




"""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


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

transformers_logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase				)						->    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_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[Any]:
      return max(metric_fn(__UpperCAmelCase    ,		__UpperCAmelCase				) for gt in ground_truths				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Optional[int]   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]
      lowerCAmelCase__      :			Dict   =	[]

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

      lowerCAmelCase__      :			Optional[Any]   =	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				)

      lowerCAmelCase__      :			List[str]   =	100.0 * em / total
      lowerCAmelCase__      :			List[str]   =	100.0 * fa / total

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Any:
      lowerCAmelCase__      :			Union[str, Any]   =	args.k
      lowerCAmelCase__      :			str   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]
      lowerCAmelCase__      :			Optional[int]   =	[line.strip() for line in open(__UpperCAmelCase    ,		"""r"""				).readlines()]

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

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      def strip_title(__UpperCAmelCase				):
            if title.startswith("""\""""				):
                  lowerCAmelCase__      :			List[Any]   =	title[1:]
            if title.endswith("""\""""				):
                  lowerCAmelCase__      :			Tuple   =	title[:-1]
            return title

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

      lowerCAmelCase__      :			Any   =	rag_model.rag.question_encoder(__UpperCAmelCase				)
      lowerCAmelCase__      :			Dict   =	question_enc_outputs[0]

      lowerCAmelCase__      :			Dict   =	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"""    ,		)
      lowerCAmelCase__      :			int   =	rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
      lowerCAmelCase__      :			int   =	[]
      for docs in all_docs:
            lowerCAmelCase__      :			Union[str, Any]   =	[strip_title(__UpperCAmelCase				) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(__UpperCAmelCase				)				)
      return provenance_strings



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      with torch.no_grad():
            lowerCAmelCase__      :			int   =	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                __UpperCAmelCase    ,		return_tensors="""pt"""    ,		padding=__UpperCAmelCase    ,		truncation=__UpperCAmelCase				)

            lowerCAmelCase__      :			Tuple   =	inputs_dict.input_ids.to(args.device				)
            lowerCAmelCase__      :			List[str]   =	inputs_dict.attention_mask.to(args.device				)
            lowerCAmelCase__      :			List[str]   =	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]]    ,		)
            lowerCAmelCase__      :			Dict   =	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 	lowercase_				(			)						->    Dict:
      lowerCAmelCase__      :			List[Any]   =	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."""    ,		)
      lowerCAmelCase__      :			int   =	parser.parse_args()
      lowerCAmelCase__      :			List[Any]   =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
      return args



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			List[Any]   =	{}
      if args.model_type is None:
            lowerCAmelCase__      :			Tuple   =	infer_model_type(args.model_name_or_path				)
            assert args.model_type is not None
      if args.model_type.startswith("""rag"""				):
            lowerCAmelCase__      :			Any   =	RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
            lowerCAmelCase__      :			Dict   =	args.n_docs
            if args.index_name is not None:
                  lowerCAmelCase__      :			str   =	args.index_name
            if args.index_path is not None:
                  lowerCAmelCase__      :			List[str]   =	args.index_path
      else:
            lowerCAmelCase__      :			Optional[Any]   =	BartForConditionalGeneration

      lowerCAmelCase__      :			Union[str, Any]   =	(
          [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				)

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











"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		List[Any]=""   ,     UpperCamelCase       :		List[str]="train"   )			->						List[Any]:

         """simple docstring"""
         assert os.path.isdir(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	[]
         lowerCAmelCase__      :			List[Any]   =	os.listdir(UpperCamelCase   )
         for story_filename in story_filenames_list:
               if "summary" in story_filename:
                     continue
               lowerCAmelCase__      :			Any   =	os.path.join(UpperCamelCase   ,     UpperCamelCase   )
               if not os.path.isfile(UpperCamelCase   ):
                     continue
               self.documents.append(UpperCamelCase   )






   def __len__(		self       :		List[Any]   )			->						int:

         """simple docstring"""
         return len(self.documents   )






   def __getitem__(		self       :		str   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.documents[idx]
         lowerCAmelCase__      :			List[Any]   =	document_path.split("""/"""   )[-1]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as source:
               lowerCAmelCase__      :			List[str]   =	source.read()
               lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	process_story(UpperCamelCase   )
         return document_name, story_lines, summary_lines






def 	lowercase_				(			__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Tuple   =	list(filter(lambda __UpperCAmelCase				: len(__UpperCAmelCase				) != 0    ,		[line.strip() for line in raw_story.split("""\n"""				)]				)				)

      # for some unknown reason some lines miss a period, add it
      lowerCAmelCase__      :			List[str]   =	[_add_missing_period(__UpperCAmelCase				) for line in nonempty_lines]

      # gather article lines
      lowerCAmelCase__      :			Optional[int]   =	[]
      lowerCAmelCase__      :			Optional[int]   =	deque(__UpperCAmelCase				)
      while True:
            try:
                  lowerCAmelCase__      :			List[Any]   =	lines.popleft()
                  if element.startswith("""@highlight"""				):
                        break
                  story_lines.append(__UpperCAmelCase				)
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      lowerCAmelCase__      :			Any   =	list(filter(lambda __UpperCAmelCase				: not t.startswith("""@highlight"""				)    ,		__UpperCAmelCase				)				)

      return story_lines, summary_lines



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			List[str]   =	[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""				):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      if len(__UpperCAmelCase				) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase				))				)
            return sequence



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      lowerCAmelCase__      :			List[str]   =	torch.ones_like(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	sequence == pad_token_id
      lowerCAmelCase__      :			str   =	0
      return mask



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			Tuple   =	[tokenizer.encode(__UpperCAmelCase				) for line in story_lines]
      lowerCAmelCase__      :			List[str]   =	[token for sentence in story_lines_token_ids for token in sentence]
      lowerCAmelCase__      :			int   =	[tokenizer.encode(__UpperCAmelCase				) for line in summary_lines]
      lowerCAmelCase__      :			Union[str, Any]   =	[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Tuple   =	[]
      for sequence in batch:
            lowerCAmelCase__      :			Union[str, Any]   =	-1
            lowerCAmelCase__      :			List[str]   =	[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2				)
            batch_embeddings.append(__UpperCAmelCase				)
      return torch.tensor(__UpperCAmelCase				)





