

'''simple docstring'''
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
     from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]				)      ->			Dict:
     """simple docstring"""




     if is_torch_version('<'							, '2.0.0'				) or not hasattr(_UpperCamelCase							, '_dynamo'				):
          return False
     return isinstance(_UpperCamelCase							, torch._dynamo.eval_frame.OptimizedModule				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     bool = True				)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     _SCREAMING_SNAKE_CASE							    =is_compiled_module(_UpperCamelCase				)
     if is_compiled:
          _SCREAMING_SNAKE_CASE							    =model
          _SCREAMING_SNAKE_CASE							    =model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(_UpperCamelCase							, _UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =model.module

     if not keep_fpaa_wrapper:
          _SCREAMING_SNAKE_CASE							    =getattr(_UpperCamelCase							, 'forward'				)
          _SCREAMING_SNAKE_CASE							    =model.__dict__.pop('_original_forward'							, _UpperCamelCase				)
          if original_forward is not None:
               while hasattr(_UpperCamelCase							, '__wrapped__'				):
                    _SCREAMING_SNAKE_CASE							    =forward.__wrapped__
                    if forward == original_forward:
                         break
               _SCREAMING_SNAKE_CASE							    =forward
          if getattr(_UpperCamelCase							, '_converted_to_transformer_engine'							, _UpperCamelCase				):
               convert_model(_UpperCamelCase							, to_transformer_engine=_UpperCamelCase				)

     if is_compiled:
          _SCREAMING_SNAKE_CASE							    =model
          _SCREAMING_SNAKE_CASE							    =compiled_model

     return model


def        _lowerCAmelCase					(						)      ->			Optional[Any]:
     """simple docstring"""




     PartialState().wait_for_everyone()


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     Tuple				)      ->			Any:
     """simple docstring"""




     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(_UpperCamelCase							, _UpperCamelCase				)
     elif PartialState().local_process_index == 0:
          torch.save(_UpperCamelCase							, _UpperCamelCase				)


@contextmanager
def        _lowerCAmelCase					(						**_UpperCamelCase						:     Union[str, Any]				)      ->			int:
     """simple docstring"""




     for key, value in kwargs.items():
          _SCREAMING_SNAKE_CASE							    =str(_UpperCamelCase				)

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			Any:
     """simple docstring"""




     if not hasattr(_UpperCamelCase							, '__qualname__'				) and not hasattr(_UpperCamelCase							, '__name__'				):
          _SCREAMING_SNAKE_CASE							    =getattr(_UpperCamelCase							, '__class__'							, _UpperCamelCase				)
     if hasattr(_UpperCamelCase							, '__qualname__'				):
          return obj.__qualname__
     if hasattr(_UpperCamelCase							, '__name__'				):
          return obj.__name__
     return str(_UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Optional[Any]				)      ->			str:
     """simple docstring"""




     for key, value in source.items():
          if isinstance(_UpperCamelCase							, _UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =destination.setdefault(_UpperCamelCase							, {}				)
               merge_dicts(_UpperCamelCase							, _UpperCamelCase				)
          else:
               _SCREAMING_SNAKE_CASE							    =value

     return destination







def        _lowerCAmelCase					(						_UpperCamelCase						:     int = None				)      ->			bool:
     """simple docstring"""




     if port is None:
          _SCREAMING_SNAKE_CASE							    =2_95_00
     with socket.socket(socket.AF_INET							, socket.SOCK_STREAM				) as s:
          return s.connect_ex(('localhost', port)				) == 0



'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase :      List[Any]					 =     version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
     from nltk import word_tokenize


lowerCamelCase :      Any					 =     "\\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 :      Optional[Any]					 =     "\\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 :      Optional[Any]					 =     "\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      :	Tuple )	->    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 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    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 						A  (		self      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






       if NLTK_VERSION >= version.Version('3.6.5' ):
            _SCREAMING_SNAKE_CASE							    =[
                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:
            _SCREAMING_SNAKE_CASE							    =[
                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 )}
