

'''simple docstring'''
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 							A__    (       A__  ):
  A__		 =				(DEISMultistepScheduler,)
  A__		 =				(('num_inference_steps', 25),)





  def 						A  (		self      :	Optional[int]							,					**_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ={
           'num_train_timesteps': 1000,
           'beta_start': 0.00_01,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
       }

       config.update(**_a )
       return config





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Optional[Any]=0							,					**_a      :	Any )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =dict(self.forward_default_kwargs )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('num_inference_steps'							,					_a )
       _SCREAMING_SNAKE_CASE							    =self.dummy_sample
       _SCREAMING_SNAKE_CASE							    =0.1 * sample
       _SCREAMING_SNAKE_CASE							    =[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config(**_a )
            _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )
            scheduler.set_timesteps(_a )
            # copy over dummy past residuals
            _SCREAMING_SNAKE_CASE							    =dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_a )
                 _SCREAMING_SNAKE_CASE							    =scheduler_class.from_pretrained(_a )
                 new_scheduler.set_timesteps(_a )
                 # copy over dummy past residuals
                 _SCREAMING_SNAKE_CASE							    =dummy_past_residuals[: new_scheduler.config.solver_order]

            _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =sample, sample
            for t in range(_a							,					time_step + scheduler.config.solver_order + 1 ):
                 _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample
                 _SCREAMING_SNAKE_CASE							    =new_scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample

                 assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"





  def 						A  (		self      :	Union[str, Any] )	->    Any:





       '''simple docstring'''






       pass





  def 						A  (		self      :	Optional[int]							,					_a      :	List[Any]=0							,					**_a      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =dict(self.forward_default_kwargs )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('num_inference_steps'							,					_a )
       _SCREAMING_SNAKE_CASE							    =self.dummy_sample
       _SCREAMING_SNAKE_CASE							    =0.1 * sample
       _SCREAMING_SNAKE_CASE							    =[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )
            scheduler.set_timesteps(_a )

            # copy over dummy past residuals (must be after setting timesteps)
            _SCREAMING_SNAKE_CASE							    =dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_a )
                 _SCREAMING_SNAKE_CASE							    =scheduler_class.from_pretrained(_a )
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(_a )

                 # copy over dummy past residual (must be after setting timesteps)
                 _SCREAMING_SNAKE_CASE							    =dummy_past_residuals[: new_scheduler.config.solver_order]

            _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample
            _SCREAMING_SNAKE_CASE							    =new_scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample

            assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"





  def 						A  (		self      :	int							,					_a      :	Optional[Any]=None							,					**_a      :	Any )	->    Optional[int]:





       '''simple docstring'''






       if scheduler is None:
            _SCREAMING_SNAKE_CASE							    =self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config(**_a )
            _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )

       _SCREAMING_SNAKE_CASE							    =self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config(**_a )
       _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )

       _SCREAMING_SNAKE_CASE							    =10
       _SCREAMING_SNAKE_CASE							    =self.dummy_model()
       _SCREAMING_SNAKE_CASE							    =self.dummy_sample_deter
       scheduler.set_timesteps(_a )

       for i, t in enumerate(scheduler.timesteps ):
            _SCREAMING_SNAKE_CASE							    =model(_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a ).prev_sample

       return sample





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =dict(self.forward_default_kwargs )

       _SCREAMING_SNAKE_CASE							    =kwargs.pop('num_inference_steps'							,					_a )

       for scheduler_class in self.scheduler_classes:
            _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config()
            _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )

            _SCREAMING_SNAKE_CASE							    =self.dummy_sample
            _SCREAMING_SNAKE_CASE							    =0.1 * sample

            if num_inference_steps is not None and hasattr(_a							,					'set_timesteps' ):
                 scheduler.set_timesteps(_a )
            elif num_inference_steps is not None and not hasattr(_a							,					'set_timesteps' ):
                 _SCREAMING_SNAKE_CASE							    =num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            _SCREAMING_SNAKE_CASE							    =[residual + 0.2, residual + 0.15, residual + 0.10]
            _SCREAMING_SNAKE_CASE							    =dummy_past_residuals[: scheduler.config.solver_order]

            _SCREAMING_SNAKE_CASE							    =scheduler.timesteps[5]
            _SCREAMING_SNAKE_CASE							    =scheduler.timesteps[6]

            _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample
            _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a							,					**_a ).prev_sample

            self.assertEqual(output_a.shape							,					sample.shape )
            self.assertEqual(output_a.shape							,					output_a.shape )





  def 						A  (		self      :	Dict )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =DEISMultistepScheduler(**self.get_scheduler_config() )
       _SCREAMING_SNAKE_CASE							    =self.full_loop(scheduler=_a )
       _SCREAMING_SNAKE_CASE							    =torch.mean(torch.abs(_a ) )

       assert abs(result_mean.item() - 0.2_39_16 ) < 1e-3

       _SCREAMING_SNAKE_CASE							    =DPMSolverSinglestepScheduler.from_config(scheduler.config )
       _SCREAMING_SNAKE_CASE							    =DPMSolverMultistepScheduler.from_config(scheduler.config )
       _SCREAMING_SNAKE_CASE							    =UniPCMultistepScheduler.from_config(scheduler.config )
       _SCREAMING_SNAKE_CASE							    =DEISMultistepScheduler.from_config(scheduler.config )

       _SCREAMING_SNAKE_CASE							    =self.full_loop(scheduler=_a )
       _SCREAMING_SNAKE_CASE							    =torch.mean(torch.abs(_a ) )

       assert abs(result_mean.item() - 0.2_39_16 ) < 1e-3





  def 						A  (		self      :	Tuple )	->    Union[str, Any]:





       '''simple docstring'''






       for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=_a )





  def 						A  (		self      :	Optional[int] )	->    Dict:





       '''simple docstring'''






       self.check_over_configs(thresholding=_a )
       for order in [1, 2, 3]:
            for solver_type in ["logrho"]:
                 for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=_a							,					prediction_type=_a							,					sample_max_value=_a							,					algorithm_type='deis'							,					solver_order=_a							,					solver_type=_a							,					)





  def 						A  (		self      :	int )	->    List[Any]:





       '''simple docstring'''






       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=_a )





  def 						A  (		self      :	List[Any] )	->    Tuple:





       '''simple docstring'''






       for algorithm_type in ["deis"]:
            for solver_type in ["logrho"]:
                 for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=_a							,					solver_type=_a							,					prediction_type=_a							,					algorithm_type=_a							,					)
                           _SCREAMING_SNAKE_CASE							    =self.full_loop(
                               solver_order=_a							,					solver_type=_a							,					prediction_type=_a							,					algorithm_type=_a							,					)
                           assert not torch.isnan(_a ).any(), "Samples have nan numbers"





  def 						A  (		self      :	Any )	->    str:





       '''simple docstring'''






       self.check_over_configs(lower_order_final=_a )
       self.check_over_configs(lower_order_final=_a )





  def 						A  (		self      :	List[str] )	->    List[Any]:





       '''simple docstring'''






       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=_a							,					time_step=0 )





  def 						A  (		self      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.full_loop()
       _SCREAMING_SNAKE_CASE							    =torch.mean(torch.abs(_a ) )

       assert abs(result_mean.item() - 0.2_39_16 ) < 1e-3





  def 						A  (		self      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.full_loop(prediction_type='v_prediction' )
       _SCREAMING_SNAKE_CASE							    =torch.mean(torch.abs(_a ) )

       assert abs(result_mean.item() - 0.0_91 ) < 1e-3





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.scheduler_classes[0]
       _SCREAMING_SNAKE_CASE							    =self.get_scheduler_config(thresholding=_a							,					dynamic_thresholding_ratio=0 )
       _SCREAMING_SNAKE_CASE							    =scheduler_class(**_a )

       _SCREAMING_SNAKE_CASE							    =10
       _SCREAMING_SNAKE_CASE							    =self.dummy_model()
       _SCREAMING_SNAKE_CASE							    =self.dummy_sample_deter.half()
       scheduler.set_timesteps(_a )

       for i, t in enumerate(scheduler.timesteps ):
            _SCREAMING_SNAKE_CASE							    =model(_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =scheduler.step(_a							,					_a							,					_a ).prev_sample

       assert sample.dtype == torch.floataa



'''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 )}
