



'''simple docstring'''





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase_	       =			version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
				from nltk import word_tokenize


lowercase_	       =			'''\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'''

lowercase_	       =			'''\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'''

lowercase_	       =			'''\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'''






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



	def 		UpperCamelCase							(						self							:    Optional[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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""]				,	reference_urls=[
						        """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
						        """https://en.wikipedia.org/wiki/METEOR""",
						    ]				,	)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    str)->							List[str]:
						import nltk

						nltk.download("""wordnet""")
						if NLTK_VERSION >= version.Version("""3.6.5"""):
											nltk.download("""punkt""")
						if NLTK_VERSION >= version.Version("""3.6.6"""):
											nltk.download("""omw-1.4""")



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    Any=0.9				,	snake_case_							:    Optional[Any]=3				,	snake_case_							:    Union[str, Any]=0.5)->							Dict:
						if NLTK_VERSION >= version.Version("""3.6.5"""):
											__lowerCAmelCase	   =[
											    meteor_score.single_meteor_score(
											        word_tokenize(UpperCamelCase_)				,	word_tokenize(UpperCamelCase_)				,	alpha=UpperCamelCase_				,	beta=UpperCamelCase_				,	gamma=UpperCamelCase_)
											    for ref, pred in zip(UpperCamelCase_				,	UpperCamelCase_)
											]
						else:
											__lowerCAmelCase	   =[
											    meteor_score.single_meteor_score(UpperCamelCase_				,	UpperCamelCase_				,	alpha=UpperCamelCase_				,	beta=UpperCamelCase_				,	gamma=UpperCamelCase_)
											    for ref, pred in zip(UpperCamelCase_				,	UpperCamelCase_)
											]

						return {"meteor": np.mean(UpperCamelCase_)}









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__)




