



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   ):




			'''simple docstring'''






			def  snake_case__     (   self							: 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  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''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  snake_case__     (   self							: List[Any]       ,       a_							: int       ,       a_							: List[Any]       ,       a_							: Optional[Any]=0.9       ,       a_							: Tuple=3       ,       a_							: Optional[Any]=0.5 ):
					'''simple docstring'''





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

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










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
				from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A   			=logging.get_logger(__name__)

__A   			={
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """deberta-v2"""




			def __init__(   self							: Optional[int]       ,       a_							: List[str]=12_81_00       ,       a_							: Optional[Any]=15_36       ,       a_							: Optional[Any]=24       ,       a_							: List[Any]=24       ,       a_							: Optional[int]=61_44       ,       a_							: List[Any]="gelu"       ,       a_							: Any=0.1       ,       a_							: Tuple=0.1       ,       a_							: Optional[Any]=5_12       ,       a_							: Tuple=0       ,       a_							: Dict=0.0_2       ,       a_							: Optional[Any]=1e-7       ,       a_							: List[str]=False       ,       a_							: List[Any]=-1       ,       a_							: List[str]=0       ,       a_							: Optional[Any]=True       ,       a_							: List[Any]=None       ,       a_							: Optional[int]=0       ,       a_							: Tuple="gelu"       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )

					__UpperCAmelCase    :     Any													=	hidden_size
					__UpperCAmelCase    :     Any													=	num_hidden_layers
					__UpperCAmelCase    :     List[Any]													=	num_attention_heads
					__UpperCAmelCase    :     Union[str, Any]													=	intermediate_size
					__UpperCAmelCase    :     List[str]													=	hidden_act
					__UpperCAmelCase    :     int													=	hidden_dropout_prob
					__UpperCAmelCase    :     List[str]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     str													=	max_position_embeddings
					__UpperCAmelCase    :     int													=	type_vocab_size
					__UpperCAmelCase    :     Tuple													=	initializer_range
					__UpperCAmelCase    :     Optional[int]													=	relative_attention
					__UpperCAmelCase    :     int													=	max_relative_positions
					__UpperCAmelCase    :     Any													=	pad_token_id
					__UpperCAmelCase    :     int													=	position_biased_input

					# Backwards compatibility
					if type(a_ ) == str:
							__UpperCAmelCase    :     Optional[Any]													=	[x.strip() for x in pos_att_type.lower().split('''|''' )]

					__UpperCAmelCase    :     Tuple													=	pos_att_type
					__UpperCAmelCase    :     int													=	vocab_size
					__UpperCAmelCase    :     Optional[Any]													=	layer_norm_eps

					__UpperCAmelCase    :     str													=	kwargs.get('''pooler_hidden_size'''       ,       a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	pooler_dropout
					__UpperCAmelCase    :     Tuple													=	pooler_hidden_act

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					if self.task == "multiple-choice":
							__UpperCAmelCase    :     List[Any]													=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
							__UpperCAmelCase    :     int													=	{0: '''batch''', 1: '''sequence'''}
					if self._config.type_vocab_size > 0:
							return OrderedDict(
							    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)] )
					else:
							return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)] )




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return 12




			def  snake_case__     (   self							: Any       ,       a_							: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: bool = False       ,       a_							: Optional["TensorType"] = None       ,       a_							: int = 3       ,       a_							: int = 40       ,       a_							: int = 40       ,       a_							: "PreTrainedTokenizerBase" = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	super().generate_dummy_inputs(preprocessor=a_       ,       framework=a_ )
					if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
					return dummy_inputs





