'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   torch.FloatTensor

class 	SCREAMING_SNAKE_CASE      (    __lowercase     ,      __lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__( self		,    __A = None		,    __A = 3		,    __A = 3		,    __A = False		,    __A = "positional"		,    __A = 0		,    __A = True		,    __A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,    __A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,    __A = (224, 448, 672, 896)		,    __A = 2		,    __A = 1		,    __A = 1		,    __A = "conv"		,    __A = "conv"		,    __A = "silu"		,    __A = 8		,    __A = 32		,    __A = 1E-5		,    __A = "default"		,    __A = True		,    __A = None		,    __A = None		,    )			->  List[str]:
								super().__init__()

								_lowerCAmelCase					    =sample_size
								_lowerCAmelCase					    =block_out_channels[0] * 4

								# Check inputs
								if len(__A					) != len(__A					):
														raise ValueError(
														    F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''					)

								if len(__A					) != len(__A					):
														raise ValueError(
														    F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''					)

								# input
								_lowerCAmelCase					    =nn.Convad(__A		,    block_out_channels[0]		,    kernel_size=3		,    padding=(1, 1)					)

								# time
								if time_embedding_type == "fourier":
														_lowerCAmelCase					    =GaussianFourierProjection(embedding_size=block_out_channels[0]		,    scale=16					)
														_lowerCAmelCase					    =2 * block_out_channels[0]
								elif time_embedding_type == "positional":
														_lowerCAmelCase					    =Timesteps(block_out_channels[0]		,    __A		,    __A					)
														_lowerCAmelCase					    =block_out_channels[0]

								_lowerCAmelCase					    =TimestepEmbedding(__A		,    __A					)

								# class embedding
								if class_embed_type is None and num_class_embeds is not None:
														_lowerCAmelCase					    =nn.Embedding(__A		,    __A					)
								elif class_embed_type == "timestep":
														_lowerCAmelCase					    =TimestepEmbedding(__A		,    __A					)
								elif class_embed_type == "identity":
														_lowerCAmelCase					    =nn.Identity(__A		,    __A					)
								else:
														_lowerCAmelCase					    =None

								_lowerCAmelCase					    =nn.ModuleList([]					)
								_lowerCAmelCase					    =None
								_lowerCAmelCase					    =nn.ModuleList([]					)

								# down
								_lowerCAmelCase					    =block_out_channels[0]
								for i, down_block_type in enumerate(__A					):
														_lowerCAmelCase					    =output_channel
														_lowerCAmelCase					    =block_out_channels[i]
														_lowerCAmelCase					    =i == len(__A					) - 1

														_lowerCAmelCase					    =get_down_block(
														    __A		,    num_layers=__A		,    in_channels=__A		,    out_channels=__A		,    temb_channels=__A		,    add_downsample=not is_final_block		,    resnet_eps=__A		,    resnet_act_fn=__A		,    resnet_groups=__A		,    attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,    downsample_padding=__A		,    resnet_time_scale_shift=__A		,    downsample_type=__A		,    )
														self.down_blocks.append(__A					)

								# mid
								_lowerCAmelCase					    =UNetMidBlockaD(
								    in_channels=block_out_channels[-1]		,    temb_channels=__A		,    resnet_eps=__A		,    resnet_act_fn=__A		,    output_scale_factor=__A		,    resnet_time_scale_shift=__A		,    attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,    resnet_groups=__A		,    add_attention=__A		,    )

								# up
								_lowerCAmelCase					    =list(reversed(__A					)					)
								_lowerCAmelCase					    =reversed_block_out_channels[0]
								for i, up_block_type in enumerate(__A					):
														_lowerCAmelCase					    =output_channel
														_lowerCAmelCase					    =reversed_block_out_channels[i]
														_lowerCAmelCase					    =reversed_block_out_channels[min(i + 1		,    len(__A					) - 1					)]

														_lowerCAmelCase					    =i == len(__A					) - 1

														_lowerCAmelCase					    =get_up_block(
														    __A		,    num_layers=layers_per_block + 1		,    in_channels=__A		,    out_channels=__A		,    prev_output_channel=__A		,    temb_channels=__A		,    add_upsample=not is_final_block		,    resnet_eps=__A		,    resnet_act_fn=__A		,    resnet_groups=__A		,    attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,    resnet_time_scale_shift=__A		,    upsample_type=__A		,    )
														self.up_blocks.append(__A					)
														_lowerCAmelCase					    =output_channel

								# out
								_lowerCAmelCase					    =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,    32					)
								_lowerCAmelCase					    =nn.GroupNorm(num_channels=block_out_channels[0]		,    num_groups=__A		,    eps=__A					)
								_lowerCAmelCase					    =nn.SiLU()
								_lowerCAmelCase					    =nn.Convad(block_out_channels[0]		,    __A		,    kernel_size=3		,    padding=1					)






		def 		UpperCamelCase__							( self		,    __A		,    __A		,    __A = None		,    __A = True		,    )			->  Union[UNetaDOutput, Tuple]:
								# 0. center input if necessary
								if self.config.center_input_sample:
														_lowerCAmelCase					    =2 * sample - 1.0

								# 1. time
								_lowerCAmelCase					    =timestep
								if not torch.is_tensor(__A					):
														_lowerCAmelCase					    =torch.tensor([timesteps]		,    dtype=torch.long		,    device=sample.device					)
								elif torch.is_tensor(__A					) and len(timesteps.shape					) == 0:
														_lowerCAmelCase					    =timesteps[None].to(sample.device					)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								_lowerCAmelCase					    =timesteps * torch.ones(sample.shape[0]		,    dtype=timesteps.dtype		,    device=timesteps.device					)

								_lowerCAmelCase					    =self.time_proj(__A					)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might actually be running in fp16. so we need to cast here.
								# there might be better ways to encapsulate this.
								_lowerCAmelCase					    =t_emb.to(dtype=self.dtype					)
								_lowerCAmelCase					    =self.time_embedding(__A					)

								if self.class_embedding is not None:
														if class_labels is None:
																				raise ValueError('class_labels should be provided when doing class conditioning'					)

														if self.config.class_embed_type == "timestep":
																				_lowerCAmelCase					    =self.time_proj(__A					)

														_lowerCAmelCase					    =self.class_embedding(__A					).to(dtype=self.dtype					)
														_lowerCAmelCase					    =emb + class_emb

								# 2. pre-process
								_lowerCAmelCase					    =sample
								_lowerCAmelCase					    =self.conv_in(__A					)

								# 3. down
								_lowerCAmelCase					    =(sample,)
								for downsample_block in self.down_blocks:
														if hasattr(__A		,    'skip_conv'					):
																				_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =downsample_block(
																				    hidden_states=__A		,    temb=__A		,    skip_sample=__A					)
														else:
																				_lowerCAmelCase    ,					_lowerCAmelCase					    =downsample_block(hidden_states=__A		,    temb=__A					)

														down_block_res_samples += res_samples

								# 4. mid
								_lowerCAmelCase					    =self.mid_block(__A		,    __A					)

								# 5. up
								_lowerCAmelCase					    =None
								for upsample_block in self.up_blocks:
														_lowerCAmelCase					    =down_block_res_samples[-len(upsample_block.resnets					) :]
														_lowerCAmelCase					    =down_block_res_samples[: -len(upsample_block.resnets					)]

														if hasattr(__A		,    'skip_conv'					):
																				_lowerCAmelCase    ,					_lowerCAmelCase					    =upsample_block(__A		,    __A		,    __A		,    __A					)
														else:
																				_lowerCAmelCase					    =upsample_block(__A		,    __A		,    __A					)

        # 6. post-process
								_lowerCAmelCase					    =self.conv_norm_out(__A					)
								_lowerCAmelCase					    =self.conv_act(__A					)
								_lowerCAmelCase					    =self.conv_out(__A					)

								if skip_sample is not None:
														sample += skip_sample

								if self.config.time_embedding_type == "fourier":
														_lowerCAmelCase					    =timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
														_lowerCAmelCase					    =sample / timesteps

								if not return_dict:
														return (sample,)

								return UNetaDOutput(sample=__A					)






'''simple docstring'''

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


lowercase_  		=			logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'semantic'






		def __init__( self		,    __A=1_0000		,    __A=True		,    __A=768		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_0048		,    __A=12_9595		,    __A=12_9599		,    __A=1_0000		,    __A=256		,    __A=49.9		,    **__A		,    )			->  Any:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    eos_token_id=__A		,    renormalize_logits=__A		,    max_new_tokens=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =text_encoding_offset
								_lowerCAmelCase					    =text_pad_token
								_lowerCAmelCase					    =eos_token_id
								_lowerCAmelCase					    =semantic_infer_token
								_lowerCAmelCase					    =semantic_vocab_size
								_lowerCAmelCase					    =max_input_semantic_length
								_lowerCAmelCase					    =semantic_rate_hz

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'coarse_acoustics'






		def __init__( self		,    __A=True		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_2048		,    __A=75		,    __A=2		,    __A=1_2050		,    __A=256		,    __A = 630		,    __A = 60		,    **__A		,    )			->  Union[str, Any]:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    renormalize_logits=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =coarse_semantic_pad_token
								_lowerCAmelCase					    =coarse_rate_hz
								_lowerCAmelCase					    =n_coarse_codebooks
								_lowerCAmelCase					    =coarse_infer_token
								_lowerCAmelCase					    =max_coarse_input_length
								_lowerCAmelCase					    =max_coarse_history
								_lowerCAmelCase					    =sliding_window_len

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				'fine_acoustics'






		def __init__( self		,    __A=0.5		,    __A=512		,    __A=1024		,    __A=8		,    **__A		,    )			->  int:
								super().__init__(temperature=__A					)

								_lowerCAmelCase					    =max_fine_history_length
								_lowerCAmelCase					    =max_fine_input_length
								_lowerCAmelCase					    =n_fine_codebooks

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'bark'
		lowercase :   Optional[Any]							=				True






		def __init__( self		,    __A = None		,    __A = None		,    __A = None		,    __A=2_4000		,    __A=1024		,    **__A		,    )			->  Optional[int]:
								if semantic_config is None:
														_lowerCAmelCase					    ={}
														logger.info('semantic_config is None. initializing the semantic model with default values.'					)

								if coarse_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'					)

								if fine_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('fine_acoustics_config is None. initializing the fine model with default values.'					)

								_lowerCAmelCase					    =BarkSemanticGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkCoarseGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkFineGenerationConfig(**__A					)

								_lowerCAmelCase					    =sample_rate
								_lowerCAmelCase					    =codebook_size






		@classmethod
		def 		UpperCamelCase__							( cls		,    __A		,    __A		,    __A		,    **__A		,    )			->  List[Any]:
								return cls(
								    semantic_config=semantic_config.to_dict()		,    coarse_acoustics_config=coarse_acoustics_config.to_dict()		,    fine_acoustics_config=fine_acoustics_config.to_dict()		,    **__A		,    )






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =copy.deepcopy(self.__dict__					)

								_lowerCAmelCase					    =self.semantic_config.to_dict()
								_lowerCAmelCase					    =self.coarse_acoustics_config.to_dict()
								_lowerCAmelCase					    =self.fine_acoustics_config.to_dict()

								_lowerCAmelCase					    =self.__class__.model_type
								return output





