


'''simple docstring'''



import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


__A		='\\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'

__A		='\\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'

__A		='\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 						_snake_case   (       datasets.Metric		):




					def 			snake_case__  ( self):
								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 			snake_case__  ( self							,							_lowerCamelCase):
								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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=0.9							,							_lowerCamelCase=3							,							_lowerCamelCase=0.5):
								if NLTK_VERSION >= version.Version("""3.6.5"""):
											UpperCAmelCase__					:   Dict       				=       [
											    meteor_score.single_meteor_score(
											        word_tokenize(_lowerCamelCase)							,							word_tokenize(_lowerCamelCase)							,							alpha=_lowerCamelCase							,							beta=_lowerCamelCase							,							gamma=_lowerCamelCase)
											    for ref, pred in zip(_lowerCamelCase							,							_lowerCamelCase)
											]
								else:
											UpperCAmelCase__					:   List[str]       				=       [
											    meteor_score.single_meteor_score(_lowerCamelCase							,							_lowerCamelCase							,							alpha=_lowerCamelCase							,							beta=_lowerCamelCase							,							gamma=_lowerCamelCase)
											    for ref, pred in zip(_lowerCamelCase							,							_lowerCamelCase)
											]

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A		={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TableTransformerForObjectDetection',
		    'TableTransformerModel',
		    'TableTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TableTransformerConfig,
		    TableTransformerOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_table_transformer import (
				    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TableTransformerForObjectDetection,
				    TableTransformerModel,
				    TableTransformerPreTrainedModel,
				)

else:
		import sys

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