






"""simple docstring"""



import sacrebleu as scb
from packaging import version

import datasets


_UpperCamelCase						:    List[str]		=						'\\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_UpperCamelCase						:    List[Any]		=						'\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

_UpperCamelCase						:    Optional[Any]		=						'\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION      )
class 			a	(  datasets.Metric      ):







    def      UpperCamelCase_ (     self			):
      if version.parse(scb.__version__			) < version.parse('1.4.12'			):
        raise ImportWarning(
            'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
            'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
      return datasets.MetricInfo(
          description=_DESCRIPTION				,						citation=_CITATION				,						homepage='https://github.com/mjpost/sacreBLEU'				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'				,						id='sequence'			),
                  'references': datasets.Sequence(datasets.Value('string'				,						id='sequence'			)				,						id='references'			),
              }			)				,						codebase_urls=['https://github.com/mjpost/sacreBLEU']				,						reference_urls=[
              'https://github.com/mjpost/sacreBLEU',
              'https://en.wikipedia.org/wiki/BLEU',
              'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
          ]				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase="exp"				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			len(references[0]			)
      if any(len(_lowerCamelCase			) != references_per_prediction for refs in references			):
        raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
      lowercase				       =			[[refs[i] for refs in references] for i in range(_lowerCamelCase			)]
      lowercase				       =			scb.corpus_bleu(
          _lowerCamelCase				,						_lowerCamelCase				,						smooth_method=_lowerCamelCase				,						smooth_value=_lowerCamelCase				,						force=_lowerCamelCase				,						lowercase=_lowerCamelCase				,						use_effective_order=_lowerCamelCase				,						**({'tokenize': tokenize} if tokenize else {})				,						)
      lowercase				       =			{
          'score': output.score,
          'counts': output.counts,
          'totals': output.totals,
          'precisions': output.precisions,
          'bp': output.bp,
          'sys_len': output.sys_len,
          'ref_len': output.ref_len,
      }
      return output_dict










"""simple docstring"""



import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


_UpperCamelCase						:    Optional[Any]		=						collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





  print(f'Reading saved model from {model_file}'       )
  lowercase				       =			torch.load(__snake_case						,   map_location=lambda __snake_case						,   __snake_case       : default_restore_location(__snake_case						,   'cpu'       )       )
  return CheckpointState(**__snake_case       )

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			src_file







    def      UpperCamelCase_ (     self			):
      raise NotImplementedError







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      if comp_type.startswith('c'			):
        return DPRContextEncoderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      if comp_type.startswith('q'			):
        return DPRQuestionEncoderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      if comp_type.startswith('r'			):
        return DPRReaderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      else:
        raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'			)


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR biencoder from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      lowercase       ,	lowercase				       =			model.ctx_encoder, 'ctx_model.'
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
      for key, value in saved_state.model_dict.items():
        if key.startswith(_lowerCamelCase			):
          lowercase				       =			key[len(_lowerCamelCase			) :]
          if not key.startswith('encode_proj.'			):
            lowercase				       =			'bert_model.' + key
          lowercase				       =			value
      encoder.load_state_dict(_lowerCamelCase			)
      return model


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR biencoder from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      lowercase       ,	lowercase				       =			model.question_encoder, 'question_model.'
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
      for key, value in saved_state.model_dict.items():
        if key.startswith(_lowerCamelCase			):
          lowercase				       =			key[len(_lowerCamelCase			) :]
          if not key.startswith('encode_proj.'			):
            lowercase				       =			'bert_model.' + key
          lowercase				       =			value
      encoder.load_state_dict(_lowerCamelCase			)
      return model


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR reader from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{
          'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
      }
      for key, value in saved_state.model_dict.items():
        if key.startswith('encoder.'			) and not key.startswith('encoder.encode_proj'			):
          lowercase				       =			'encoder.bert_model.' + key[len('encoder.'			) :]
        lowercase				       =			value
      model.span_predictor.load_state_dict(_lowerCamelCase			)
      return model


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Path						,   __snake_case     :    Path       ):





  '''simple docstring'''





  lowercase				       =			Path(__snake_case       )
  dest_dir.mkdir(exist_ok=__snake_case       )

  lowercase				       =			DPRState.from_type(__snake_case						,   src_file=__snake_case       )
  lowercase				       =			dpr_state.load_dpr_model()
  model.save_pretrained(__snake_case       )
  model.from_pretrained(__snake_case       )  # sanity check


if __name__ == "__main__":
    _UpperCamelCase						:    Optional[int]		=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
    )
    parser.add_argument(
        '--src',
        type=str,
        help=(
            'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
            ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
            ' \'retriever\' checkpoints.'
        ),
    )
    parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
    _UpperCamelCase						:    List[Any]		=						parser.parse_args()

    _UpperCamelCase						:    int		=						Path(args.src)
    _UpperCamelCase						:    List[Any]		=						F'''converted-{src_file.name}''' if args.dest is None else args.dest
    _UpperCamelCase						:    Optional[Any]		=						Path(dest_dir)
    assert src_file.exists()
    assert (
        args.type is not None
    ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
    convert(args.type, src_file, dest_dir)


