
'''simple docstring'''
import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class        SCREAMING_SNAKE_CASE__	(			nn.Linear):







				def __init__(    self		,							A_		,							A_		,							A_ = True		,							A_ = "constant"		,							A_ = 0.0		,							A_ = "topK"		,							)->						Tuple:







											'''simple docstring'''






											super(A_		,							self			).__init__(in_features=A_		,							out_features=A_		,							bias=A_			)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											UpperCamelCase					       =			pruning_method

											if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCamelCase					       =			mask_scale
																		UpperCamelCase					       =			mask_init
																		UpperCamelCase					       =			nn.Parameter(torch.empty(self.weight.size()			)			)
																		self.init_mask()







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											if self.mask_init == "constant":
																		init.constant_(self.mask_scores		,							val=self.mask_scale			)
											elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores		,							a=-self.mask_scale		,							b=self.mask_scale			)
											elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores		,							a=math.sqrt(5			)			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						int:







											'''simple docstring'''






											if self.pruning_method == "topK":
																		UpperCamelCase					       =			TopKBinarizer.apply(self.mask_scores		,							A_			)
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCamelCase					       =			'sigmoied' in self.pruning_method
																		UpperCamelCase					       =			ThresholdBinarizer.apply(self.mask_scores		,							A_		,							A_			)
											elif self.pruning_method == "magnitude":
																		UpperCamelCase					       =			MagnitudeBinarizer.apply(self.weight		,							A_			)
											elif self.pruning_method == "l0":
																		UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			-0.1, 1.1, 2 / 3
																		if self.training:
																									UpperCamelCase					       =			torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0_001		,							0.9_999			)
																									UpperCamelCase					       =			torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
																		else:
																									UpperCamelCase					       =			torch.sigmoid(self.mask_scores			)
																		UpperCamelCase					       =			s * (r - l) + l
																		UpperCamelCase					       =			s_bar.clamp(min=0.0		,							max=1.0			)
											# Mask weights with computed mask
											UpperCamelCase					       =			mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(A_		,							A_		,							self.bias			)






'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase :  Union[str, Any]      =      logging.get_logger(__name__)

lowerCAmelCase :  List[Any]      =      {
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen_decoder"""
				lowerCAmelCase_			       =				["""past_key_values"""]







				def __init__(    self		,							A_=2048		,							A_=2048		,							A_=24		,							A_=4096		,							A_=16		,							A_=0.0		,							A_=True		,							A_="gelu"		,							A_=1024		,							A_=0.1		,							A_=0.0		,							A_=0.0		,							A_=0.02		,							A_=False		,							A_=4		,							A_=2048		,							A_=2048		,							A_=None		,							A_=False		,							**A_		,							)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			vocab_size
											UpperCamelCase					       =			max_position_embeddings
											UpperCamelCase					       =			hidden_size
											UpperCamelCase					       =			ffn_dim
											UpperCamelCase					       =			num_hidden_layers
											UpperCamelCase					       =			num_attention_heads
											UpperCamelCase					       =			dropout
											UpperCamelCase					       =			attention_dropout
											UpperCamelCase					       =			activation_dropout
											UpperCamelCase					       =			activation_function
											UpperCamelCase					       =			initializer_factor
											UpperCamelCase					       =			layerdrop
											UpperCamelCase					       =			use_cache
											UpperCamelCase					       =			scale_embedding  # scale factor will be sqrt(d_model) if True
											UpperCamelCase					       =			num_codebooks
											super().__init__(
											    pad_token_id=A_		,							bos_token_id=A_		,							eos_token_id=A_		,							tie_word_embeddings=A_		,							**A_		,							)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen"""
				lowerCAmelCase_			       =				True







				def __init__(    self		,							**A_			)->						Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_			)
											if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																		raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'			)

											UpperCamelCase					       =			kwargs.pop('text_encoder'			)
											UpperCamelCase					       =			text_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('audio_encoder'			)
											UpperCamelCase					       =			audio_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('decoder'			)

											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			MusicgenDecoderConfig(**A_			)
											UpperCamelCase					       =			True







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							A_		,							A_		,							**A_		,							)->						Tuple:







											'''simple docstring'''






											return cls(
											    text_encoder=text_encoder_config.to_dict()		,							audio_encoder=audio_encoder_config.to_dict()		,							decoder=decoder_config.to_dict()		,							**A_		,							)







				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			copy.deepcopy(self.__dict__			)
											UpperCamelCase					       =			self.text_encoder.to_dict()
											UpperCamelCase					       =			self.audio_encoder.to_dict()
											UpperCamelCase					       =			self.decoder.to_dict()
											UpperCamelCase					       =			self.__class__.model_type
											return output




