




'''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     _a						(			datasets.Metric				):
							def 					A   (			self		:			Optional[Any]       ):



											'''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 					A   (			self		:			Tuple     ,       lowercase		:			str       ):



											'''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 					A   (			self		:			Any     ,       lowercase		:			str     ,       lowercase		:			Any     ,       lowercase		:			Any=0.9     ,       lowercase		:			Tuple=3     ,       lowercase		:			List[str]=0.5       ):



											'''simple docstring'''

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

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










'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=logging.get_logger(__name__)

A									={
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  List[Any]            =      """mgp-str"""
       def __init__(			self		:			str     ,       lowercase		:			str=[32, 128]     ,       lowercase		:			Optional[Any]=4     ,       lowercase		:			Optional[Any]=3     ,       lowercase		:			Dict=27     ,       lowercase		:			Any=38     ,       lowercase		:			int=50_257     ,       lowercase		:			List[str]=30_522     ,       lowercase		:			Optional[int]=768     ,       lowercase		:			List[Any]=12     ,       lowercase		:			Tuple=12     ,       lowercase		:			Optional[int]=4.0     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			str=False     ,       lowercase		:			str=1E-5     ,       lowercase		:			Dict=0.0     ,       lowercase		:			Dict=0.0     ,       lowercase		:			Tuple=0.0     ,       lowercase		:			str=False     ,       lowercase		:			Optional[Any]=0.02     ,       **lowercase		:			List[str]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )

           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	patch_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	max_token_length
           UpperCAmelCase     =	num_character_labels
           UpperCAmelCase     =	num_bpe_labels
           UpperCAmelCase     =	num_wordpiece_labels
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	mlp_ratio
           UpperCAmelCase     =	distilled
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	drop_rate
           UpperCAmelCase     =	qkv_bias
           UpperCAmelCase     =	attn_drop_rate
           UpperCAmelCase     =	drop_path_rate
           UpperCAmelCase     =	output_aa_attentions
           UpperCAmelCase     =	initializer_range




