

"""simple docstring"""
import sacrebleu as scb
from packaging import version

import datasets


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

_A						=     """\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

_A						=     """
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    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).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    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`).
    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:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""

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


  '''simple docstring'''







  def    _a			(self  ):


        """simple docstring"""

        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    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase="exp"       ,					_lowerCamelCase=None       ,					_lowerCamelCase=False       ,					_lowerCamelCase=False       ,					_lowerCamelCase=None       ,					_lowerCamelCase=False       ,					):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						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"""  )
        UpperCAmelCase__ :       Optional[int]						=						[[refs[i] for refs in references] for i in range(_lowerCamelCase  )]
        UpperCAmelCase__ :       Optional[Any]						=						scb.corpus_bleu(
            _lowerCamelCase       ,					_lowerCamelCase       ,					smooth_method=_lowerCamelCase       ,					smooth_value=_lowerCamelCase       ,					force=_lowerCamelCase       ,					lowercase=_lowerCamelCase       ,					use_effective_order=_lowerCamelCase       ,					**({"""tokenize""": tokenize} if tokenize else {})       ,					)
        UpperCAmelCase__ :       str						=						{
            """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 json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
      # Initialise PyTorch model
      UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
      UpperCAmelCase__ :       Any						=						GPTNeoConfig(
          hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
      print(F"""Building PyTorch model from configuration: {config}"""						)
      UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
      model.save_pretrained(lowerCAmelCase						)


if __name__ == "__main__":
    _A						=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained mesh-tf model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    _A						=     parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

