
import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  AutoencoderKL
	UpperCamelCase				=  """sample"""
	UpperCamelCase				=  1E-2





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= 4
			SCREAMING_SNAKE_CASE       :     str				= 3
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (32, 32)

			SCREAMING_SNAKE_CASE       :     Any				= floats_tensor((batch_size, num_channels) + sizes					).to(lowerCamelCase_					)

			return {"sample": image}





	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			return (3, 32, 32)





	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			List[str]:







			'''simple docstring'''




			return (3, 32, 32)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= {
			    '''block_out_channels''': [32, 64],
			    '''in_channels''': 3,
			    '''out_channels''': 3,
			    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
			    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
			    '''latent_channels''': 4,
			}
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.dummy_input
			return init_dict, inputs_dict





	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :str					)							->			str:







			'''simple docstring'''




			pass





	@unittest.skipIf(torch_device == '''mps'''		, '''Gradient checkpointing skipped on MPS'''					)
	def   __lowerCAmelCase (						self    :int					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.prepare_init_args_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model_class(**lowerCamelCase_					)
			model.to(lowerCamelCase_					)

			assert not model.is_gradient_checkpointing and model.training

			SCREAMING_SNAKE_CASE       :     List[str]				= model(**lowerCamelCase_					).sample
			# run the backwards pass on the model. For backwards pass, for simplicity purpose,
			# we won't calculate the loss and rather backprop on out.sum()
			model.zero_grad()

			SCREAMING_SNAKE_CASE       :     str				= torch.randn_like(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (out - labels).mean()
			loss.backward()

			# re-instantiate the model now enabling gradient checkpointing
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_class(**lowerCamelCase_					)
			# clone model
			model_a.load_state_dict(model.state_dict()					)
			model_a.to(lowerCamelCase_					)
			model_a.enable_gradient_checkpointing()

			assert model_a.is_gradient_checkpointing and model_a.training

			SCREAMING_SNAKE_CASE       :     List[Any]				= model_a(**lowerCamelCase_					).sample
			# run the backwards pass on the model. For backwards pass, for simplicity purpose,
			# we won't calculate the loss and rather backprop on out.sum()
			model_a.zero_grad()
			SCREAMING_SNAKE_CASE       :     List[Any]				= (out_a - labels).mean()
			loss_a.backward()

			# compare the output and parameters gradients
			self.assertTrue((loss - loss_a).abs() < 1E-5					)
			SCREAMING_SNAKE_CASE       :     Dict				= dict(model.named_parameters()					)
			SCREAMING_SNAKE_CASE       :     int				= dict(model_a.named_parameters()					)
			for name, param in named_params.items():
					self.assertTrue(torch_all_close(param.grad.data		, named_params_a[name].grad.data		, atol=5E-5					)					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''		, output_loading_info=lowerCamelCase_					)
			self.assertIsNotNone(lowerCamelCase_					)
			self.assertEqual(len(loading_info['''missing_keys''']					)		, 0					)

			model.to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= model(**self.dummy_input					)

			assert image is not None, "Make sure output is not None"







	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''					)
			SCREAMING_SNAKE_CASE       :     str				= model.to(lowerCamelCase_					)
			model.eval()

			if torch_device == "mps":
					SCREAMING_SNAKE_CASE       :     List[Any]				= torch.manual_seed(0					)
			else:
					SCREAMING_SNAKE_CASE       :     int				= torch.Generator(device=lowerCamelCase_					).manual_seed(0					)

			SCREAMING_SNAKE_CASE       :     str				= torch.randn(
			    1		, model.config.in_channels		, model.config.sample_size		, model.config.sample_size		, generator=torch.manual_seed(0					)		, )
			SCREAMING_SNAKE_CASE       :     int				= image.to(lowerCamelCase_					)
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(lowerCamelCase_		, sample_posterior=lowerCamelCase_		, generator=lowerCamelCase_					).sample

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= output[0, -1, -3:, -3:].flatten().cpu()

			# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
			# the expected output slices are not the same for CPU and GPU.
			if torch_device == "mps":
					SCREAMING_SNAKE_CASE       :     int				= torch.tensor(
					    [
					        -4.0078E-01,
					        -3.8323E-04,
					        -1.2681E-01,
					        -1.1462E-01,
					        2.0095E-01,
					        1.0893E-01,
					        -8.8247E-02,
					        -3.0361E-01,
					        -9.8644E-03,
					    ]					)
			elif torch_device == "cpu":
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor(
					    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]					)
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor(
					    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]					)

			self.assertTrue(torch_all_close(lowerCamelCase_		, lowerCamelCase_		, rtol=1E-2					)					)





@slow
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :str		, lowerCamelCase_    :Dict					)							->			Union[str, Any]:







			'''simple docstring'''




			return f"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase_					) for s in shape]					)}.npy"





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[int]:







			'''simple docstring'''




			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Optional[Any]=0		, lowerCamelCase_    :Optional[Any]=(4, 3, 5_12, 5_12)		, lowerCamelCase_    :Union[str, Any]=False					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= torch.floataa if fpaa else torch.floataa
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_		, lowerCamelCase_					)					)					).to(lowerCamelCase_					).to(lowerCamelCase_					)
			return image





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :str="CompVis/stable-diffusion-v1-4"		, lowerCamelCase_    :int=False					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= '''fp16''' if fpaa else None
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.floataa if fpaa else torch.floataa

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= AutoencoderKL.from_pretrained(
			    lowerCamelCase_		, subfolder='''vae'''		, torch_dtype=lowerCamelCase_		, revision=lowerCamelCase_		, )
			model.to(lowerCamelCase_					).eval()

			return model





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Tuple=0					)							->			Optional[Any]:







			'''simple docstring'''




			if torch_device == "mps":
					return torch.manual_seed(lowerCamelCase_					)
			return torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]					)
	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.get_sd_vae_model()
			SCREAMING_SNAKE_CASE       :     List[str]				= self.get_sd_image(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_generator(lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     str				= model(lowerCamelCase_		, generator=lowerCamelCase_		, sample_posterior=lowerCamelCase_					).sample

			assert sample.shape == image.shape

			SCREAMING_SNAKE_CASE       :     List[str]				= sample[-1, -2:, -2:, :2].flatten().float().cpu()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice					)

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=3E-3					)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
	        [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
	        # fmt: on
	    ]					)
	@require_torch_gpu
	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :List[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_sd_vae_model(fpaa=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= self.get_sd_image(lowerCamelCase_		, fpaa=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= self.get_generator(lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_		, generator=lowerCamelCase_		, sample_posterior=lowerCamelCase_					).sample

			assert sample.shape == image.shape

			SCREAMING_SNAKE_CASE       :     Tuple				= sample[-1, -2:, :2, -2:].flatten().float().cpu()
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor(lowerCamelCase_					)

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=1E-2					)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]					)
	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :int					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_sd_vae_model()
			SCREAMING_SNAKE_CASE       :     Any				= self.get_sd_image(lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCamelCase_					).sample

			assert sample.shape == image.shape

			SCREAMING_SNAKE_CASE       :     Any				= sample[-1, -2:, -2:, :2].flatten().float().cpu()
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice					)

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=3E-3					)





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
	        [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
	        # fmt: on
	    ]					)
	@require_torch_gpu
	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_sd_vae_model()
			SCREAMING_SNAKE_CASE       :     Dict				= self.get_sd_image(lowerCamelCase_		, shape=(3, 4, 64, 64)					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Any				= model.decode(lowerCamelCase_					).sample

			assert list(sample.shape					) == [3, 3, 5_12, 5_12]

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= sample[-1, -2:, :2, -2:].flatten().cpu()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor(lowerCamelCase_					)

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=1E-3					)





	@parameterized.expand(
	    [
	        # fmt: off
	        [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
	        [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
	        # fmt: on
	    ]					)
	@require_torch_gpu
	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.get_sd_vae_model(fpaa=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_sd_image(lowerCamelCase_		, shape=(3, 4, 64, 64)		, fpaa=lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model.decode(lowerCamelCase_					).sample

			assert list(sample.shape					) == [3, 3, 5_12, 5_12]

			SCREAMING_SNAKE_CASE       :     str				= sample[-1, -2:, :2, -2:].flatten().float().cpu()
			SCREAMING_SNAKE_CASE       :     Any				= torch.tensor(lowerCamelCase_					)

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=5E-3					)





	@parameterized.expand([(13,), (16,), (27,)]					)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()		, reason='''xformers is not required when using PyTorch 2.0.'''					)
	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_sd_vae_model(fpaa=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.get_sd_image(lowerCamelCase_		, shape=(3, 4, 64, 64)		, fpaa=lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     List[str]				= model.decode(lowerCamelCase_					).sample

			model.enable_xformers_memory_efficient_attention()
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model.decode(lowerCamelCase_					).sample

			assert list(sample.shape					) == [3, 3, 5_12, 5_12]

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=1E-1					)





	@parameterized.expand([(13,), (16,), (37,)]					)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()		, reason='''xformers is not required when using PyTorch 2.0.'''					)
	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Optional[int]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_sd_vae_model()
			SCREAMING_SNAKE_CASE       :     Dict				= self.get_sd_image(lowerCamelCase_		, shape=(3, 4, 64, 64)					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     List[str]				= model.decode(lowerCamelCase_					).sample

			model.enable_xformers_memory_efficient_attention()
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     str				= model.decode(lowerCamelCase_					).sample

			assert list(sample.shape					) == [3, 3, 5_12, 5_12]

			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=1E-2					)







	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
	        [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
	        # fmt: on
	    ]					)
	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.get_sd_vae_model()
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_sd_image(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_generator(lowerCamelCase_					)

			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model.encode(lowerCamelCase_					).latent_dist
					SCREAMING_SNAKE_CASE       :     Dict				= dist.sample(generator=lowerCamelCase_					)

			assert list(sample.shape					) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= sample[0, -1, -3:, -3:].flatten().cpu()
			SCREAMING_SNAKE_CASE       :     Any				= torch.tensor(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= 3E-3 if torch_device != '''mps''' else 1E-2
			assert torch_all_close(lowerCamelCase_		, lowerCamelCase_		, atol=lowerCamelCase_					)







"""simple docstring"""






import math
from typing import Optional

import numpy as np

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Any						     =			{
    "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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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__(**lowerCamelCase_					)





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			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   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





