
'''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__ :		int					=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							int:





  """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_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	List[str]			)					->							Optional[Any]:





  """simple docstring"""

  return max(metric_fn(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			) for gt in ground_truths			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							List[Any]:





  """simple docstring"""

  __A		:  str       				=		[line.strip() for line in open(SCREAMING_SNAKE_CASE__      ,  """r"""			).readlines()]
  __A		:  Tuple       				=		[]

  if args.gold_data_mode == "qa":
    __A		:  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		:  List[str]       				=		[line.strip() for line in open(SCREAMING_SNAKE_CASE__      ,  """r"""			).readlines()]
    __A		:  Any       				=		[[reference] for reference in references]

  __A		:  Any       				=		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		:  str       				=		100.0 * em / total
  __A		:  Any       				=		100.0 * fa / total

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	Dict			)					->							Optional[Any]:





  """simple docstring"""

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

  __A		:  Optional[Any]       				=		100.0 * em / total
  logger.info(F"Precision@{k}: {em: .2f}"			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							str:





  """simple docstring"""

  def strip_title(__SCREAMING_SNAKE_CASE    :	List[str]			):
    if title.startswith("""\""""			):
      __A		:  List[str]       				=		title[1:]
    if title.endswith("""\""""			):
      __A		:  Optional[int]       				=		title[:-1]
    return title

  __A		:  Dict       				=		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		:  Any       				=		rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__			)
  __A		:  List[str]       				=		question_enc_outputs[0]

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							List[str]:





  """simple docstring"""

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

    __A		:  Optional[Any]       				=		inputs_dict.input_ids.to(args.device			)
    __A		:  Dict       				=		inputs_dict.attention_mask.to(args.device			)
    __A		:  Tuple       				=		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		:  str       				=		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 							A_  (    )					->							Union[str, Any]:





  """simple docstring"""

  __A		:  Optional[int]       				=		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		:  str       				=		parser.parse_args()
  __A		:  Optional[int]       				=		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)
  return args







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  int       				=		{}
  if args.model_type is None:
    __A		:  str       				=		infer_model_type(args.model_name_or_path			)
    assert args.model_type is not None
  if args.model_type.startswith("""rag"""			):
    __A		:  Tuple       				=		RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
    __A		:  Optional[int]       				=		args.n_docs
    if args.index_name is not None:
      __A		:  List[str]       				=		args.index_name
    if args.index_path is not None:
      __A		:  Optional[Any]       				=		args.index_path
  else:
    __A		:  List[Any]       				=		BartForConditionalGeneration

  __A		:  List[str]       				=		(
      [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		:  int       				=		get_scores if args.eval_mode == """e2e""" else get_precision_at_k
  __A		:  int       				=		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		:  Tuple       				=		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__			)
      __A		:  int       				=		model_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,  retriever=SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__			)
      model.retriever.init_retrieval()
    else:
      __A		:  Any       				=		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		:  Dict       				=		[]
      for line in tqdm(SCREAMING_SNAKE_CASE__			):
        questions.append(line.strip()			)
        if len(SCREAMING_SNAKE_CASE__			) == args.eval_batch_size:
          __A		:  Tuple       				=		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		:  List[Any]       				=		[]
      if len(SCREAMING_SNAKE_CASE__			) > 0:
        __A		:  Tuple       				=		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__":
     A__ :		Tuple					=get_args()
     main(args)










from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1
       lowerCAmelCase__ 						=	2
       lowerCAmelCase__ 						=	3




       def __repr__(    self					:       Optional[int]						):
         return f'''{self.__class__.__name__}.{self.name}'''







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       float:
  __snake_case    ,  __snake_case:		Union[str, Any]					    =		point
  return degrees(atana(y - miny   ,   x - minx))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Direction:
  __snake_case    ,  __snake_case:		Dict					    =		starting
  __snake_case    ,  __snake_case:		List[str]					    =		via
  __snake_case    ,  __snake_case:		Optional[int]					    =		target
  __snake_case:		int					    =		degrees(atana(ya - ya   ,   xa - xa))
  via_angle %= 360
  __snake_case:		List[Any]					    =		degrees(atana(ya - ya   ,   xa - xa))
  target_angle %= 360
  # t-
  #  \ \
  #   \ v
  #    \|
  #     s
  # via_angle is always lower than target_angle, if direction is left.
  # If they are same, it means they are on a same line of convex hull.
  if target_angle > via_angle:
    return Direction.left
  elif target_angle == via_angle:
    return Direction.straight
  else:
    return Direction.right







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       list[tuple[int, int]]:

  if len(SCREAMING_SNAKE_CASE__) <= 2:
    # There is no convex hull
    raise ValueError("""graham_scan: argument must contain more than 3 points.""")
  if len(SCREAMING_SNAKE_CASE__) == 3:
    return points
  # find the lowest and the most left point
  __snake_case:		Optional[int]					    =		0
  __snake_case    ,  __snake_case:		Optional[int]					    =		maxsize, maxsize
  for i, point in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:		Tuple					    =		point[0]
    __snake_case:		Optional[Any]					    =		point[1]
    if y < miny:
      __snake_case:		Optional[Any]					    =		y
      __snake_case:		Union[str, Any]					    =		x
      __snake_case:		Any					    =		i
    if y == miny and x < minx:
      __snake_case:		Optional[Any]					    =		x
      __snake_case:		str					    =		i

    # remove the lowest and the most left point from points for preparing for sort
  points.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:		Optional[int]					    =		sorted(SCREAMING_SNAKE_CASE__   ,   key=lambda SCREAMING_SNAKE_CASE__: angle_comparer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  # This insert actually costs complexity,
  # and you should instead add (minx, miny) into stack later.
  # I'm using insert just for easy understanding.
  sorted_points.insert(0   ,   (minx, miny))

  __snake_case:		deque[tuple[int, int]]					    =		deque()
  stack.append(sorted_points[0])
  stack.append(sorted_points[1])
  stack.append(sorted_points[2])
  # In any ways, the first 3 points line are towards left.
  # Because we sort them the angle from minx, miny.
  __snake_case:		Optional[Any]					    =		Direction.left

  for i in range(3   ,   len(SCREAMING_SNAKE_CASE__)):
    while True:
      __snake_case:		Optional[Any]					    =		stack[-2]
      __snake_case:		List[Any]					    =		stack[-1]
      __snake_case:		int					    =		sorted_points[i]
      __snake_case:		Union[str, Any]					    =		check_direction(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

      if next_direction == Direction.left:
        __snake_case:		Dict					    =		Direction.left
        break
      if next_direction == Direction.straight:
        if current_direction == Direction.left:
          # We keep current_direction as left.
          # Because if the straight line keeps as straight,
          # we want to know if this straight line is towards left.
          break
        elif current_direction == Direction.right:
          # If the straight line is towards right,
          # every previous points on those straigh line is not convex hull.
          stack.pop()
      if next_direction == Direction.right:
        stack.pop()
    stack.append(sorted_points[i])
  return list(SCREAMING_SNAKE_CASE__)






