


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DDIMParallelScheduler,)
					__UpperCAmelCase            =				(("eta", 0.0), ("num_inference_steps", 50))






					def A      (				self ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(**snake_case_						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase					, __lowercase 					=   1_0, 0.0

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter

								scheduler.set_timesteps(snake_case_						)

								for t in scheduler.timesteps:
											__lowercase 					=   model(snake_case_ ,       snake_case_						)
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						).prev_sample

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=snake_case_						)

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(steps_offset=1						)
								__lowercase 					=   scheduler_class(**snake_case_						)
								scheduler.set_timesteps(5						)
								assert torch.equal(scheduler.timesteps ,       torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]						)						)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								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 A      (				self						)   ->			List[Any]:







								'''simple docstring'''

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






					def A      (				self						)   ->			Any:







								'''simple docstring'''

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






					def A      (				self						)   ->			int:







								'''simple docstring'''

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






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=snake_case_						)






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







								'''simple docstring'''

								for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=snake_case_						)






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







								'''simple docstring'''

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								for t in [1, 1_0, 4_9]:
											self.check_over_forward(time_step=snake_case_						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 1_0, 5_0] ,       [1_0, 5_0, 5_0_0]						):
											self.check_over_forward(time_step=snake_case_ ,       num_inference_steps=snake_case_						)






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







								'''simple docstring'''

								for t, eta in zip([1, 1_0, 4_9] ,       [0.0, 0.5, 1.0]						):
											self.check_over_forward(time_step=snake_case_ ,       eta=snake_case_						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								assert torch.sum(torch.abs(scheduler._get_variance(0 ,       0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0 ,       4_0_0						) - 0.1_4_7_7_1						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0 ,       9_6_0						) - 0.3_2_4_6_0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0 ,       0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ,       4_8_6						) - 0.0_0_9_7_9						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ,       9_9_8						) - 0.0_2						)						) < 1e-5






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase					, __lowercase 					=   1_0, 0.0
								scheduler.set_timesteps(snake_case_						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								__lowercase 					=   self.dummy_sample_deter + 0.1
								__lowercase 					=   self.dummy_sample_deter - 0.1

								__lowercase 					=   samplea.shape[0]
								__lowercase 					=   torch.stack([samplea, samplea, samplea] ,       dim=0						)
								__lowercase 					=   torch.arange(snake_case_						)[0:3, None].repeat(1 ,       snake_case_						)

								__lowercase 					=   model(samples.flatten(0 ,       1						) ,       timesteps.flatten(0 ,       1						)						)
								__lowercase 					=   scheduler.batch_step_no_noise(snake_case_ ,       timesteps.flatten(0 ,       1						) ,       samples.flatten(0 ,       1						) ,       snake_case_						)

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_1_4_7.7_9_0_4						) < 1e-2
								assert abs(result_mean.item() - 0.4_9_8_2						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.full_loop()

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1e-2
								assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1e-3






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1e-2
								assert abs(result_mean.item() - 0.0_6_8_4						) < 1e-3






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(set_alpha_to_one=snake_case_ ,       beta_start=0.0_1						)
								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1e-2
								assert abs(result_mean.item() - 0.1_9_5_1						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(set_alpha_to_one=snake_case_ ,       beta_start=0.0_1						)
								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1e-2
								assert abs(result_mean.item() - 0.1_9_4_1						) < 1e-3






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


a : Optional[int]		=				datasets.logging.get_logger(__name__)


a : Tuple		=				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

a : Union[str, Any]		=				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

a : Union[str, Any]		=				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

a : Tuple		=				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION				)
class 	lowerCamelCase_       (				datasets.Metric				):



					'''simple docstring'''







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







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION ,       citation=_CITATION ,       homepage='''https://github.com/google-research/bleurt''' ,       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/bleurt'''] ,       reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,       )






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







								'''simple docstring'''

								if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''						)
											__lowercase 					=   '''bleurt-base-128'''

								if self.config_name.lower() in CHECKPOINT_URLS:
											__lowercase 					=   self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
											__lowercase 					=   self.config_name.upper()

								else:
											raise KeyError(
											    F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'						)

								# download the model checkpoint specified by self.config_name and set up the scorer
								__lowercase 					=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
								__lowercase 					=   score.BleurtScorer(os.path.join(snake_case_ ,       snake_case_						)						)






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.scorer.score(references=snake_case_ ,       candidates=snake_case_						)
								return {"scores": scores}


