'''simple docstring'''




import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__UpperCAmelCase												= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__UpperCAmelCase												= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__UpperCAmelCase												= """
Calculates average rouge scores for a list of hypotheses and 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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""




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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """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/google-research/google-research/tree/master/rouge''']  ,    reference_urls=[
                 '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
                 '''https://github.com/google-research/google-research/tree/master/rouge''',
             ]  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		Optional[int]  ,    a		:		int  ,    a		:		int=None  ,    a		:		Optional[Any]=True  ,    a		:		Optional[Any]=False   ):





         """simple docstring"""



         if rouge_types is None:
             __lowerCamelCase          =  ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

         __lowerCamelCase          =  rouge_scorer.RougeScorer(rouge_types=a  ,    use_stemmer=a   )
         if use_aggregator:
             __lowerCamelCase          =  scoring.BootstrapAggregator()
         else:
             __lowerCamelCase          =  []

         for ref, pred in zip(a  ,    a   ):
             __lowerCamelCase          =  scorer.score(a  ,    a   )
             if use_aggregator:
                 aggregator.add_scores(a   )
             else:
                 scores.append(a   )

         if use_aggregator:
             __lowerCamelCase          =  aggregator.aggregate()
         else:
             __lowerCamelCase          =  {}
             for key in scores[0]:
                 __lowerCamelCase          =  [score[key] for score in scores]

         return result

'''simple docstring'''




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=(DPMSolverSDEScheduler,)
     lowerCamelCase  :					List[str]							=1_0






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {
             '''num_train_timesteps''': 11_00,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**a   )
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,    [0.00_02, 0.0_02, 0.02]   ):
             self.check_over_configs(beta_start=a  ,    beta_end=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75   ) < 1e-2
             assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53   ) < 1e-2
             assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03   ) < 1e-2
             assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25   ) < 1e-2
             assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38   ) < 1e-2
             assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a  ,    use_karras_sigmas=a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
