
"""simple docstring"""



import math
from typing import Optional

import numpy as np

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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"encodec"


				def __init__(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Dict=[1.5, 3.0, 6.0, 12.0, 24.0]				,     UpperCAmelCase_	:			Dict=24_000				,     UpperCAmelCase_	:			List[Any]=1				,     UpperCAmelCase_	:			str=False				,     UpperCAmelCase_	:			List[Any]=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			Optional[Any]=128				,     UpperCAmelCase_	:			Tuple=32				,     UpperCAmelCase_	:			int=1				,     UpperCAmelCase_	:			List[str]=[8, 5, 4, 2]				,     UpperCAmelCase_	:			int="weight_norm"				,     UpperCAmelCase_	:			int=7				,     UpperCAmelCase_	:			Union[str, Any]=7				,     UpperCAmelCase_	:			Dict=3				,     UpperCAmelCase_	:			int=2				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			Optional[Any]="reflect"				,     UpperCAmelCase_	:			List[str]=2				,     UpperCAmelCase_	:			List[Any]=2				,     UpperCAmelCase_	:			Optional[int]=1.0				,     UpperCAmelCase_	:			str=1_024				,     UpperCAmelCase_	:			int=None				,     UpperCAmelCase_	:			Any=True				,     **UpperCAmelCase_	:			List[Any]				,     )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			target_bandwidths
											_lowerCAmelCase  	=			sampling_rate
											_lowerCAmelCase  	=			audio_channels
											_lowerCAmelCase  	=			normalize
											_lowerCAmelCase  	=			chunk_length_s
											_lowerCAmelCase  	=			overlap
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_filters
											_lowerCAmelCase  	=			num_residual_layers
											_lowerCAmelCase  	=			upsampling_ratios
											_lowerCAmelCase  	=			norm_type
											_lowerCAmelCase  	=			kernel_size
											_lowerCAmelCase  	=			last_kernel_size
											_lowerCAmelCase  	=			residual_kernel_size
											_lowerCAmelCase  	=			dilation_growth_rate
											_lowerCAmelCase  	=			use_causal_conv
											_lowerCAmelCase  	=			pad_mode
											_lowerCAmelCase  	=			compress
											_lowerCAmelCase  	=			num_lstm_layers
											_lowerCAmelCase  	=			trim_right_ratio
											_lowerCAmelCase  	=			codebook_size
											_lowerCAmelCase  	=			codebook_dim if codebook_dim is not None else hidden_size
											_lowerCAmelCase  	=			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__(**UpperCAmelCase_  )


				@property
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  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	:			int  )		->  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	:			Optional[int]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			np.prod(self.upsampling_ratios  )
											return math.ceil(self.sampling_rate / hop_length  )







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







											"""simple docstring"""




											return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)  )








"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_snake_case          =						parser.parse_args()


_snake_case          =						'''cpu'''
_snake_case          =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_snake_case          =						'''path-to-your-trained-model'''
_snake_case          =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
	_snake_case          =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case          =						pipe.to(device)

# to channels last
_snake_case          =						pipe.unet.to(memory_format=torch.channels_last)
_snake_case          =						pipe.vae.to(memory_format=torch.channels_last)
_snake_case          =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
	_snake_case          =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
	_snake_case          =						pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')






