r"""    ).readlines()]
   lowerCamelCase									=[]

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

   lowerCamelCase									=lowerCamelCase									=lowerCamelCase									=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    )

   lowerCamelCase									=100.0 * em / total
   lowerCamelCase									=100.0 * fa / total

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






def  UpperCAmelCase__						(    lowerCamelCase	:			str			,lowerCamelCase	:			int			,lowerCamelCase	:			int    ):
   lowerCamelCase									=args.k
   lowerCamelCase									=[line.strip() for line in open(lowerCamelCase			,"""r"""    ).readlines()]
   lowerCamelCase									=[line.strip() for line in open(lowerCamelCase			,"""r"""    ).readlines()]

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

   lowerCamelCase									=100.0 * em / total
   logger.info(f'''Precision@{k}: {em: .2f}'''    )






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			Tuple			,lowerCamelCase	:			Optional[Any]    ):
   def strip_title(lowerCamelCase	:			Tuple    ):
      if title.startswith("""\""""    ):
         lowerCamelCase									=title[1:]
      if title.endswith("""\""""    ):
         lowerCamelCase									=title[:-1]
      return title

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

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

   lowerCamelCase									=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"""			,)
   lowerCamelCase									=rag_model.retriever.index.get_doc_dicts(result.doc_ids    )
   lowerCamelCase									=[]
   for docs in all_docs:
      lowerCamelCase									=[strip_title(lowerCamelCase    ) for title in docs["""title"""]]
      provenance_strings.append("""\t""".join(lowerCamelCase    )    )
   return provenance_strings






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict			,lowerCamelCase	:			Any			,lowerCamelCase	:			Union[str, Any]    ):
   with torch.no_grad():
      lowerCamelCase									=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          lowerCamelCase			,return_tensors="""pt"""			,padding=lowerCamelCase			,truncation=lowerCamelCase    )

      lowerCamelCase									=inputs_dict.input_ids.to(args.device    )
      lowerCamelCase									=inputs_dict.attention_mask.to(args.device    )
      lowerCamelCase									=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]]			,)
      lowerCamelCase									=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  UpperCAmelCase__						(    ):
   lowerCamelCase									=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."""			,)
   lowerCamelCase									=parser.parse_args()
   lowerCamelCase									=torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
   return args






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									={}
   if args.model_type is None:
      lowerCamelCase									=infer_model_type(args.model_name_or_path    )
      assert args.model_type is not None
   if args.model_type.startswith("""rag"""    ):
      lowerCamelCase									=RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
      lowerCamelCase									=args.n_docs
      if args.index_name is not None:
         lowerCamelCase									=args.index_name
      if args.index_path is not None:
         lowerCamelCase									=args.index_path
   else:
      lowerCamelCase									=BartForConditionalGeneration

   lowerCamelCase									=(
       [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    )

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







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    List[str] 	=     HerbertTokenizer
     __lowerCamelCase       :    Union[str, Any] 	=     HerbertTokenizerFast
     __lowerCamelCase       :    Union[str, Any] 	=     True





     def 						lowerCamelCase							(	self  )		->				int:
        super().setUp()

        # Use a simpler test file without japanese/chinese characters
        with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							, encoding="""utf-8"""  ) as f_data:
           lowerCamelCase									=f_data.read().replace("""\n\n"""							, """\n"""  ).strip()

        lowerCamelCase									=[
            """<s>""",
            """</s>""",
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """w</w>""",
            """r</w>""",
            """t</w>""",
            """lo""",
            """low""",
            """er</w>""",
            """low</w>""",
            """lowest</w>""",
            """newer</w>""",
            """wider</w>""",
            """,</w>""",
            """<unk>""",
        ]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""vocab_file"""]  )
        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""merges_file"""]  )
        with open(self.vocab_file							, """w"""  ) as fp:
           fp.write(json.dumps(_UpperCAmelCase  )  )
        with open(self.merges_file							, """w"""  ) as fp:
           fp.write("""\n""".join(_UpperCAmelCase  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Optional[Any]:
        lowerCamelCase									="""lower newer"""
        lowerCamelCase									="""lower newer"""
        return input_text, output_text





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class(vocab_file=self.vocab_file							, merges_file=self.merges_file  )

        lowerCamelCase									="""lower"""
        lowerCamelCase									=["""low""", """er</w>"""]
        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokens + ["""<unk>"""]
        lowerCamelCase									=[16, 17, 23]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        if not self.test_rust_tokenizer:
           return

        lowerCamelCase									=self.get_tokenizer()
        lowerCamelCase									=self.get_rust_tokenizer()

        lowerCamelCase									="""lower,newer"""

        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=self.get_rust_tokenizer()
        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""  )

        lowerCamelCase									=tokenizer.encode("""konstruowanie sekwencji"""							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.encode("""konstruowanie wielu sekwencji"""							, add_special_tokens=_UpperCAmelCase  )

        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase							, _UpperCAmelCase  )

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + text_a + [2]