



'''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


UpperCamelCase__ :			Any												=				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def        lowerCAmelCase_     (			_lowerCamelCase:     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        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     Tuple      ):
  return max(metric_fn(_lowerCAmelCase ,   _lowerCAmelCase      ) for gt in ground_truths      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     Dict      ):
  __SCREAMING_SNAKE_CASE :	Any					 =	[line.strip() for line in open(_lowerCAmelCase ,   """r"""      ).readlines()]
  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[]

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

  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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      )

  __SCREAMING_SNAKE_CASE :	Dict					 =	1_00.0 * em / total
  __SCREAMING_SNAKE_CASE :	Dict					 =	1_00.0 * fa / total

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


def        lowerCAmelCase_     (			_lowerCamelCase:     List[Any] ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     str      ):
  __SCREAMING_SNAKE_CASE :	int					 =	args.k
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[line.strip() for line in open(_lowerCAmelCase ,   """r"""      ).readlines()]
  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[line.strip() for line in open(_lowerCAmelCase ,   """r"""      ).readlines()]

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

  __SCREAMING_SNAKE_CASE :	List[Any]					 =	1_00.0 * em / total
  logger.info(F"Precision@{k}: {em: .2f}"      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     Any ,   _lowerCamelCase:     Any      ):




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

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

  __SCREAMING_SNAKE_CASE :	Any					 =	rag_model.rag.question_encoder(_lowerCAmelCase      )
  __SCREAMING_SNAKE_CASE :	str					 =	question_enc_outputs[0]

  __SCREAMING_SNAKE_CASE :	List[str]					 =	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""" ,   )
  __SCREAMING_SNAKE_CASE :	Tuple					 =	rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
  __SCREAMING_SNAKE_CASE :	str					 =	[]
  for docs in all_docs:
    __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[strip_title(_lowerCAmelCase      ) for title in docs["title"]]
    provenance_strings.append("""\t""".join(_lowerCAmelCase      )      )
  return provenance_strings


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     List[Any] ,   _lowerCamelCase:     Optional[int]      ):




  with torch.no_grad():
    __SCREAMING_SNAKE_CASE :	Any					 =	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _lowerCAmelCase ,   return_tensors="""pt""" ,   padding=_lowerCAmelCase ,   truncation=_lowerCAmelCase      )

    __SCREAMING_SNAKE_CASE :	Optional[int]					 =	inputs_dict.input_ids.to(args.device      )
    __SCREAMING_SNAKE_CASE :	Any					 =	inputs_dict.attention_mask.to(args.device      )
    __SCREAMING_SNAKE_CASE :	Dict					 =	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]] ,   )
    __SCREAMING_SNAKE_CASE :	str					 =	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        lowerCAmelCase_     (			):
  __SCREAMING_SNAKE_CASE :	Any					 =	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.""" ,   )
  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	parser.parse_args()
  __SCREAMING_SNAKE_CASE :	Tuple					 =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
  return args


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{}
  if args.model_type is None:
    __SCREAMING_SNAKE_CASE :	List[Any]					 =	infer_model_type(args.model_name_or_path      )
    assert args.model_type is not None
  if args.model_type.startswith("""rag"""      ):
    __SCREAMING_SNAKE_CASE :	Any					 =	RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
    __SCREAMING_SNAKE_CASE :	Dict					 =	args.n_docs
    if args.index_name is not None:
      __SCREAMING_SNAKE_CASE :	List[Any]					 =	args.index_name
    if args.index_path is not None:
      __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	args.index_path
  else:
    __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	BartForConditionalGeneration

  __SCREAMING_SNAKE_CASE :	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""" ,   _lowerCAmelCase      )

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




'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[str]												=				{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	'''t5'''
			_A   :	Optional[Any]    					=	['''past_key_values''']
			_A   :	Any    					=	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=3_2_1_2_8							,  lowerCAmelCase__  :				Dict=5_1_2							,  lowerCAmelCase__  :				str=6_4							,  lowerCAmelCase__  :				Tuple=2_0_4_8							,  lowerCAmelCase__  :				Optional[Any]=6							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Any=8							,  lowerCAmelCase__  :				Dict=3_2							,  lowerCAmelCase__  :				Dict=1_2_8							,  lowerCAmelCase__  :				Any=0.1							,  lowerCAmelCase__  :				str=1E-6							,  lowerCAmelCase__  :				str=1.0							,  lowerCAmelCase__  :				Optional[Any]="relu"							,  lowerCAmelCase__  :				List[str]=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				List[Any]=1							,  **lowerCAmelCase__  :				Dict							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	str					 =	d_model
					__SCREAMING_SNAKE_CASE :	str					 =	d_kv
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	d_ff
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Any					 =	relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE :	int					 =	relative_attention_max_distance
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dropout_rate
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_norm_epsilon
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	initializer_factor
					__SCREAMING_SNAKE_CASE :	Any					 =	feed_forward_proj
					__SCREAMING_SNAKE_CASE :	str					 =	use_cache

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.feed_forward_proj.split("""-"""		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	act_info[-1]
					__SCREAMING_SNAKE_CASE :	int					 =	act_info[0] == """gated"""

					if len(lowerCAmelCase__		) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__		) > 2:
							raise ValueError(
							    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
							    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
							    """'gated-gelu' or 'relu'"""		)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	"""gelu_new"""

					super().__init__(
					    pad_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__							,  )






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""past_encoder_sequence + sequence"""
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch"""}
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase__							,  direction="""inputs"""		)

					return common_inputs



			@property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return 1_3