




"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       a							      =     None


a							      =     logging.get_logger(__name__)


a							      =     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a							      =     {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

a							      =     {
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
a							      =     ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      VOCAB_FILES_NAMES
 UpperCAmelCase       :	Any     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase       :	Union[str, Any]     =      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase       :	List[str]     =      ['''input_ids''', '''attention_mask''']
 UpperCAmelCase       :	Tuple     =      MBartTokenizer

 UpperCAmelCase       :	List[int]     =      []
 UpperCAmelCase       :	List[int]     =      []

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[str]="<s>"       ,				_UpperCAmelCase			:			Union[str, Any]="</s>"       ,				_UpperCAmelCase			:			Any="</s>"       ,				_UpperCAmelCase			:			Any="<s>"       ,				_UpperCAmelCase			:			List[str]="<unk>"       ,				_UpperCAmelCase			:			Tuple="<pad>"       ,				_UpperCAmelCase			:			str="<mask>"       ,				_UpperCAmelCase			:			str=None       ,				_UpperCAmelCase			:			str=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Dict       ,				):
       # Mask token behave like a normal word, i.e. include the space before it
       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else mask_token

       super().__init__(
           vocab_file=_UpperCAmelCase       ,				tokenizer_file=_UpperCAmelCase       ,				bos_token=_UpperCAmelCase       ,				eos_token=_UpperCAmelCase       ,				sep_token=_UpperCAmelCase       ,				cls_token=_UpperCAmelCase       ,				unk_token=_UpperCAmelCase       ,				pad_token=_UpperCAmelCase       ,				mask_token=_UpperCAmelCase       ,				src_lang=_UpperCAmelCase       ,				tgt_lang=_UpperCAmelCase       ,				additional_special_tokens=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

       _A     =  vocab_file
       _A     =  False if not self.vocab_file else True

       _A     =  FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

       self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}						)
       _A     =  {
           lang_code: self.convert_tokens_to_ids(_UpperCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _A     =  src_lang if src_lang is not None else 'en_XX'
       _A     =  self.convert_tokens_to_ids(self._src_lang						)
       _A     =  tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return self._src_lang

 @src_lang.setter
 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			str						):
       _A     =  new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.sep_token_id]
       _A     =  [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 + sep + token_ids_a + sep						) * [0]

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str]       ,				_UpperCAmelCase			:			Optional[str]       ,				**_UpperCAmelCase			:			Dict						):
       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
       _A     =  src_lang
       _A     =  self(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  tgt_lang_id
       return inputs

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str = "en_XX"       ,				_UpperCAmelCase			:			Optional[List[str]] = None       ,				_UpperCAmelCase			:			str = "ro_RO"       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  src_lang
       _A     =  tgt_lang
       return super().prepare_seqaseq_batch(_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_src_lang_special_tokens(self.src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_tgt_lang_special_tokens(self.tgt_lang						)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

       _A     =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _A     =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _A     =  processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens						)						)       ,				)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

       _A     =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _A     =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _A     =  processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens						)						)       ,				)



 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str] = None						):
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'						)

       if not os.path.isdir(_UpperCAmelCase						):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
             return
       _A     =  os.path.join(
           _UpperCAmelCase       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
             copyfile(self.vocab_file       ,				_UpperCAmelCase						)

       return (out_vocab_file,)









from sklearn.metrics import fa_score

import datasets


lowerCamelCase      :       Union[str, Any]    =						'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


lowerCamelCase      :       Optional[Any]    =						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


lowerCamelCase      :       Optional[Any]    =						'''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



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


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return datasets.MetricInfo(
               description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
                   {
                       "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                       "references": datasets.Sequence(datasets.Value("int32"						)						),
                   }
                   if self.config_name == "multilabel"
                   else {
                       "predictions": datasets.Value("int32"						),
                       "references": datasets.Value("int32"						),
                   }						)       ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1       ,    __UpperCamelCase="binary"       ,    __UpperCamelCase=None						)->						Optional[int]:
           __lowerCAmelCase             =       fa_score(
               __UpperCamelCase       ,    __UpperCamelCase       ,    labels=__UpperCamelCase       ,    pos_label=__UpperCamelCase       ,    average=__UpperCamelCase       ,    sample_weight=__UpperCamelCase						)
           return {"f1": float(__UpperCamelCase						) if score.size == 1 else score}






