
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase											=    version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
 from nltk import word_tokenize


_lowercase											=    """\
@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},
}
"""

_lowercase											=    """\
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.
"""

_lowercase											=    """
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 			lowercase_		(      datasets.Metric       ):


       def 	_snake_case				(    self				)						->    List[Any]:
              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						,       __A				)						->    str:
              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						,       __A						,       __A						,       __A=0.9						,       __A=3						,       __A=0.5				)						->    Optional[Any]:
              if NLTK_VERSION >= version.Version('''3.6.5'''				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=[
                         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_							:				int		=[
                         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				)}


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
