
import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__A							='''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

__A							='''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

__A							='''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					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"					),
					        }					)		,    codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]		,    reference_urls=[
					        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
					        "https://github.com/google-research/google-research/tree/master/rouge",
					    ]		,    )



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=None		,    lowercase=True		,    lowercase=False					)							->			List[Any]:
					if rouge_types is None:
									lowerCamelCase_         =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

					lowerCamelCase_         =     rouge_scorer.RougeScorer(rouge_types=lowercase		,    use_stemmer=lowercase					)
					if use_aggregator:
									lowerCamelCase_         =     scoring.BootstrapAggregator()
					else:
									lowerCamelCase_         =     []

					for ref, pred in zip(lowercase		,    lowercase					):
									lowerCamelCase_         =     scorer.score(lowercase		,    lowercase					)
									if use_aggregator:
													aggregator.add_scores(lowercase					)
									else:
													scores.append(lowercase					)

					if use_aggregator:
									lowerCamelCase_         =     aggregator.aggregate()
					else:
									lowerCamelCase_         =     {}
									for key in scores[0]:
													lowerCamelCase_         =     [score[key] for score in scores]

					return result




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__A							=logging.get_logger(__name__)

__A							={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A							=[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__A							={
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A							={F"""funnel-transformer/{name}""": 5_1_2 for name in _model_names}
__A							={F"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					VOCAB_FILES_NAMES
	lowerCAmelCase__   =					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__   =					PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__   =					FunnelTokenizer
	lowerCAmelCase__   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__   =					2



	def __init__(       self		,    lowercase=None		,    lowercase=None		,    lowercase=True		,    lowercase="<unk>"		,    lowercase="<sep>"		,    lowercase="<pad>"		,    lowercase="<cls>"		,    lowercase="<mask>"		,    lowercase="<s>"		,    lowercase="</s>"		,    lowercase=True		,    lowercase=True		,    lowercase=None		,    lowercase="##"		,    **lowercase		,    )							->			List[str]:
					super().__init__(
					    lowercase		,    tokenizer_file=lowercase		,    do_lower_case=lowercase		,    unk_token=lowercase		,    sep_token=lowercase		,    pad_token=lowercase		,    cls_token=lowercase		,    mask_token=lowercase		,    bos_token=lowercase		,    eos_token=lowercase		,    clean_text=lowercase		,    tokenize_chinese_chars=lowercase		,    strip_accents=lowercase		,    wordpieces_prefix=lowercase		,    **lowercase		,    )

					lowerCamelCase_         =     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
					if (
					    normalizer_state.get("lowercase"		,    lowercase					) != do_lower_case
					    or normalizer_state.get("strip_accents"		,    lowercase					) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"		,    lowercase					) != tokenize_chinese_chars
					):
									lowerCamelCase_         =     getattr(lowercase		,    normalizer_state.pop("type"					)					)
									lowerCamelCase_         =     do_lower_case
									lowerCamelCase_         =     strip_accents
									lowerCamelCase_         =     tokenize_chinese_chars
									lowerCamelCase_         =     normalizer_class(**lowercase					)

					lowerCamelCase_         =     do_lower_case



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=None					)							->			str:
					lowerCamelCase_         =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

					return output



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			List[int]:
					lowerCamelCase_         =     [self.sep_token_id]
					lowerCamelCase_         =     [self.cls_token_id]
					if token_ids_a is None:
									return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0]
					return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			Tuple[str]:
					lowerCamelCase_         =     self._tokenizer.model.save(lowercase		,    name=lowercase					)
					return tuple(lowercase					)


