
"""simple docstring"""




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=1			):

					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('.'			)[n_shave_prefix_segments:]			)
					else:
										return ".".join(path.split('.'			)[:n_shave_prefix_segments]			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=0			):
					SCREAMING_SNAKE_CASE				=	[]
					for old_item in old_list:
										SCREAMING_SNAKE_CASE				=	old_item
										SCREAMING_SNAKE_CASE				=	new_item.replace('block.',     'resnets.'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('conv_shorcut',     'conv1'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('in_shortcut',     'conv_shortcut'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('temb_proj',     'time_emb_proj'			)

										SCREAMING_SNAKE_CASE				=	shave_segments(A__,     n_shave_prefix_segments=A__			)

										mapping.append({'old': old_item, 'new': new_item}			)

					return mapping

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=False			):
					SCREAMING_SNAKE_CASE				=	[]
					for old_item in old_list:
										SCREAMING_SNAKE_CASE				=	old_item

										# In `model.mid`, the layer is called `attn`.
										if not in_mid:
															SCREAMING_SNAKE_CASE				=	new_item.replace('attn',     'attentions'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('.k.',     '.key.'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('.v.',     '.value.'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('.q.',     '.query.'			)

										SCREAMING_SNAKE_CASE				=	new_item.replace('proj_out',     'proj_attn'			)
										SCREAMING_SNAKE_CASE				=	new_item.replace('norm',     'group_norm'			)

										SCREAMING_SNAKE_CASE				=	shave_segments(A__,     n_shave_prefix_segments=A__			)
										mapping.append({'old': old_item, 'new': new_item}			)

					return mapping

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None			):
					assert isinstance(A__,     A__			), "Paths should be a list of dicts containing 'old' and 'new' keys."

					if attention_paths_to_split is not None:
										if config is None:
															raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'			)

										for path, path_map in attention_paths_to_split.items():
															SCREAMING_SNAKE_CASE				=	old_checkpoint[path]
															SCREAMING_SNAKE_CASE				=	old_tensor.shape[0] // 3

															SCREAMING_SNAKE_CASE				=	(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

															SCREAMING_SNAKE_CASE				=	old_tensor.shape[0] // config.get('num_head_channels',     1			) // 3

															SCREAMING_SNAKE_CASE				=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
															SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	old_tensor.split(channels // num_heads,     dim=1			)

															SCREAMING_SNAKE_CASE				=	query.reshape(A__			).squeeze()
															SCREAMING_SNAKE_CASE				=	key.reshape(A__			).squeeze()
															SCREAMING_SNAKE_CASE				=	value.reshape(A__			).squeeze()

					for path in paths:
										SCREAMING_SNAKE_CASE				=	path['new']

										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										SCREAMING_SNAKE_CASE				=	new_path.replace('down.',     'down_blocks.'			)
										SCREAMING_SNAKE_CASE				=	new_path.replace('up.',     'up_blocks.'			)

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				SCREAMING_SNAKE_CASE				=	new_path.replace(replacement['old'],     replacement['new']			)

										if "attentions" in new_path:
															SCREAMING_SNAKE_CASE				=	old_checkpoint[path['old']].squeeze()
										else:
															SCREAMING_SNAKE_CASE				=	old_checkpoint[path['old']]

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	{}

					SCREAMING_SNAKE_CASE				=	checkpoint['temb.dense.0.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['temb.dense.0.bias']
					SCREAMING_SNAKE_CASE				=	checkpoint['temb.dense.1.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['temb.dense.1.bias']

					SCREAMING_SNAKE_CASE				=	checkpoint['norm_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['norm_out.bias']

					SCREAMING_SNAKE_CASE				=	checkpoint['conv_in.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['conv_in.bias']
					SCREAMING_SNAKE_CASE				=	checkpoint['conv_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['conv_out.bias']

					SCREAMING_SNAKE_CASE				=	len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'down' in layer}			)
					SCREAMING_SNAKE_CASE				=	{
					    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(A__			)
					}

					SCREAMING_SNAKE_CASE				=	len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'up' in layer}			)
					SCREAMING_SNAKE_CASE				=	{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(A__			)}

					for i in range(A__			):
										SCREAMING_SNAKE_CASE				=	(i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''down.{i}.downsample.op.weight'''
															]
															SCREAMING_SNAKE_CASE				=	checkpoint[f'''down.{i}.downsample.op.bias''']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

										if any('block' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'block' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
															    for layer_id in range(A__			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']			):
																									SCREAMING_SNAKE_CASE				=	renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(A__,     A__,     A__			)

										if any('attn' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'attn' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
															    for layer_id in range(A__			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']			):
																									SCREAMING_SNAKE_CASE				=	renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     config=A__			)

					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.block_1' in key]
					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.block_2' in key]
					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					SCREAMING_SNAKE_CASE				=	renew_resnet_paths(A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}],     )

					SCREAMING_SNAKE_CASE				=	renew_resnet_paths(A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}],     )

					SCREAMING_SNAKE_CASE				=	renew_attention_paths(A__,     in_mid=A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}],     )

					for i in range(A__			):
										SCREAMING_SNAKE_CASE				=	num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''up.{i}.upsample.conv.weight'''
															]
															SCREAMING_SNAKE_CASE				=	checkpoint[f'''up.{i}.upsample.conv.bias''']

										if any('block' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'block' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(A__			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									SCREAMING_SNAKE_CASE				=	{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																									SCREAMING_SNAKE_CASE				=	renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     additional_replacements=[replace_indices]			)

										if any('attn' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'attn' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(A__			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									SCREAMING_SNAKE_CASE				=	{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																									SCREAMING_SNAKE_CASE				=	renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     additional_replacements=[replace_indices]			)

					SCREAMING_SNAKE_CASE				=	{k.replace('mid_new_2',     'mid_block'			): v for k, v in new_checkpoint.items()}
					return new_checkpoint

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	{}

					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.norm_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.norm_out.bias']

					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.conv_in.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.conv_in.bias']
					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.conv_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['encoder.conv_out.bias']

					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.norm_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.norm_out.bias']

					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.conv_in.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.conv_in.bias']
					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.conv_out.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['decoder.conv_out.bias']

					SCREAMING_SNAKE_CASE				=	len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'down' in layer}			)
					SCREAMING_SNAKE_CASE				=	{
					    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(A__			)
					}

					SCREAMING_SNAKE_CASE				=	len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'up' in layer}			)
					SCREAMING_SNAKE_CASE				=	{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(A__			)}

					for i in range(A__			):
										SCREAMING_SNAKE_CASE				=	(i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''encoder.down.{i}.downsample.conv.weight'''
															]
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''encoder.down.{i}.downsample.conv.bias'''
															]

										if any('block' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'block' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
															    for layer_id in range(A__			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']			):
																									SCREAMING_SNAKE_CASE				=	renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(A__,     A__,     A__			)

										if any('attn' in layer for layer in down_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'attn' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
															    for layer_id in range(A__			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']			):
																									SCREAMING_SNAKE_CASE				=	renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     config=A__			)

					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.block_1' in key]
					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.block_2' in key]
					SCREAMING_SNAKE_CASE				=	[key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					SCREAMING_SNAKE_CASE				=	renew_resnet_paths(A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}],     )

					SCREAMING_SNAKE_CASE				=	renew_resnet_paths(A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}],     )

					SCREAMING_SNAKE_CASE				=	renew_attention_paths(A__,     in_mid=A__			)
					assign_to_checkpoint(
					    A__,     A__,     A__,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}],     )

					for i in range(A__			):
										SCREAMING_SNAKE_CASE				=	num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''decoder.up.{i}.upsample.conv.weight'''
															]
															SCREAMING_SNAKE_CASE				=	checkpoint[
															    f'''decoder.up.{i}.upsample.conv.bias'''
															]

										if any('block' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'block' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(A__			)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									SCREAMING_SNAKE_CASE				=	{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																									SCREAMING_SNAKE_CASE				=	renew_resnet_paths(blocks[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     additional_replacements=[replace_indices]			)

										if any('attn' in layer for layer in up_blocks[i]			):
															SCREAMING_SNAKE_CASE				=	len(
															    {'.'.join(shave_segments(A__,     3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'attn' in layer}			)
															SCREAMING_SNAKE_CASE				=	{
															    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(A__			)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1			):
																									SCREAMING_SNAKE_CASE				=	{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																									SCREAMING_SNAKE_CASE				=	renew_attention_paths(attns[j]			)
																									assign_to_checkpoint(A__,     A__,     A__,     additional_replacements=[replace_indices]			)

					SCREAMING_SNAKE_CASE				=	{k.replace('mid_new_2',     'mid_block'			): v for k, v in new_checkpoint.items()}
					SCREAMING_SNAKE_CASE				=	checkpoint['quant_conv.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['quant_conv.bias']
					if "quantize.embedding.weight" in checkpoint:
										SCREAMING_SNAKE_CASE				=	checkpoint['quantize.embedding.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['post_quant_conv.weight']
					SCREAMING_SNAKE_CASE				=	checkpoint['post_quant_conv.bias']

					return new_checkpoint


if __name__ == "__main__":
	snake_case    =		argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)

	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the architecture.',
	)

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

	snake_case    =		parser.parse_args()
	snake_case    =		torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		snake_case    =		json.loads(f.read())

	# unet case
	snake_case    =		{key.split('.')[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		snake_case    =		convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		snake_case    =		convert_ddpm_checkpoint(checkpoint, config)

	if "ddpm" in config:
		del config["ddpm"]

	if config["_class_name"] == "VQModel":
		snake_case    =		VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		snake_case    =		AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		snake_case    =		UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		snake_case    =		DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

		snake_case    =		DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)


import copy
from typing import Dict

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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						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				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						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				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         if semantic_config is None:
             snake_case      =					{}
             logger.info("semantic_config is None. initializing the semantic model with default values."   )

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

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

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, 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      :    List[str]   )						->						int:
         snake_case      =					copy.deepcopy(self.__dict__   )

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

         snake_case      =					self.__class__.model_type
         return output

