



"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Any										= (PNDMScheduler,)
					lowercase__   :       int										= (('num_inference_steps', 50),)

					def 			snake_case__      (    self				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				{
									    '''num_train_timesteps''': 1_0_0_0,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**lowerCamelCase__   )
									return config

					def 			snake_case__      (    self				,       lowerCamelCase__=0				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				dict(self.forward_default_kwargs   )
									_lowerCamelCase        =				kwargs.pop('''num_inference_steps'''				,       lowerCamelCase__   )
									_lowerCamelCase        =				self.dummy_sample
									_lowerCamelCase        =				0.1 * sample
									_lowerCamelCase        =				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

													_lowerCamelCase        =				scheduler.step_prk(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				new_scheduler.step_prk(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

													_lowerCamelCase        =				scheduler.step_plms(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				new_scheduler.step_plms(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self				,       lowerCamelCase__=0				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				dict(self.forward_default_kwargs   )
									_lowerCamelCase        =				kwargs.pop('''num_inference_steps'''				,       lowerCamelCase__   )
									_lowerCamelCase        =				self.dummy_sample
									_lowerCamelCase        =				0.1 * sample
									_lowerCamelCase        =				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													_lowerCamelCase        =				self.get_scheduler_config()
													_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )
													scheduler.set_timesteps(lowerCamelCase__   )

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

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

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

													_lowerCamelCase        =				scheduler.step_prk(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				new_scheduler.step_prk(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

													_lowerCamelCase        =				scheduler.step_plms(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				new_scheduler.step_plms(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

					def 			snake_case__      (    self				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				self.scheduler_classes[0]
									_lowerCamelCase        =				self.get_scheduler_config(**lowerCamelCase__   )
									_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )

									_lowerCamelCase        =				1_0
									_lowerCamelCase        =				self.dummy_model()
									_lowerCamelCase        =				self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__   )

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

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

									return sample

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				dict(self.forward_default_kwargs   )

									_lowerCamelCase        =				kwargs.pop('''num_inference_steps'''				,       lowerCamelCase__   )

									for scheduler_class in self.scheduler_classes:
													_lowerCamelCase        =				self.get_scheduler_config()
													_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )

													_lowerCamelCase        =				self.dummy_sample
													_lowerCamelCase        =				0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCamelCase__				,       '''set_timesteps'''   ):
																	scheduler.set_timesteps(lowerCamelCase__   )
													elif num_inference_steps is not None and not hasattr(lowerCamelCase__				,       '''set_timesteps'''   ):
																	_lowerCamelCase        =				num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowerCamelCase        =				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													_lowerCamelCase        =				dummy_past_residuals[:]

													_lowerCamelCase        =				scheduler.step_prk(lowerCamelCase__				,       0				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				scheduler.step_prk(lowerCamelCase__				,       1				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

													_lowerCamelCase        =				scheduler.step_plms(lowerCamelCase__				,       0				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample
													_lowerCamelCase        =				scheduler.step_plms(lowerCamelCase__				,       1				,       lowerCamelCase__				,       **lowerCamelCase__   ).prev_sample

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

					def 			snake_case__      (    self   ):
									for timesteps in [1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__   )

									_lowerCamelCase        =				self.scheduler_classes[0]
									_lowerCamelCase        =				self.get_scheduler_config(steps_offset=1   )
									_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )
									scheduler.set_timesteps(1_0   )
									assert torch.equal(
									    scheduler.timesteps				,       torch.LongTensor(
									        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]   )				,       )

					def 			snake_case__      (    self   ):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]				,       [0.0_0_2, 0.0_2]   ):
													self.check_over_configs(beta_start=lowerCamelCase__				,       beta_end=lowerCamelCase__   )

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

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

					def 			snake_case__      (    self   ):
									for t in [1, 5, 1_0]:
													self.check_over_forward(time_step=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									for t, num_inference_steps in zip([1, 5, 1_0]				,       [1_0, 5_0, 1_0_0]   ):
													self.check_over_forward(num_inference_steps=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									_lowerCamelCase        =				2_7

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

													_lowerCamelCase        =				self.get_scheduler_config()
													_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )

													scheduler.set_timesteps(lowerCamelCase__   )

													# 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(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ).prev_sample

					def 			snake_case__      (    self   ):
									with self.assertRaises(lowerCamelCase__   ):
													_lowerCamelCase        =				self.scheduler_classes[0]
													_lowerCamelCase        =				self.get_scheduler_config()
													_lowerCamelCase        =				scheduler_class(**lowerCamelCase__   )

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.full_loop()
									_lowerCamelCase        =				torch.sum(torch.abs(lowerCamelCase__   )   )
									_lowerCamelCase        =				torch.mean(torch.abs(lowerCamelCase__   )   )

									assert abs(result_sum.item() - 1_9_8.1_3_1_8   ) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0   ) < 1e-3

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.full_loop(prediction_type='''v_prediction'''   )
									_lowerCamelCase        =				torch.sum(torch.abs(lowerCamelCase__   )   )
									_lowerCamelCase        =				torch.mean(torch.abs(lowerCamelCase__   )   )

									assert abs(result_sum.item() - 6_7.3_9_8_6   ) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8   ) < 1e-3

					def 			snake_case__      (    self   ):
									# We specify different beta, so that the first alpha is 0.99
									_lowerCamelCase        =				self.full_loop(set_alpha_to_one=lowerCamelCase__				,       beta_start=0.0_1   )
									_lowerCamelCase        =				torch.sum(torch.abs(lowerCamelCase__   )   )
									_lowerCamelCase        =				torch.mean(torch.abs(lowerCamelCase__   )   )

									assert abs(result_sum.item() - 2_3_0.0_3_9_9   ) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5   ) < 1e-3





					def 			snake_case__      (    self   ):
									# We specify different beta, so that the first alpha is 0.99
									_lowerCamelCase        =				self.full_loop(set_alpha_to_one=lowerCamelCase__				,       beta_start=0.0_1   )
									_lowerCamelCase        =				torch.sum(torch.abs(lowerCamelCase__   )   )
									_lowerCamelCase        =				torch.mean(torch.abs(lowerCamelCase__   )   )

									assert abs(result_sum.item() - 1_8_6.9_4_8_2   ) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4   ) < 1e-3





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
