



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

import datasets


_lowerCAmelCase    :Dict     =					'\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\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'

_lowerCAmelCase    :Tuple     =					'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

_lowerCAmelCase    :str     =					'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION      )
class 							_UpperCAmelCase							( datasets.Metric      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Dict:
         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#chrf--chrf'''       ,	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#chrf--chrf''']       ,	reference_urls=[
                 '''https://github.com/m-popovic/chrF''',
             ]       ,	)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A = CHRF.CHAR_ORDER       ,	A = CHRF.WORD_ORDER       ,	A = CHRF.BETA       ,	A = False       ,	A = False       ,	A = False       ,	)							->					Union[str, Any]:
         _UpperCAmelCase							:  Dict											=    len(references[0]		)
         if any(len(A		) != references_per_prediction for refs in references		):
             raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
         _UpperCAmelCase							:  Any											=    [[refs[i] for refs in references] for i in range(A		)]

         _UpperCAmelCase							:  Union[str, Any]											=    CHRF(A       ,	A       ,	A       ,	A       ,	A       ,	A		)
         _UpperCAmelCase							:  Any											=    sb_chrf.corpus_score(A       ,	A		)

         return {
             "score": output.score,
             "char_order": output.char_order,
             "word_order": output.word_order,
             "beta": output.beta,
         }







"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    AddedToken('''<ent>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    AddedToken('''<ent2>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'							)
    tokenizer.save_pretrained(UpperCamelCase__							)
    with open(os.path.join(UpperCamelCase__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            _UpperCAmelCase							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. Expected only missing embeddings.position_ids'							)
    if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
        raise ValueError(
            '''Unexpected keys'''
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}'							)

    # Check outputs
    _UpperCAmelCase							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    _UpperCAmelCase							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    torch.tensor([[0.1457, 0.1044, 0.0174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
            F' {expected_shape}'							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(UpperCamelCase__							)							)
    model.save_pretrained(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
  parser.add_argument(
      '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
  )
  parser.add_argument(
      '--entity_vocab_path',
      default=None,
      type=str,
      help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
  )
  parser.add_argument(
      '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


