


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				def filter_log(UpperCAmelCase__							):
								return False if "This IS expected if you are initializing" in record.msg else True

				_SCREAMING_SNAKE_CASE      = datasets.utils.logging.get_logger('transformers.modeling_utils'							)
				logger.addFilter(_A							)
				try:
								yield
				finally:
								logger.removeFilter(_A							)


snake_case			:      List[str] 							=   '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

snake_case			:      List[str] 							=   '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case			:      int 							=   '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	List[str]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION				, citation=_CITATION				, homepage='https://github.com/Tiiiger/bert_score'				, 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/Tiiiger/bert_score']				, reference_urls=[
											        'https://github.com/Tiiiger/bert_score',
											        'https://arxiv.org/abs/1904.09675',
											    ]				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_=None				, A_=None				, A_=None				, A_=False				, A_=False				, A_=None				, A_=64				, A_=4				, A_=False				, A_=False				, A_=None				, A_=False				, )->	Dict:
											_SCREAMING_SNAKE_CASE      = bert_score.utils.get_hash
											_SCREAMING_SNAKE_CASE      = bert_score.BERTScorer

											if version.parse(bert_score.__version__	) >= version.parse('0.3.10'	):
															_SCREAMING_SNAKE_CASE      = functools.partial(A_				, use_fast_tokenizer=A_	)
															_SCREAMING_SNAKE_CASE      = functools.partial(A_				, use_fast_tokenizer=A_	)
											elif use_fast_tokenizer:
															raise ImportWarning(
															    'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
															    'You can install it with `pip install \"bert-score>=0.3.10\"`.'	)

											if model_type is None:
															assert lang is not None, "either lang or model_type should be specified"
															_SCREAMING_SNAKE_CASE      = bert_score.utils.langamodel[lang.lower()]

											if num_layers is None:
															_SCREAMING_SNAKE_CASE      = bert_score.utils.modelalayers[model_type]

											_SCREAMING_SNAKE_CASE      = get_hash(
											    model=A_				, num_layers=A_				, idf=A_				, rescale_with_baseline=A_				, use_custom_baseline=baseline_path is not None				, )

											with filter_logging_context():
															if not hasattr(self				, 'cached_bertscorer'	) or self.cached_bertscorer.hash != hashcode:
																			_SCREAMING_SNAKE_CASE      = scorer(
																			    model_type=A_				, num_layers=A_				, batch_size=A_				, nthreads=A_				, all_layers=A_				, idf=A_				, device=A_				, lang=A_				, rescale_with_baseline=A_				, baseline_path=A_				, )

											((_SCREAMING_SNAKE_CASE)      ,      (_SCREAMING_SNAKE_CASE)      ,      (_SCREAMING_SNAKE_CASE))      = self.cached_bertscorer.score(
											    cands=A_				, refs=A_				, verbose=A_				, batch_size=A_				, )
											_SCREAMING_SNAKE_CASE      = {
											    'precision': P.tolist(),
											    'recall': R.tolist(),
											    'f1': F.tolist(),
											    'hashcode': hashcode,
											}
											return output_dict





							def 		__magic_name__			(    self				, A_=None				, A_=None				, **A_	)->	List[str]:






											if references is not None:
															_SCREAMING_SNAKE_CASE      = [[ref] if isinstance(A_				, A_	) else ref for ref in references]
											super().add_batch(predictions=A_				, references=A_				, **A_	)





							def 		__magic_name__			(    self				, A_=None				, A_=None				, **A_	)->	Optional[Any]:






											if isinstance(A_				, A_	):
															_SCREAMING_SNAKE_CASE      = [reference]
											super().add(prediction=A_				, reference=A_				, **A_	)





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
							SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





							def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
											super().__init__(
											    A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

											_SCREAMING_SNAKE_CASE      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
											if pre_tok_state.get('add_prefix_space'				, A_	) != add_prefix_space:
															_SCREAMING_SNAKE_CASE      = getattr(A_				, pre_tok_state.pop('type'	)	)
															_SCREAMING_SNAKE_CASE      = add_prefix_space
															_SCREAMING_SNAKE_CASE      = pre_tok_class(**A_	)

											_SCREAMING_SNAKE_CASE      = add_prefix_space





							@property
							def 		__magic_name__			(    self	)->	str:
											if self._mask_token is None:
															if self.verbose:
																			logger.error('Using mask_token, but it is not set yet.'	)
															return None
											return str(self._mask_token	)





							@mask_token.setter
							def 		__magic_name__			(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
											_SCREAMING_SNAKE_CASE      = value





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)
											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._batch_encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)

											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											_SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
											return tuple(A_	)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)

											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
