




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     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		List[str]:

    """simple docstring"""


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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  [line.strip() for line in open(lowercase_    ,   '''r'''					).readlines()]
    __UpperCamelCase     						=  []

    if args.gold_data_mode == "qa":
        __UpperCamelCase     						=  pd.read_csv(lowercase_    ,   sep='''\t'''    ,   header=lowercase_					)
        for answer_list in data[1]:
            __UpperCamelCase     						=  ast.literal_eval(lowercase_					)
            answers.append(lowercase_					)
    else:
        __UpperCamelCase     						=  [line.strip() for line in open(lowercase_    ,   '''r'''					).readlines()]
        __UpperCamelCase     						=  [[reference] for reference in references]

    __UpperCamelCase     						=  __UpperCamelCase     						=  __UpperCamelCase     						=  0
    for prediction, ground_truths in zip(lowercase_    ,   lowercase_					):
        total += 1
        em += metric_max_over_ground_truths(lowercase_    ,   lowercase_    ,   lowercase_					)
        fa += metric_max_over_ground_truths(lowercase_    ,   lowercase_    ,   lowercase_					)

    __UpperCamelCase     						=  100.0 * em / total
    __UpperCamelCase     						=  100.0 * fa / total

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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  args.k
    __UpperCamelCase     						=  [line.strip() for line in open(lowercase_    ,   '''r'''					).readlines()]
    __UpperCamelCase     						=  [line.strip() for line in open(lowercase_    ,   '''r'''					).readlines()]

    __UpperCamelCase     						=  __UpperCamelCase     						=  0
    for hypo, reference in zip(lowercase_    ,   lowercase_					):
        __UpperCamelCase     						=  set(hypo.split('''\t'''					)[:k]					)
        __UpperCamelCase     						=  set(reference.split('''\t'''					)					)
        total += 1
        em += len(hypo_provenance & ref_provenance					) / k

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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    def strip_title(lowercase_					):
        if title.startswith('''"'''					):
            __UpperCamelCase     						=  title[1:]
        if title.endswith('''"'''					):
            __UpperCamelCase     						=  title[:-1]
        return title

    __UpperCamelCase     						=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowercase_    ,   return_tensors='''pt'''    ,   padding=lowercase_    ,   truncation=lowercase_    ,   )['''input_ids'''].to(args.device					)

    __UpperCamelCase     						=  rag_model.rag.question_encoder(lowercase_					)
    __UpperCamelCase     						=  question_enc_outputs[0]

    __UpperCamelCase     						=  rag_model.retriever(
        lowercase_    ,   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'''    ,   )
    __UpperCamelCase     						=  rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
    __UpperCamelCase     						=  []
    for docs in all_docs:
        __UpperCamelCase     						=  [strip_title(lowercase_					) for title in docs['''title''']]
        provenance_strings.append('''\t'''.join(lowercase_					)					)
    return provenance_strings




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    with torch.no_grad():
        __UpperCamelCase     						=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            lowercase_    ,   return_tensors='''pt'''    ,   padding=lowercase_    ,   truncation=lowercase_					)

        __UpperCamelCase     						=  inputs_dict.input_ids.to(args.device					)
        __UpperCamelCase     						=  inputs_dict.attention_mask.to(args.device					)
        __UpperCamelCase     						=  rag_model.generate(  # rag_model overwrites generate
            lowercase_    ,   attention_mask=lowercase_    ,   num_beams=args.num_beams    ,   min_length=args.min_length    ,   max_length=args.max_length    ,   early_stopping=lowercase_    ,   num_return_sequences=1    ,   bad_words_ids=[[0, 0]]    ,   )
        __UpperCamelCase     						=  rag_model.retriever.generator_tokenizer.batch_decode(lowercase_    ,   skip_special_tokens=lowercase_					)

        if args.print_predictions:
            for q, a in zip(lowercase_    ,   lowercase_					):
                logger.info('''Q: {} - A: {}'''.format(lowercase_    ,   lowercase_					)					)

        return answers




def     __SCREAMING_SNAKE_CASE      (     )				->		Any:

    """simple docstring"""


    __UpperCamelCase     						=  argparse.ArgumentParser()
    parser.add_argument(
        '''--model_type'''    ,   choices=['''rag_sequence''', '''rag_token''', '''bart''']    ,   type=lowercase_    ,   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=lowercase_    ,   choices=['''exact''', '''compressed''', '''legacy''']    ,   type=lowercase_    ,   help='''RAG model retriever type'''    ,   )
    parser.add_argument(
        '''--index_path'''    ,   default=lowercase_    ,   type=lowercase_    ,   help='''Path to the retrieval index'''    ,   )
    parser.add_argument('''--n_docs'''    ,   default=5    ,   type=lowercase_    ,   help='''Number of retrieved docs'''					)
    parser.add_argument(
        '''--model_name_or_path'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''    ,   )
    parser.add_argument(
        '''--eval_mode'''    ,   choices=['''e2e''', '''retrieval''']    ,   default='''e2e'''    ,   type=lowercase_    ,   help=(
            '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
            ''' precision@k.'''
        )    ,   )
    parser.add_argument('''--k'''    ,   default=1    ,   type=lowercase_    ,   help='''k for the precision@k calculation'''					)
    parser.add_argument(
        '''--evaluation_set'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''Path to a file containing evaluation samples'''    ,   )
    parser.add_argument(
        '''--gold_data_path'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''Path to a tab-separated file with gold samples'''    ,   )
    parser.add_argument(
        '''--gold_data_mode'''    ,   default='''qa'''    ,   type=lowercase_    ,   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=lowercase_    ,   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=lowercase_    ,   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=lowercase_    ,   help='''Number of beams to be used when generating answers'''    ,   )
    parser.add_argument('''--min_length'''    ,   default=1    ,   type=lowercase_    ,   help='''Min length of the generated answers'''					)
    parser.add_argument('''--max_length'''    ,   default=50    ,   type=lowercase_    ,   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.'''    ,   )
    __UpperCamelCase     						=  parser.parse_args()
    __UpperCamelCase     						=  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)
    return args




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Any:

    """simple docstring"""


    __UpperCamelCase     						=  {}
    if args.model_type is None:
        __UpperCamelCase     						=  infer_model_type(args.model_name_or_path					)
        assert args.model_type is not None
    if args.model_type.startswith('''rag'''					):
        __UpperCamelCase     						=  RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
        __UpperCamelCase     						=  args.n_docs
        if args.index_name is not None:
            __UpperCamelCase     						=  args.index_name
        if args.index_path is not None:
            __UpperCamelCase     						=  args.index_path
    else:
        __UpperCamelCase     						=  BartForConditionalGeneration

    __UpperCamelCase     						=  (
        [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'''    ,   lowercase_					)

    __UpperCamelCase     						=  get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
    __UpperCamelCase     						=  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(lowercase_    ,   args.predictions_path    ,   args.gold_data_path					)
            continue

        logger.info('''***** Running evaluation for {} *****'''.format(lowercase_					)					)
        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'''					):
            __UpperCamelCase     						=  RagRetriever.from_pretrained(lowercase_    ,   **lowercase_					)
            __UpperCamelCase     						=  model_class.from_pretrained(lowercase_    ,   retriever=lowercase_    ,   **lowercase_					)
            model.retriever.init_retrieval()
        else:
            __UpperCamelCase     						=  model_class.from_pretrained(lowercase_    ,   **lowercase_					)
        model.to(args.device					)

        with open(args.evaluation_set    ,   '''r'''					) as eval_file, open(args.predictions_path    ,   '''w'''					) as preds_file:
            __UpperCamelCase     						=  []
            for line in tqdm(lowercase_					):
                questions.append(line.strip()					)
                if len(lowercase_					) == args.eval_batch_size:
                    __UpperCamelCase     						=  evaluate_batch_fn(lowercase_    ,   lowercase_    ,   lowercase_					)
                    preds_file.write('''\n'''.join(lowercase_					) + '''\n'''					)
                    preds_file.flush()
                    __UpperCamelCase     						=  []
            if len(lowercase_					) > 0:
                __UpperCamelCase     						=  evaluate_batch_fn(lowercase_    ,   lowercase_    ,   lowercase_					)
                preds_file.write('''\n'''.join(lowercase_					)					)
                preds_file.flush()

            score_fn(lowercase_    ,   args.predictions_path    ,   args.gold_data_path					)


if __name__ == "__main__":
 a_      						=   get_args()
 main(args)












import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 a_      						=   None

a_      						=   logging.get_logger(__name__)

a_      						=   {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

a_      						=   {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       int			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       Any			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       Optional[Any]			=    PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__	:       int			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       Dict			=    DebertaVaTokenizer







      def __init__(   self    :       int       ,				snake_case    :       int=None       ,				snake_case    :       Optional[int]=None       ,				snake_case    :       Any=False       ,				snake_case    :       Optional[int]=False       ,				snake_case    :       Dict="[CLS]"       ,				snake_case    :       Any="[SEP]"       ,				snake_case    :       Union[str, Any]="[UNK]"       ,				snake_case    :       List[Any]="[SEP]"       ,				snake_case    :       Union[str, Any]="[PAD]"       ,				snake_case    :       Tuple="[CLS]"       ,				snake_case    :       Dict="[MASK]"       ,				**snake_case    :       int       ,				):
          super().__init__(
              snake_case       ,				tokenizer_file=snake_case       ,				do_lower_case=snake_case       ,				bos_token=snake_case       ,				eos_token=snake_case       ,				unk_token=snake_case       ,				sep_token=snake_case       ,				pad_token=snake_case       ,				cls_token=snake_case       ,				mask_token=snake_case       ,				split_by_punct=snake_case       ,				**snake_case       ,				)

          __UpperCamelCase     						=  do_lower_case
          __UpperCamelCase     						=  split_by_punct
          __UpperCamelCase     						=  vocab_file
          __UpperCamelCase     						=  False if not self.vocab_file else True







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple=None ):

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCamelCase     						=  [self.cls_token_id]
          __UpperCamelCase     						=  [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep







      def 					snake_case		(   self    :       Any       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]=None       ,				snake_case    :       Union[str, Any]=False ):

          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=snake_case       ,				token_ids_a=snake_case       ,				already_has_special_tokens=snake_case )

          if token_ids_a is not None:
              return [1] + ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
          return [1] + ([0] * len(snake_case )) + [1]







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[Any]=None ):
          __UpperCamelCase     						=  [self.sep_token_id]
          __UpperCamelCase     						=  [self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







      def 					snake_case		(   self    :       List[str]       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.''' )

          if not os.path.isdir(snake_case ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
              return
          __UpperCamelCase     						=  os.path.join(
              snake_case       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ):
              copyfile(self.vocab_file       ,				snake_case )

          return (out_vocab_file,)






