






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase : Union[str, Any]						   =       version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
	from nltk import word_tokenize


lowerCamelCase : List[str]						   =       '''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

lowerCamelCase : Union[str, Any]						   =       '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

lowerCamelCase : Any						   =       '''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION			)
class 		lowerCAmelCase     ( datasets.Metric			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	int							)			->  List[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       lowerCAmelCase				(  self				:	Dict		,     __a				:	List[Any]							)			->  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       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Dict		,     __a				:	Union[str, Any]		,     __a				:	Union[str, Any]=0.9		,     __a				:	Union[str, Any]=3		,     __a				:	Any=0.5							)			->  Optional[Any]:


												"""simple docstring"""

												if NLTK_VERSION >= version.Version("""3.6.5"""							):
																			__lowercase      :							Tuple				=     [
																			    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:
																			__lowercase      :							str				=     [
																			    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							)}







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps