
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['WhisperTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'WhisperForConditionalGeneration',
						    'WhisperModel',
						    'WhisperPreTrainedModel',
						    'WhisperForAudioClassification',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFWhisperForConditionalGeneration',
						    'TFWhisperModel',
						    'TFWhisperPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'FlaxWhisperForConditionalGeneration',
						    'FlaxWhisperModel',
						    'FlaxWhisperPreTrainedModel',
						    'FlaxWhisperForAudioClassification',
						]


if TYPE_CHECKING:
						from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
						from .feature_extraction_whisper import WhisperFeatureExtractor
						from .processing_whisper import WhisperProcessor
						from .tokenization_whisper import WhisperTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_whisper_fast import WhisperTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_whisper import (
												    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WhisperForAudioClassification,
												    WhisperForConditionalGeneration,
												    WhisperModel,
												    WhisperPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_whisper import (
												    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWhisperForConditionalGeneration,
												    TFWhisperModel,
												    TFWhisperPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_whisper import (
												    FlaxWhisperForAudioClassification,
												    FlaxWhisperForConditionalGeneration,
												    FlaxWhisperModel,
												    FlaxWhisperPreTrainedModel,
												)

else:
						import sys

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









"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCAmelCase_   						=   '\\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'

lowerCAmelCase_   						=   '\\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'

lowerCAmelCase_   						=   '\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    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				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						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				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						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

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