




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

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


if is_sentencepiece_available():
			from .tokenization_barthez import BarthezTokenizer
else:
			__SCREAMING_SNAKE_CASE 						=None

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE 						={
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

__SCREAMING_SNAKE_CASE 						="""▁"""






class  __magic_name__						(  _lowerCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					['input_ids', 'attention_mask']
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					BarthezTokenizer


				def __init__(							self:   int		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Optional[int]="<s>"		,					_lowerCamelCase:   Any="</s>"		,					_lowerCamelCase:   List[str]="</s>"		,					_lowerCamelCase:   int="<s>"		,					_lowerCamelCase:   Union[str, Any]="<unk>"		,					_lowerCamelCase:   Optional[int]="<pad>"		,					_lowerCamelCase:   Any="<mask>"		,					**_lowerCamelCase:   Optional[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    AddedToken(_lowerCAmelCase		,					lstrip=_lowerCAmelCase		,					rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase		,					_lowerCAmelCase		) else mask_token

											super().__init__(
											    _lowerCAmelCase		,					tokenizer_file=_lowerCAmelCase		,					bos_token=_lowerCAmelCase		,					eos_token=_lowerCAmelCase		,					unk_token=_lowerCAmelCase		,					sep_token=_lowerCAmelCase		,					cls_token=_lowerCAmelCase		,					pad_token=_lowerCAmelCase		,					mask_token=_lowerCAmelCase		,					**_lowerCAmelCase		,					)

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


				def     _A							(							self:   str		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):







											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 + sep + token_ids_a + sep


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





				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   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(_lowerCAmelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCAmelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

											return (out_vocab_file,)








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							self:   Optional[int]		):
											return datasets.MetricInfo(
											    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''		,					id='''sequence'''		),
											            '''references''': datasets.Value('''string'''		,					id='''sequence'''		),
											        }		)		,					reference_urls=[]		,					)





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

											if ignore_punctuation:
																		SCREAMING_SNAKE_CASE_   	=    string.punctuation.maketrans(''''''		,					''''''		,					string.punctuation		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											if ignore_numbers:
																		SCREAMING_SNAKE_CASE_   	=    string.digits.maketrans(''''''		,					''''''		,					string.digits		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


