



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


lowercase_	       =			'''\
@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",
}
'''

lowercase_	       =			'''\
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
'''

lowercase_	       =			'''
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 						__a (     datasets.Metric				):



	def 		UpperCamelCase							(						self							:    int)->							Union[str, Any]:
						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 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Dict				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]=None				,	snake_case_							:    Dict=True				,	snake_case_							:    Dict=False)->							Tuple:
						if rouge_types is None:
											__lowerCAmelCase	   =["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

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

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

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

						return result









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_	       =			{
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''Blip2Model''',
				    '''Blip2QFormerModel''',
				    '''Blip2PreTrainedModel''',
				    '''Blip2ForConditionalGeneration''',
				    '''Blip2VisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




