

"""simple docstring"""






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_lowerCAmelCase						=		getLogger(__name__)


_lowerCAmelCase						=		"""cuda""" if torch.cuda.is_available() else """cpu"""

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase = 8 ,   _lowerCamelCase = DEFAULT_DEVICE ,   _lowerCamelCase=False ,   _lowerCamelCase="summarization" ,   _lowerCamelCase=None ,   **_lowerCamelCase ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    Path(_lowerCamelCase   ).open('w' ,   encoding='utf-8'   )
    _lowerCAmelCase       :   int  =    str(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase   ).to(_lowerCamelCase   )
    if fpaa:
        _lowerCAmelCase       :   List[Any]  =    model.half()

    _lowerCAmelCase       :   Tuple  =    AutoTokenizer.from_pretrained(_lowerCamelCase   )
    logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""   )  # if this is wrong, check config.model_type.

    _lowerCAmelCase       :   Optional[Any]  =    time.time()
    # update config with task specific params
    use_task_specific_params(_lowerCamelCase ,   _lowerCamelCase   )
    if prefix is None:
        _lowerCAmelCase       :   Optional[Any]  =    prefix or getattr(model.config ,   'prefix' ,   ''   ) or ''
    for examples_chunk in tqdm(list(chunks(_lowerCamelCase ,   _lowerCamelCase   )   )   ):
        _lowerCAmelCase       :   Union[str, Any]  =    [prefix + text for text in examples_chunk]
        _lowerCAmelCase       :   Union[str, Any]  =    tokenizer(_lowerCamelCase ,   return_tensors='pt' ,   truncation=_lowerCamelCase ,   padding='longest'   ).to(_lowerCamelCase   )
        _lowerCAmelCase       :   Tuple  =    model.generate(
            input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_lowerCamelCase ,   )
        _lowerCAmelCase       :   int  =    tokenizer.batch_decode(_lowerCamelCase ,   skip_special_tokens=_lowerCamelCase ,   clean_up_tokenization_spaces=_lowerCamelCase   )
        for hypothesis in dec:
            fout.write(hypothesis + '\n'   )
            fout.flush()
    fout.close()
    _lowerCAmelCase       :   str  =    int(time.time() - start_time   )  # seconds
    _lowerCAmelCase       :   Tuple  =    len(_lowerCamelCase   )
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4   )}

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'   )

def 		lowerCamelCase__		(					_lowerCamelCase=True   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    argparse.ArgumentParser()
    parser.add_argument('model_name' ,   type=_lowerCamelCase ,   help='like facebook/bart-large-cnn,t5-base, etc.'   )
    parser.add_argument('input_path' ,   type=_lowerCamelCase ,   help='like cnn_dm/test.source'   )
    parser.add_argument('save_path' ,   type=_lowerCamelCase ,   help='where to save summaries'   )
    parser.add_argument('--reference_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='like cnn_dm/test.target'   )
    parser.add_argument('--score_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default='metrics.json' ,   help='where to save metrics'   )
    parser.add_argument('--device' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='cuda, cuda:1, cpu etc.'   )
    parser.add_argument(
        '--prefix' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='will be added to the begininng of src examples'   )
    parser.add_argument('--task' ,   type=_lowerCamelCase ,   default='summarization' ,   help='used for task_specific_params + metrics'   )
    parser.add_argument('--bs' ,   type=_lowerCamelCase ,   default=8 ,   required=_lowerCamelCase ,   help='batch size'   )
    parser.add_argument(
        '--n_obs' ,   type=_lowerCamelCase ,   default=-1 ,   required=_lowerCamelCase ,   help='How many observations. Defaults to all.'   )
    parser.add_argument('--fp16' ,   action='store_true'   )
    parser.add_argument('--dump-args' ,   action='store_true' ,   help='print the custom hparams with the results'   )
    parser.add_argument(
        '--info' ,   nargs='?' ,   type=_lowerCamelCase ,   const=datetime_now() ,   help=(
            'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
            ' lang=en-ru. If no value is passed, the current datetime string will be used.'
        ) ,   )
    # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
    _lowerCAmelCase,					_lowerCAmelCase       :   Any  =    parser.parse_known_args()
    _lowerCAmelCase       :   Optional[Any]  =    parse_numeric_n_bool_cl_kwargs(_lowerCamelCase   )
    if parsed_args and verbose:
        print(f"""parsed the following generate kwargs: {parsed_args}"""   )
    _lowerCAmelCase       :   List[Any]  =    [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
    if args.n_obs > 0:
        _lowerCAmelCase       :   Dict  =    examples[: args.n_obs]
    Path(args.save_path   ).parent.mkdir(exist_ok=_lowerCamelCase   )

    if args.reference_path is None and Path(args.score_path   ).exists():
        warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""   )

    if args.device == "cpu" and args.fpaa:
        # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
        raise ValueError('Can\'t mix --fp16 and --device cpu'   )

    _lowerCAmelCase       :   str  =    generate_summaries_or_translations(
        _lowerCamelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_lowerCamelCase ,   )

    if args.reference_path is None:
        return {}

    # Compute scores
    _lowerCAmelCase       :   Dict  =    calculate_bleu if 'translation' in args.task else calculate_rouge
    _lowerCAmelCase       :   Dict  =    [x.rstrip() for x in open(args.save_path   ).readlines()]
    _lowerCAmelCase       :   List[str]  =    [x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_lowerCamelCase   )]
    _lowerCAmelCase       :   dict  =    score_fn(_lowerCamelCase ,   _lowerCamelCase   )
    scores.update(_lowerCamelCase   )

    if args.dump_args:
        scores.update(_lowerCamelCase   )
    if args.info:
        _lowerCAmelCase       :   Any  =    args.info

    if verbose:
        print(_lowerCamelCase   )

    if args.score_path is not None:
        json.dump(_lowerCamelCase ,   open(args.score_path ,   'w'   )   )

    return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)








"""simple docstring"""






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCAmelCase						=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


_lowerCAmelCase						=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase						=		{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase						=		{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





_lowerCAmelCase						=		collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_lowerCAmelCase						=		collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCAmelCase						=		r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """







@add_start_docstrings(a__						)
class 	__UpperCamelCase    :

  def __call__(		self    ,_A    ,_A = None    ,_A = None    ,_A = False    ,_A = False    ,_A = None    ,_A = None    ,_A = None    ,**_A    ,):
      '''simple docstring'''

      if titles is None and texts is None:
          return super().__call__(
              _A    ,padding=_A    ,truncation=_A    ,max_length=_A    ,return_tensors=_A    ,return_attention_mask=_A    ,**_A    ,)
      elif titles is None or texts is None:
          _lowerCAmelCase       :   Optional[int]  =    titles if texts is None else texts
          return super().__call__(
              _A    ,_A    ,padding=_A    ,truncation=_A    ,max_length=_A    ,return_tensors=_A    ,return_attention_mask=_A    ,**_A    ,)
      _lowerCAmelCase       :   str  =    titles if not isinstance(_A    ,_A      ) else [titles]
      _lowerCAmelCase       :   List[str]  =    texts if not isinstance(_A    ,_A      ) else [texts]
      _lowerCAmelCase       :   Union[str, Any]  =    len(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    questions if not isinstance(_A    ,_A      ) else [questions] * n_passages
      if len(_A      ) != len(_A      ):
          raise ValueError(
              F"""There should be as many titles than texts but got {len(_A      )} titles and {len(_A      )} texts."""      )
      _lowerCAmelCase       :   Union[str, Any]  =    super().__call__(_A    ,_A    ,padding=_A    ,truncation=_A      )['input_ids']
      _lowerCAmelCase       :   Tuple  =    super().__call__(_A    ,add_special_tokens=_A    ,padding=_A    ,truncation=_A      )['input_ids']
      _lowerCAmelCase       :   Optional[int]  =    {
          'input_ids': [
              (encoded_question_and_title + encoded_text)[:max_length]
              if max_length is not None and truncation
              else encoded_question_and_title + encoded_text
              for encoded_question_and_title, encoded_text in zip(_A    ,_A      )
          ]
      }
      if return_attention_mask is not False:
          _lowerCAmelCase       :   Tuple  =    []
          for input_ids in encoded_inputs["input_ids"]:
              attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
          _lowerCAmelCase       :   List[Any]  =    attention_mask
      return self.pad(_A    ,padding=_A    ,max_length=_A    ,return_tensors=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A = 16    ,_A = 64    ,_A = 4    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    reader_input['input_ids']
      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   int  =    reader_output[:3]
      _lowerCAmelCase       :   Optional[Any]  =    len(_A      )
      _lowerCAmelCase       :   Any  =    sorted(range(_A      )    ,reverse=_A    ,key=relevance_logits.__getitem__      )
      _lowerCAmelCase       :   List[DPRReaderOutput]  =    []
      for doc_id in sorted_docs:
          _lowerCAmelCase       :   int  =    list(input_ids[doc_id]      )
          # assuming question & title information is at the beginning of the sequence
          _lowerCAmelCase       :   Any  =    sequence_ids.index(self.sep_token_id    ,2      ) + 1  # second sep id
          if sequence_ids[-1] == self.pad_token_id:
              _lowerCAmelCase       :   List[str]  =    sequence_ids.index(self.pad_token_id      )
          else:
              _lowerCAmelCase       :   Optional[int]  =    len(_A      )

          _lowerCAmelCase       :   Optional[Any]  =    self._get_best_spans(
              start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,max_answer_length=_A    ,top_spans=_A    ,)
          for start_index, end_index in best_spans:
              start_index += passage_offset
              end_index += passage_offset
              nbest_spans_predictions.append(
                  DPRSpanPrediction(
                      span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,relevance_score=relevance_logits[doc_id]    ,doc_id=_A    ,start_index=_A    ,end_index=_A    ,text=self.decode(sequence_ids[start_index : end_index + 1]      )    ,)      )
          if len(_A      ) >= num_spans:
              break
      return nbest_spans_predictions[:num_spans]







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    []
      for start_index, start_score in enumerate(_A      ):
          for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]      ):
              scores.append(((start_index, start_index + answer_length), start_score + end_score)      )
      _lowerCAmelCase       :   Tuple  =    sorted(_A    ,key=lambda _A      : x[1]    ,reverse=_A      )
      _lowerCAmelCase       :   int  =    []
      for (start_index, end_index), score in scores:
          if start_index > end_index:
              raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""      )
          _lowerCAmelCase       :   List[str]  =    end_index - start_index + 1
          if length > max_answer_length:
              raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""      )
          if any(
              start_index <= prev_start_index <= prev_end_index <= end_index
              or prev_start_index <= start_index <= end_index <= prev_end_index
              for (prev_start_index, prev_end_index) in chosen_span_intervals      ):
              continue
          chosen_span_intervals.append((start_index, end_index)      )

          if len(_A      ) == top_spans:
              break
      return chosen_span_intervals







@add_end_docstrings(a__						)
class 	__UpperCamelCase    (    a__							,							a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		READER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		READER_PRETRAINED_INIT_CONFIGURATION
  _UpperCAmelCase          =		["input_ids", "attention_mask"]





