


'''simple docstring'''




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class  lowerCAmelCase				(				_snake_case							):
	_lowerCamelCase   :					Dict			      = (DDPMScheduler,)



	def 				lowercase     (					self ,					**snake_case__    ):
		lowerCAmelCase					:    Dict      							= {
		    "num_train_timesteps": 1000,
		    "beta_start": 0.0_0_0_1,
		    "beta_end": 0.0_2,
		    "beta_schedule": "linear",
		    "variance_type": "fixed_small",
		    "clip_sample": True,
		}

		config.update(**snake_case_    )
		return config



	def 				lowercase     (					self    ):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=snake_case_    )



	def 				lowercase     (					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=snake_case_ ,					beta_end=snake_case_    )



	def 				lowercase     (					self    ):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=snake_case_    )



	def 				lowercase     (					self    ):
		for variance in ["fixed_small", "fixed_large", "other"]:
			self.check_over_configs(variance_type=snake_case_    )



	def 				lowercase     (					self    ):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=snake_case_    )



	def 				lowercase     (					self    ):
		self.check_over_configs(thresholding=snake_case_    )
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "sample", "v_prediction"]:
				self.check_over_configs(
				    thresholding=snake_case_ ,					prediction_type=snake_case_ ,					sample_max_value=snake_case_ ,					)



	def 				lowercase     (					self    ):
		for prediction_type in ["epsilon", "sample", "v_prediction"]:
			self.check_over_configs(prediction_type=snake_case_    )



	def 				lowercase     (					self    ):
		for t in [0, 500, 999]:
			self.check_over_forward(time_step=snake_case_    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.scheduler_classes[0]
		lowerCAmelCase					:    Optional[Any]      							= self.get_scheduler_config()
		lowerCAmelCase					:    Tuple      							= scheduler_class(**snake_case_    )

		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 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.scheduler_classes[0]
		lowerCAmelCase					:    List[Any]      							= self.get_scheduler_config()
		lowerCAmelCase					:    Optional[int]      							= scheduler_class(**snake_case_    )

		lowerCAmelCase					:    Any      							= len(snake_case_    )

		lowerCAmelCase					:    str      							= self.dummy_model()
		lowerCAmelCase					:    Union[str, Any]      							= self.dummy_sample_deter
		lowerCAmelCase					:    Union[str, Any]      							= torch.manual_seed(0    )

		for t in reversed(range(snake_case_    )    ):
			# 1. predict noise residual
			lowerCAmelCase					:    List[str]      							= model(snake_case_ ,					snake_case_    )

			# 2. predict previous mean of sample x_t-1
			lowerCAmelCase					:    Dict      							= scheduler.step(snake_case_ ,					snake_case_ ,					snake_case_ ,					generator=snake_case_    ).prev_sample

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

		lowerCAmelCase					:    Any      							= torch.sum(torch.abs(snake_case_    )    )
		lowerCAmelCase					:    str      							= torch.mean(torch.abs(snake_case_    )    )

		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 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.scheduler_classes[0]
		lowerCAmelCase					:    Tuple      							= self.get_scheduler_config(prediction_type='v_prediction'    )
		lowerCAmelCase					:    Union[str, Any]      							= scheduler_class(**snake_case_    )

		lowerCAmelCase					:    str      							= len(snake_case_    )

		lowerCAmelCase					:    Tuple      							= self.dummy_model()
		lowerCAmelCase					:    Dict      							= self.dummy_sample_deter
		lowerCAmelCase					:    Tuple      							= torch.manual_seed(0    )

		for t in reversed(range(snake_case_    )    ):
			# 1. predict noise residual
			lowerCAmelCase					:    Dict      							= model(snake_case_ ,					snake_case_    )

			# 2. predict previous mean of sample x_t-1
			lowerCAmelCase					:    Dict      							= scheduler.step(snake_case_ ,					snake_case_ ,					snake_case_ ,					generator=snake_case_    ).prev_sample

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

		lowerCAmelCase					:    Dict      							= torch.sum(torch.abs(snake_case_    )    )
		lowerCAmelCase					:    Optional[Any]      							= torch.mean(torch.abs(snake_case_    )    )

		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 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.scheduler_classes[0]
		lowerCAmelCase					:    Tuple      							= self.get_scheduler_config()
		lowerCAmelCase					:    Dict      							= scheduler_class(**snake_case_    )

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

		scheduler.set_timesteps(timesteps=snake_case_    )

		lowerCAmelCase					:    Tuple      							= scheduler.timesteps

		for i, timestep in enumerate(snake_case_    ):
			if i == len(snake_case_    ) - 1:
				lowerCAmelCase					:    int      							= -1
			else:
				lowerCAmelCase					:    Optional[Any]      							= timesteps[i + 1]

			lowerCAmelCase					:    str      							= scheduler.previous_timestep(snake_case_    )
			lowerCAmelCase					:    List[Any]      							= prev_t.item()

			self.assertEqual(snake_case_ ,					snake_case_    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.scheduler_classes[0]
		lowerCAmelCase					:    str      							= self.get_scheduler_config()
		lowerCAmelCase					:    Optional[Any]      							= scheduler_class(**snake_case_    )

		lowerCAmelCase					:    Union[str, Any]      							= [100, 87, 50, 51, 0]

		with self.assertRaises(snake_case_ ,					msg='`custom_timesteps` must be in descending order.'    ):
			scheduler.set_timesteps(timesteps=snake_case_    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.scheduler_classes[0]
		lowerCAmelCase					:    int      							= self.get_scheduler_config()
		lowerCAmelCase					:    Union[str, Any]      							= scheduler_class(**snake_case_    )

		lowerCAmelCase					:    List[str]      							= [100, 87, 50, 1, 0]
		lowerCAmelCase					:    Optional[int]      							= len(snake_case_    )

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.scheduler_classes[0]
		lowerCAmelCase					:    Any      							= self.get_scheduler_config()
		lowerCAmelCase					:    Dict      							= scheduler_class(**snake_case_    )

		lowerCAmelCase					:    str      							= [scheduler.config.num_train_timesteps]

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




'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


_lowerCAmelCase  :		Dict		     =	'\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_lowerCAmelCase  :		Optional[Any]		     =	'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

_lowerCAmelCase  :		List[Any]		     =	'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



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



	def 				lowercase     (					self    ):
		if version.parse(scb.__version__    ) < version.parse('1.4.12'    ):
			raise ImportWarning(
			    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
			    'You can install it with `pip install "sacrebleu>=1.4.12"`.'    )
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,					citation=_CITATION ,					homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf' ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
		        {
		            'predictions': datasets.Value('string' ,					id='sequence'    ),
		            'references': datasets.Sequence(datasets.Value('string' ,					id='sequence'    ) ,					id='references'    ),
		        }    ) ,					codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf'] ,					reference_urls=[
		        'https://github.com/m-popovic/chrF',
		    ] ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ = CHRF.CHAR_ORDER ,					snake_case__ = CHRF.WORD_ORDER ,					snake_case__ = CHRF.BETA ,					snake_case__ = False ,					snake_case__ = False ,					snake_case__ = False ,					):
		lowerCAmelCase					:    List[str]      							= len(references[0]    )
		if any(len(snake_case__    ) != references_per_prediction for refs in references    ):
			raise ValueError('Sacrebleu requires the same number of references for each prediction'    )
		lowerCAmelCase					:    List[str]      							= [[refs[i] for refs in references] for i in range(snake_case__    )]

		lowerCAmelCase					:    Union[str, Any]      							= CHRF(snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Dict      							= sb_chrf.corpus_score(snake_case__ ,					snake_case__    )

		return {
		    "score": output.score,
		    "char_order": output.char_order,
		    "word_order": output.word_order,
		    "beta": output.beta,
		}
