






import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


UpperCAmelCase_					:		List[str]           = {
    '''vit_b32''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 32,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 16,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_68,
        '''image_resolution''': 8_40,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 24,
        '''vision_width''': 10_24,
        '''vision_patch_size''': 14,
        '''transformer_width''': 7_68,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    List[str]=""      ,     __magic_name__   :    List[Any]="/"   )						->     Tuple:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      []

	for k, v in params.items():
		UpperCamelCase	:int		=      parent_key + sep + k if parent_key else k

		if isinstance(__magic_name__      ,     collections.MutableMapping   ):
			items.extend(flatten_nested_dict(__magic_name__      ,     __magic_name__      ,     sep=__magic_name__   ).items()   )
		else:
			items.append((new_key, v)   )
	return dict(__magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Any   )						->     Dict:






	"""simple docstring"""



	return jax.tree_util.tree_map(lambda __magic_name__   : x.astype(jnp.floataa   ) if x.dtype == jnp.bfloataa else x      ,     __magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    List[str]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:str		=      pt_attn_layer.in_proj_weight.chunk(3      ,     dim=0   )
	UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:str		=      pt_attn_layer.in_proj_bias.chunk(3      ,     dim=0   )

	UpperCamelCase	:str		=      pt_attn_layer.out_proj.weight
	UpperCamelCase	:str		=      pt_attn_layer.out_proj.bias

	UpperCamelCase	:List[Any]		=      q_proj
	UpperCamelCase	:List[Any]		=      q_proj_bias

	UpperCamelCase	:Optional[int]		=      k_proj
	UpperCamelCase	:List[str]		=      k_proj_bias

	UpperCamelCase	:Dict		=      v_proj
	UpperCamelCase	:Any		=      v_proj_bias

	UpperCamelCase	:int		=      out_proj_weights
	UpperCamelCase	:Optional[Any]		=      out_proj_bias

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    Union[str, Any]   )						->     Dict:






	"""simple docstring"""



	copy_linear(hf_mlp.fca      ,     pt_mlp.c_fc   )
	copy_linear(hf_mlp.fca      ,     pt_mlp.c_proj   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Optional[int]   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Dict		=      pt_linear.weight
	UpperCamelCase	:Union[str, Any]		=      pt_linear.bias

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    Optional[Any]   )						->     Dict:






	"""simple docstring"""



	copy_linear(hf_layer.layer_norma      ,     pt_layer.ln_a   )
	copy_linear(hf_layer.layer_norma      ,     pt_layer.ln_a   )

	# copy MLP
	copy_mlp(hf_layer.mlp      ,     pt_layer.mlp   )

	# copy attn
	copy_attn_layer(hf_layer.self_attn      ,     pt_layer.attn   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    Union[str, Any]   )						->     Optional[int]:






	"""simple docstring"""



	for hf_layer, pt_layer in zip(__magic_name__      ,     __magic_name__   ):
		copy_layer(__magic_name__      ,     __magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]      ,     __magic_name__   :    str   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:str		=      pt_model.token_embedding.weight
	UpperCamelCase	:Optional[int]		=      pt_model.positional_embedding

	# copy layer norm
	copy_linear(hf_encoder.final_layer_norm      ,     pt_model.ln_final   )

	# copy hidden layers
	copy_layers(hf_encoder.encoder.layers      ,     pt_model.transformer.resblocks   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    Any   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      pt_model.text_projection.data.T

	# copy text encoder
	copy_encoder(hf_model.text_model      ,     __magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Dict   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      pt_model.visual.proj.data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layernorm      ,     pt_model.visual.ln_pre   )
	copy_linear(hf_model.vision_model.post_layernorm      ,     pt_model.visual.ln_post   )

	# copy embeds
	UpperCamelCase	:List[str]		=      pt_model.visual.conva.weight.data
	UpperCamelCase	:Union[str, Any]		=      pt_model.visual.class_embedding
	UpperCamelCase	:Any		=      pt_model.visual.positional_embedding.data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers      ,     pt_model.visual.transformer.resblocks   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Union[str, Any]   )						->     int:






	"""simple docstring"""



	UpperCamelCase	:Any		=      flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]   )

	UpperCamelCase	:Dict		=      torch.from_numpy(flax_class_token_params["""scale"""]   )
	UpperCamelCase	:List[str]		=      torch.from_numpy(flax_class_token_params["""bias"""]   )
	UpperCamelCase	:int		=      nn.Parameter(__magic_name__   )
	UpperCamelCase	:Tuple		=      nn.Parameter(__magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    Tuple   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:str		=      hf_model.state_dict()
	UpperCamelCase	:Tuple		=      {}

	# Rename class prediction head flax params to pytorch HF
	UpperCamelCase	:Tuple		=      flatten_nested_dict(flax_params["""class_head"""]   )

	for flax_key, v in flax_class_params.items():
		UpperCamelCase	:str		=      flax_key.replace("""/"""      ,     """."""   )
		UpperCamelCase	:Optional[Any]		=      torch_key.replace(""".kernel"""      ,     """.weight"""   )
		UpperCamelCase	:str		=      torch_key.replace("""Dense_0"""      ,     """dense0"""   )
		UpperCamelCase	:str		=      """class_head.""" + torch_key

		if "weight" in torch_key and v.ndim == 2:
			UpperCamelCase	:Union[str, Any]		=      v.T

		UpperCamelCase	:str		=      nn.Parameter(torch.from_numpy(__magic_name__   )   )

	# Rename box prediction box flax params to pytorch HF
	UpperCamelCase	:List[str]		=      flatten_nested_dict(flax_params["""obj_box_head"""]   )

	for flax_key, v in flax_box_params.items():
		UpperCamelCase	:Dict		=      flax_key.replace("""/"""      ,     """."""   )
		UpperCamelCase	:List[str]		=      torch_key.replace(""".kernel"""      ,     """.weight"""   )
		UpperCamelCase	:Tuple		=      torch_key.replace("""_"""      ,     """"""   ).lower()
		UpperCamelCase	:Dict		=      """box_head.""" + torch_key

		if "weight" in torch_key and v.ndim == 2:
			UpperCamelCase	:Dict		=      v.T

		UpperCamelCase	:List[str]		=      nn.Parameter(torch.from_numpy(__magic_name__   )   )

	# Copy flax params to PyTorch params
	for name, param in new_params.items():
		if name in pt_params.keys():
			pt_params[name].copy_(__magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    Optional[int]   )						->     List[str]:






	"""simple docstring"""



	for k, v in flax_attn_params.items():
		if k.startswith("""transformer"""   ):
			UpperCamelCase	:List[str]		=      k.replace("""transformer.resblocks"""      ,     """text_model.encoder.layers"""   )
		else:
			UpperCamelCase	:Dict		=      k.replace("""visual.transformer.resblocks"""      ,     """vision_model.encoder.layers"""   )

		UpperCamelCase	:Tuple		=      torch_key.replace("""attn"""      ,     """self_attn"""   )
		UpperCamelCase	:Any		=      torch_key.replace("""key"""      ,     """k_proj"""   )
		UpperCamelCase	:Tuple		=      torch_key.replace("""value"""      ,     """v_proj"""   )
		UpperCamelCase	:str		=      torch_key.replace("""query"""      ,     """q_proj"""   )
		UpperCamelCase	:Optional[Any]		=      torch_key.replace("""out"""      ,     """out_proj"""   )

		if "bias" in torch_key and v.ndim == 2:
			UpperCamelCase	:Tuple		=      v.shape[0] * v.shape[1]
			UpperCamelCase	:Tuple		=      v.reshape(__magic_name__   )

		if "weight" in torch_key and "out" in torch_key:
			UpperCamelCase	:Any		=      (v.shape[0] * v.shape[1], v.shape[2])
			UpperCamelCase	:List[str]		=      v.reshape(__magic_name__   ).T

		if "weight" in torch_key and "out" not in torch_key:
			UpperCamelCase	:Optional[int]		=      (v.shape[0], v.shape[1] * v.shape[2])
			UpperCamelCase	:Optional[Any]		=      v.reshape(__magic_name__   ).T

		# Copy flax CLIP attn params to HF PyTorch params
		UpperCamelCase	:List[Any]		=      torch.from_numpy(__magic_name__   )
		hf_backbone.state_dict()[torch_key].copy_(__magic_name__   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[int]   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      {}
	UpperCamelCase	:Union[str, Any]		=      []

	for k, v in params.items():
		if "attn." in k:
			UpperCamelCase	:List[str]		=      k[: k.rindex("""attn."""   ) + 5]
			if base in processed_attn_layers:
				continue

			processed_attn_layers.append(__magic_name__   )
			UpperCamelCase	:int		=      params[base + """out.weight"""].shape[-1]
			UpperCamelCase	:int		=      params[base + """out.weight"""].reshape(__magic_name__      ,     __magic_name__   ).T
			UpperCamelCase	:Optional[int]		=      params[base + """out.bias"""]
		else:
			UpperCamelCase	:List[str]		=      v
	return new_params

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Tuple   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      CLIP(**__magic_name__   )
	torch_model.eval()
	UpperCamelCase	:Optional[int]		=      torch_model.state_dict()

	UpperCamelCase	:Optional[int]		=      flatten_nested_dict(flax_params["""backbone"""]["""clip"""]   )
	UpperCamelCase	:Optional[int]		=      {}

	for flax_key, v in flax_clip_params.items():
		UpperCamelCase	:Union[str, Any]		=      flax_key.replace("""/"""      ,     """."""   )
		UpperCamelCase	:Union[str, Any]		=      torch_key.replace("""text.token_embedding.embedding"""      ,     """token_embedding.kernel"""   )

		if (
		    torch_key.startswith("""text.transformer"""   )
		    or torch_key.startswith("""text.text_projection"""   )
		    or torch_key.startswith("""text.ln_final"""   )
		    or torch_key.startswith("""text.positional_embedding"""   )
		):
			UpperCamelCase	:Dict		=      torch_key[5:]

		UpperCamelCase	:int		=      torch_key.replace("""text_projection.kernel"""      ,     """text_projection"""   )
		UpperCamelCase	:List[Any]		=      torch_key.replace("""visual.proj.kernel"""      ,     """visual.proj"""   )
		UpperCamelCase	:str		=      torch_key.replace(""".scale"""      ,     """.weight"""   )
		UpperCamelCase	:Tuple		=      torch_key.replace(""".kernel"""      ,     """.weight"""   )

		if "conv" in torch_key or "downsample.0.weight" in torch_key:
			UpperCamelCase	:Dict		=      v.transpose(3      ,     2      ,     0      ,     1   )

		elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
			# Fully connected layers are transposed, embeddings are not
			UpperCamelCase	:List[str]		=      v.T

		UpperCamelCase	:str		=      v

	UpperCamelCase	:str		=      _convert_attn_layers(__magic_name__   )
	new_torch_params.update(__magic_name__   )
	UpperCamelCase	:Dict		=      {}

	# Copy flax CLIP backbone params to PyTorch params
	for name, param in new_torch_params.items():
		if name in torch_clip_params.keys():
			UpperCamelCase	:Optional[int]		=      torch.from_numpy(new_torch_params[name]   )
			torch_clip_params[name].copy_(__magic_name__   )
		else:
			UpperCamelCase	:Any		=      param

	return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Any      ,     __magic_name__   :    Union[str, Any]      ,     __magic_name__   :    List[Any]      ,     __magic_name__   :    Tuple=None   )						->     int:






	"""simple docstring"""



	UpperCamelCase	:int		=      Repository(__magic_name__      ,     clone_from=f"""google/{pytorch_dump_folder_path}"""   )
	repo.git_pull()

	if config_path is not None:
		UpperCamelCase	:List[str]		=      OwlViTConfig.from_pretrained(__magic_name__   )
	else:
		UpperCamelCase	:List[Any]		=      OwlViTConfig()

	UpperCamelCase	:int		=      OwlViTModel(__magic_name__   ).eval()
	UpperCamelCase	:Optional[Any]		=      OwlViTForObjectDetection(__magic_name__   ).eval()

	copy_text_model_and_projection(__magic_name__      ,     __magic_name__   )
	copy_vision_model_and_projection(__magic_name__      ,     __magic_name__   )
	UpperCamelCase	:List[str]		=      pt_backbone.logit_scale
	copy_flax_attn_params(__magic_name__      ,     __magic_name__   )

	UpperCamelCase	:List[str]		=      hf_backbone
	copy_class_merge_token(__magic_name__      ,     __magic_name__   )
	copy_class_box_heads(__magic_name__      ,     __magic_name__   )

	# Save HF model
	hf_model.save_pretrained(repo.local_dir   )

	# Initialize image processor
	UpperCamelCase	:str		=      OwlViTImageProcessor(
	    size=config.vision_config.image_size      ,     crop_size=config.vision_config.image_size   )
	# Initialize tokenizer
	UpperCamelCase	:Any		=      CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""      ,     pad_token="""!"""      ,     model_max_length=16   )

	# Initialize processor
	UpperCamelCase	:Union[str, Any]		=      OwlViTProcessor(image_processor=__magic_name__      ,     tokenizer=__magic_name__   )
	image_processor.save_pretrained(repo.local_dir   )
	processor.save_pretrained(repo.local_dir   )

	repo.git_add()
	repo.git_commit("""Upload model and processor"""   )
	repo.git_push()


if __name__ == "__main__":
	UpperCAmelCase_					:		Tuple           = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--owlvit_version''',
	    default=None,
	    type=str,
	    required=True,
	    help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
	)
	parser.add_argument(
	    '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
	)
	parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
	)
	UpperCAmelCase_					:		Union[str, Any]           = parser.parse_args()

	# Initialize PyToch clip model
	UpperCAmelCase_					:		int           = args.owlvit_version
	if model_name == "clip_b16":
		UpperCAmelCase_					:		Dict           = CONFIGS['''vit_b16''']
	elif model_name == "clip_b32":
		UpperCAmelCase_					:		Any           = CONFIGS['''vit_b32''']
	elif model_name == "clip_l14":
		UpperCAmelCase_					:		Union[str, Any]           = CONFIGS['''vit_l14''']

	# Load from checkpoint and convert params to float-32
	UpperCAmelCase_					:		Union[str, Any]           = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
	UpperCAmelCase_					:		Any           = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
	del variables

	# Convert CLIP backbone
	UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_					:		Tuple           = convert_clip_backbone(flax_params, torch_config)

	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)








import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		Dict           = {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_					:		Any           = '''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	str	      =    """bark_module"""
	snake_case__				:	int	      =    ["""past_key_values"""]

	snake_case__				:	Optional[int]	      =    {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	    """vocab_size""": """input_vocab_size""",
	    """window_size""": """block_size""",
	}





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]=1_024					,    __lowerCamelCase							:   int=10_048					,    __lowerCamelCase							:   List[Any]=10_048					,    __lowerCamelCase							:   str=12					,    __lowerCamelCase							:   Dict=12					,    __lowerCamelCase							:   Tuple=768					,    __lowerCamelCase							:   Dict=0.0					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Tuple=0.02					,    __lowerCamelCase							:   List[str]=True					,    **__lowerCamelCase							:   str					,    ):
		UpperCamelCase	:Optional[int]		=      block_size
		UpperCamelCase	:List[Any]		=      input_vocab_size
		UpperCamelCase	:int		=      output_vocab_size
		UpperCamelCase	:int		=      num_layers
		UpperCamelCase	:Union[str, Any]		=      num_heads
		UpperCamelCase	:Dict		=      hidden_size
		UpperCamelCase	:Any		=      dropout
		UpperCamelCase	:List[str]		=      bias
		UpperCamelCase	:Tuple		=      use_cache
		UpperCamelCase	:Any		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   Union[str, os.PathLike]					,    __lowerCamelCase							:   Optional[Union[str, os.PathLike]] = None					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   Optional[Union[str, bool]] = None					,    __lowerCamelCase							:   str = "main"					,    **__lowerCamelCase							:   List[Any]					,    ):
		UpperCamelCase	:Dict		=      cache_dir
		UpperCamelCase	:List[Any]		=      force_download
		UpperCamelCase	:List[Any]		=      local_files_only
		UpperCamelCase	:int		=      revision

		cls._set_token_in_kwargs(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	, UpperCamelCase	:Dict		=      cls.get_config_dict(__lowerCamelCase					,    **__lowerCamelCase      )

		# get the config dict if we are loading from Bark
		if config_dict.get("""model_type"""      ) == "bark":
			UpperCamelCase	:List[str]		=      config_dict[F"""{cls.model_type}_config"""]

		if "model_type" in config_dict and hasattr(cls					,    """model_type"""      ) and config_dict["model_type"] != cls.model_type:
			logger.warning(
			    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
			    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

		return cls.from_dict(__lowerCamelCase					,    **__lowerCamelCase      )

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""						,  model="""BarkSemanticModel""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Optional[int]	      =    """semantic"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""						,  model="""BarkCoarseModel""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    """coarse_acoustics"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""						,  model="""BarkFineModel""" )						,  """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	List[Any]	      =    """fine_acoustics"""





	def __init__(						self							:   Any					,    __lowerCamelCase							:   str=True					,    __lowerCamelCase							:   Optional[int]=8					,    __lowerCamelCase							:   Dict=1					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:List[str]		=      n_codes_total
		UpperCamelCase	:str		=      n_codes_given

		super().__init__(tie_word_embeddings=__lowerCamelCase					,    **__lowerCamelCase      )

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Union[str, Any]	      =    """bark"""
	snake_case__				:	Union[str, Any]	      =    True





	def __init__(						self							:   int					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   str=0.02					,    **__lowerCamelCase							:   List[str]					,    ):
		if semantic_config is None:
			UpperCamelCase	:Union[str, Any]		=      {}
			logger.info("""semantic_config is None. initializing the semantic model with default values."""      )

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

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

		if codec_config is None:
			UpperCamelCase	:Dict		=      {}
			logger.info("""codec_config is None. initializing the codec model with default values."""      )

		UpperCamelCase	:Tuple		=      BarkSemanticConfig(**__lowerCamelCase      )
		UpperCamelCase	:str		=      BarkCoarseConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      BarkFineConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      AutoConfig.for_model(**__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   BarkSemanticConfig					,    __lowerCamelCase							:   BarkCoarseConfig					,    __lowerCamelCase							:   BarkFineConfig					,    __lowerCamelCase							:   AutoConfig					,    **__lowerCamelCase							:   int					,    ):
		return cls(
		    semantic_config=semantic_config.to_dict()					,    coarse_acoustics_config=coarse_acoustics_config.to_dict()					,    fine_acoustics_config=fine_acoustics_config.to_dict()					,    codec_config=codec_config.to_dict()					,    **__lowerCamelCase					,    )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:List[Any]		=      copy.deepcopy(self.__dict__      )

		UpperCamelCase	:Any		=      self.semantic_config.to_dict()
		UpperCamelCase	:Optional[int]		=      self.coarse_acoustics_config.to_dict()
		UpperCamelCase	:Tuple		=      self.fine_acoustics_config.to_dict()
		UpperCamelCase	:List[str]		=      self.codec_config.to_dict()

		UpperCamelCase	:List[str]		=      self.__class__.model_type
		return output
