




'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 			UpperCAmelCase_      (   _UpperCAmelCase       ):




							'''simple docstring'''



							a__						  =     (DDIMParallelScheduler,)
							a__						  =     (('''eta''', 0.0), ('''num_inference_steps''', 5_0))





							def    _UpperCAmelCase     (					self       :	Any       ,     **a       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					{
										    """num_train_timesteps""": 1_000,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """clip_sample""": True,
										}

										config.update(**lowercase_			)
										return config





							def    _UpperCAmelCase     (					self       :	Dict       ,     **a       :	List[str]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config(**lowercase_			)
										SCREAMING_SNAKE_CASE           =					scheduler_class(**lowercase_			)

										SCREAMING_SNAKE_CASE           =					10, 0.0

										SCREAMING_SNAKE_CASE           =					self.dummy_model()
										SCREAMING_SNAKE_CASE           =					self.dummy_sample_deter

										scheduler.set_timesteps(lowercase_			)

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE           =					model(lowercase_       ,     lowercase_			)
													SCREAMING_SNAKE_CASE           =					scheduler.step(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_			).prev_sample

										return sample





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[str]:
										for timesteps in [100, 500, 1_000]:
													self.check_over_configs(num_train_timesteps=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[Any]:
										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowercase_			)

										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config(steps_offset=1			)
										SCREAMING_SNAKE_CASE           =					scheduler_class(**lowercase_			)
										scheduler.set_timesteps(5			)
										assert torch.equal(scheduler.timesteps       ,     torch.LongTensor([801, 601, 401, 201, 1]			)			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Any:
										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,     [0.002, 0.02, 0.2, 2]			):
													self.check_over_configs(beta_start=lowercase_       ,     beta_end=lowercase_			)





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





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Any:
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[Any]:
										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Optional[Any]:
										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
										self.check_over_configs(thresholding=lowercase_			)
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowercase_       ,     prediction_type=lowercase_       ,     sample_max_value=lowercase_       ,     )





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[Any]:
										for t in [1, 10, 49]:
													self.check_over_forward(time_step=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Any:
										for t, num_inference_steps in zip([1, 10, 50]       ,     [10, 50, 500]			):
													self.check_over_forward(time_step=lowercase_       ,     num_inference_steps=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										for t, eta in zip([1, 10, 49]       ,     [0.0, 0.5, 1.0]			):
													self.check_over_forward(time_step=lowercase_       ,     eta=lowercase_			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config()
										SCREAMING_SNAKE_CASE           =					scheduler_class(**lowercase_			)

										assert torch.sum(torch.abs(scheduler._get_variance(0       ,     0			) - 0.0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420       ,     400			) - 0.1_4771			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980       ,     960			) - 0.3_2460			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0       ,     0			) - 0.0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ,     486			) - 0.0_0979			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ,     998			) - 0.02			)			) < 1E-5





							def    _UpperCAmelCase     (					self       :	int			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config()
										SCREAMING_SNAKE_CASE           =					scheduler_class(**lowercase_			)

										SCREAMING_SNAKE_CASE           =					10, 0.0
										scheduler.set_timesteps(lowercase_			)

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

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

										SCREAMING_SNAKE_CASE           =					model(samples.flatten(0       ,     1			)       ,     timesteps.flatten(0       ,     1			)			)
										SCREAMING_SNAKE_CASE           =					scheduler.batch_step_no_noise(lowercase_       ,     timesteps.flatten(0       ,     1			)       ,     samples.flatten(0       ,     1			)       ,     lowercase_			)

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(lowercase_			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(lowercase_			)			)

										assert abs(result_sum.item() - 1_147.7_904			) < 1E-2
										assert abs(result_mean.item() - 0.4982			) < 1E-3





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.full_loop()

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(lowercase_			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(lowercase_			)			)

										assert abs(result_sum.item() - 172.0_067			) < 1E-2
										assert abs(result_mean.item() - 0.22_3967			) < 1E-3





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.full_loop(prediction_type="""v_prediction"""			)

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(lowercase_			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(lowercase_			)			)

										assert abs(result_sum.item() - 52.5302			) < 1E-2
										assert abs(result_mean.item() - 0.0684			) < 1E-3





							def    _UpperCAmelCase     (					self       :	int			)			->					Any:
										# We specify different beta, so that the first alpha is 0.99
										SCREAMING_SNAKE_CASE           =					self.full_loop(set_alpha_to_one=lowercase_       ,     beta_start=0.01			)
										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(lowercase_			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(lowercase_			)			)

										assert abs(result_sum.item() - 149.8_295			) < 1E-2
										assert abs(result_mean.item() - 0.1951			) < 1E-3







							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										# We specify different beta, so that the first alpha is 0.99
										SCREAMING_SNAKE_CASE           =					self.full_loop(set_alpha_to_one=lowercase_       ,     beta_start=0.01			)
										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(lowercase_			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(lowercase_			)			)

										assert abs(result_sum.item() - 149.0_784			) < 1E-2
										assert abs(result_mean.item() - 0.1941			) < 1E-3












import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							List[Any]							=  logging.get_logger(__name__)

__A						:							Tuple							=  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''encodec'''





							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,     a       :	Union[str, Any]=24_000       ,     a       :	List[Any]=1       ,     a       :	str=False       ,     a       :	List[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=128       ,     a       :	int=32       ,     a       :	int=1       ,     a       :	Dict=[8, 5, 4, 2]       ,     a       :	List[str]="weight_norm"       ,     a       :	str=7       ,     a       :	int=7       ,     a       :	Optional[int]=3       ,     a       :	Optional[int]=2       ,     a       :	Optional[int]=True       ,     a       :	Union[str, Any]="reflect"       ,     a       :	Dict=2       ,     a       :	Union[str, Any]=2       ,     a       :	Optional[Any]=1.0       ,     a       :	List[Any]=1_024       ,     a       :	int=None       ,     a       :	Dict=True       ,     **a       :	Tuple       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					target_bandwidths
										SCREAMING_SNAKE_CASE           =					sampling_rate
										SCREAMING_SNAKE_CASE           =					audio_channels
										SCREAMING_SNAKE_CASE           =					normalize
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_filters
										SCREAMING_SNAKE_CASE           =					num_residual_layers
										SCREAMING_SNAKE_CASE           =					upsampling_ratios
										SCREAMING_SNAKE_CASE           =					norm_type
										SCREAMING_SNAKE_CASE           =					kernel_size
										SCREAMING_SNAKE_CASE           =					last_kernel_size
										SCREAMING_SNAKE_CASE           =					residual_kernel_size
										SCREAMING_SNAKE_CASE           =					dilation_growth_rate
										SCREAMING_SNAKE_CASE           =					use_causal_conv
										SCREAMING_SNAKE_CASE           =					pad_mode
										SCREAMING_SNAKE_CASE           =					compress
										SCREAMING_SNAKE_CASE           =					num_lstm_layers
										SCREAMING_SNAKE_CASE           =					trim_right_ratio
										SCREAMING_SNAKE_CASE           =					codebook_size
										SCREAMING_SNAKE_CASE           =					codebook_dim if codebook_dim is not None else hidden_size
										SCREAMING_SNAKE_CASE           =					use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""			)

										super().__init__(**a			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1       ,     int((1.0 - self.overlap) * self.chunk_length			)			)





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					np.prod(self.upsampling_ratios			)
										return math.ceil(self.sampling_rate / hop_length			)







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)






