
'''simple docstring'''





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 			_lowerCAmelCase      (			_snake_case ):
				_lowercase    	=(PNDMScheduler,)
				_lowercase    	=(("""num_inference_steps""", 50),)




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			{
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**snake_case_  )
							return config




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       snake_case_  )
							lowerCAmelCase_      			=			self.dummy_sample
							lowerCAmelCase_      			=			0.1 * sample
							lowerCAmelCase_      			=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      			=			self.get_scheduler_config(**snake_case_  )
										lowerCAmelCase_      			=			scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )
										# copy over dummy past residuals
										lowerCAmelCase_      			=			dummy_past_residuals[:]

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

										lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			new_scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample

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

										lowerCAmelCase_      			=			scheduler.step_plms(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			new_scheduler.step_plms(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample

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




				def   __a		(      self  )       ->   str:
							pass




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   Any:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       snake_case_  )
							lowerCAmelCase_      			=			self.dummy_sample
							lowerCAmelCase_      			=			0.1 * sample
							lowerCAmelCase_      			=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      			=			self.get_scheduler_config()
										lowerCAmelCase_      			=			scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )

										# copy over dummy past residuals (must be after setting timesteps)
										lowerCAmelCase_      			=			dummy_past_residuals[:]

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

													# copy over dummy past residual (must be after setting timesteps)
													lowerCAmelCase_      			=			dummy_past_residuals[:]

										lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			new_scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample

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

										lowerCAmelCase_      			=			scheduler.step_plms(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			new_scheduler.step_plms(snake_case_       ,       snake_case_       ,       snake_case_       ,       **snake_case_  ).prev_sample

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




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(**snake_case_  )
							lowerCAmelCase_      			=			scheduler_class(**snake_case_  )

							lowerCAmelCase_      			=			10
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							scheduler.set_timesteps(snake_case_  )

							for i, t in enumerate(scheduler.prk_timesteps  ):
										lowerCAmelCase_      			=			model(snake_case_       ,       snake_case_  )
										lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_  ).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps  ):
										lowerCAmelCase_      			=			model(snake_case_       ,       snake_case_  )
										lowerCAmelCase_      			=			scheduler.step_plms(snake_case_       ,       snake_case_       ,       snake_case_  ).prev_sample

							return sample




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )

							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       snake_case_  )

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      			=			self.get_scheduler_config()
										lowerCAmelCase_      			=			scheduler_class(**snake_case_  )

										lowerCAmelCase_      			=			self.dummy_sample
										lowerCAmelCase_      			=			0.1 * sample

										if num_inference_steps is not None and hasattr(snake_case_       ,       "set_timesteps"  ):
													scheduler.set_timesteps(snake_case_  )
										elif num_inference_steps is not None and not hasattr(snake_case_       ,       "set_timesteps"  ):
													lowerCAmelCase_      			=			num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										lowerCAmelCase_      			=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
										lowerCAmelCase_      			=			dummy_past_residuals[:]

										lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       0       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       1       ,       snake_case_       ,       **snake_case_  ).prev_sample

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

										lowerCAmelCase_      			=			scheduler.step_plms(snake_case_       ,       0       ,       snake_case_       ,       **snake_case_  ).prev_sample
										lowerCAmelCase_      			=			scheduler.step_plms(snake_case_       ,       1       ,       snake_case_       ,       **snake_case_  ).prev_sample

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




				def   __a		(      self  )       ->   Optional[int]:
							for timesteps in [100, 1_000]:
										self.check_over_configs(num_train_timesteps=snake_case_  )




				def   __a		(      self  )       ->   Union[str, Any]:
							for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=snake_case_  )

							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(steps_offset=1  )
							lowerCAmelCase_      			=			scheduler_class(**snake_case_  )
							scheduler.set_timesteps(10  )
							assert torch.equal(
							    scheduler.timesteps       ,       torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]  )       ,       )




				def   __a		(      self  )       ->   Optional[int]:
							for beta_start, beta_end in zip([0.0001, 0.001]       ,       [0.002, 0.02]  ):
										self.check_over_configs(beta_start=snake_case_       ,       beta_end=snake_case_  )




				def   __a		(      self  )       ->   Tuple:
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=snake_case_  )




				def   __a		(      self  )       ->   Dict:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=snake_case_  )




				def   __a		(      self  )       ->   str:
							for t in [1, 5, 10]:
										self.check_over_forward(time_step=snake_case_  )




				def   __a		(      self  )       ->   int:
							for t, num_inference_steps in zip([1, 5, 10]       ,       [10, 50, 100]  ):
										self.check_over_forward(num_inference_steps=snake_case_  )




				def   __a		(      self  )       ->   int:
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							lowerCAmelCase_      			=			27

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      			=			self.dummy_sample
										lowerCAmelCase_      			=			0.1 * sample

										lowerCAmelCase_      			=			self.get_scheduler_config()
										lowerCAmelCase_      			=			scheduler_class(**snake_case_  )

										scheduler.set_timesteps(snake_case_  )

										# before power of 3 fix, would error on first step, so we only need to do two
										for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
													lowerCAmelCase_      			=			scheduler.step_prk(snake_case_       ,       snake_case_       ,       snake_case_  ).prev_sample




				def   __a		(      self  )       ->   Union[str, Any]:
							with self.assertRaises(snake_case_  ):
										lowerCAmelCase_      			=			self.scheduler_classes[0]
										lowerCAmelCase_      			=			self.get_scheduler_config()
										lowerCAmelCase_      			=			scheduler_class(**snake_case_  )

										scheduler.step_plms(self.dummy_sample       ,       1       ,       self.dummy_sample  ).prev_sample




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.full_loop()
							lowerCAmelCase_      			=			torch.sum(torch.abs(snake_case_  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(snake_case_  )  )

							assert abs(result_sum.item() - 198.1318  ) < 1e-2
							assert abs(result_mean.item() - 0.2580  ) < 1e-3




				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			self.full_loop(prediction_type="v_prediction"  )
							lowerCAmelCase_      			=			torch.sum(torch.abs(snake_case_  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(snake_case_  )  )

							assert abs(result_sum.item() - 67.3986  ) < 1e-2
							assert abs(result_mean.item() - 0.0878  ) < 1e-3




				def   __a		(      self  )       ->   List[Any]:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase_      			=			self.full_loop(set_alpha_to_one=snake_case_       ,       beta_start=0.01  )
							lowerCAmelCase_      			=			torch.sum(torch.abs(snake_case_  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(snake_case_  )  )

							assert abs(result_sum.item() - 230.0399  ) < 1e-2
							assert abs(result_mean.item() - 0.2995  ) < 1e-3




				def   __a		(      self  )       ->   str:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase_      			=			self.full_loop(set_alpha_to_one=snake_case_       ,       beta_start=0.01  )
							lowerCAmelCase_      			=			torch.sum(torch.abs(snake_case_  )  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(snake_case_  )  )

							assert abs(result_sum.item() - 186.9482  ) < 1e-2
							assert abs(result_mean.item() - 0.2434  ) < 1e-3





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							if audio is not None and text is not None:
										raise ValueError(
										    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
							if audio_target is not None and text_target is not None:
										raise ValueError(
										    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
							if audio is None and audio_target is None and text is None and text_target is None:
										raise ValueError(
										    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

							if input_values is not None and input_ids is not None:
										raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
							if input_values is None and input_ids is None and labels is None:
										raise ValueError(
										    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

							if input_values is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



