

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 				__UpperCamelCase				(					datasets.Metric			):







	def 	_SCREAMING_SNAKE_CASE							(   self: List[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 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Optional[int]      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Any=0.9  , __UpperCamelCase: str=3  , __UpperCamelCase: List[str]=0.5      ):

						'''simple docstring'''





						if NLTK_VERSION >= version.Version('3.6.5'      ):
											__magic_name__							=    [
											    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:
											__magic_name__							=    [
											    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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__						=							logging.get_logger(__name__)

A__						=							{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "camembert"







	def __init__(   self: Optional[Any]  , __UpperCamelCase: Any=3_05_22  , __UpperCamelCase: Tuple=7_68  , __UpperCamelCase: str=12  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=30_72  , __UpperCamelCase: Any="gelu"  , __UpperCamelCase: Optional[int]=0.1  , __UpperCamelCase: Any=0.1  , __UpperCamelCase: str=5_12  , __UpperCamelCase: Dict=2  , __UpperCamelCase: str=0.02  , __UpperCamelCase: List[Any]=1E-12  , __UpperCamelCase: List[Any]=1  , __UpperCamelCase: Any=0  , __UpperCamelCase: Union[str, Any]=2  , __UpperCamelCase: Dict="absolute"  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=None  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase      )

						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    hidden_act
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    type_vocab_size
						__magic_name__							=    initializer_range
						__magic_name__							=    layer_norm_eps
						__magic_name__							=    position_embedding_type
						__magic_name__							=    use_cache
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__							=    {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__							=    {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]      )




