






"""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


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

transformers_logging.set_verbosity_info()







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]			)				->    List[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	:		Any		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		int			)				->    Optional[int]:
  return max(metric_fn(_lowerCamelCase		,    _lowerCamelCase			) for gt in ground_truths			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Union[str, Any]			)				->    Dict:
  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_   					=						1_00.0 * em / total
  lowerCamelCase_   					=						1_00.0 * fa / total

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict			)				->    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_   					=						1_00.0 * em / total
  logger.info(F'''Precision@{k}: {em: .2f}'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Any			)				->    int:
  def strip_title(_lowerCamelCase	:		List[Any]			):
    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 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		int			)				->    Dict:
  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 					lowerCamelCase__	(							)				->    Optional[int]:
  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 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]			)				->    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__":
  _SCREAMING_SNAKE_CASE :    List[str]    =						get_args()
  main(args)












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						[
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE :    List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




