


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


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

A			:						List[str]        =  '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

A			:						Optional[int]        =  '\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    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))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(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/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     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		List[Any]=False						)		->  Optional[Any]:




							"""simple docstring"""


							if rouge_types is None:
									lowercase__	     =						["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

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

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

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

							return result



"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

          with self.assertRaises(_a      ,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=_a      ,       timesteps=_a							)

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a      ,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,       ):
                 scheduler.set_timesteps(timesteps=_a							)


