

import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


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

set_seed(770)


A_	:			Optional[int]  	=      {
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


A_	:			int  	=      {
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

A_	:			str  	=      os.path.dirname(os.path.abspath(__file__))
A_	:			int  	=      os.path.join(os.path.expanduser('~'), '.cache')
A_	:			Tuple  	=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			Tuple=False )					->				Union[str, Any]:
	A__							:   Dict					       =		model_type
	if use_small:
		key += "_small"
	return os.path.join(lowercase_   ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			Optional[Any] )					->				List[Any]:
	os.makedirs(lowercase_   ,				exist_ok=lowercase_ )
	hf_hub_download(repo_id=lowercase_   ,				filename=lowercase_   ,				local_dir=lowercase_ )

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			List[str]   ,				lowercase_:			Any=False   ,				lowercase_:			int="text" )					->				Tuple:
	if model_type == "text":
		A__							:   str					       =		BarkSemanticModel
		A__							:   Dict					       =		BarkSemanticConfig
		A__							:   Tuple					       =		BarkSemanticGenerationConfig
	elif model_type == "coarse":
		A__							:   int					       =		BarkCoarseModel
		A__							:   Union[str, Any]					       =		BarkCoarseConfig
		A__							:   int					       =		BarkCoarseGenerationConfig
	elif model_type == "fine":
		A__							:   Union[str, Any]					       =		BarkFineModel
		A__							:   Any					       =		BarkFineConfig
		A__							:   Union[str, Any]					       =		BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	A__							:   Union[str, Any]					       =		f"""{model_type}_small""" if use_small else model_type
	A__							:   Union[str, Any]					       =		REMOTE_MODEL_PATHS[model_key]
	if not os.path.exists(lowercase_ ):
		logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
		_download(model_info["""repo_id"""]   ,				model_info["""file_name"""] )
	A__							:   Any					       =		torch.load(lowercase_   ,				map_location=lowercase_ )
	# this is a hack
	A__							:   Dict					       =		checkpoint["""model_args"""]
	if "input_vocab_size" not in model_args:
		A__							:   Dict					       =		model_args["""vocab_size"""]
		A__							:   Optional[Any]					       =		model_args["""vocab_size"""]
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	A__							:   Union[str, Any]					       =		model_args.pop("""n_head""" )
	A__							:   str					       =		model_args.pop("""n_embd""" )
	A__							:   Dict					       =		model_args.pop("""n_layer""" )

	A__							:   str					       =		ConfigClass(**checkpoint["""model_args"""] )
	A__							:   Optional[int]					       =		ModelClass(config=lowercase_ )
	A__							:   Dict					       =		GenerationConfigClass()

	A__							:   Tuple					       =		model_generation_config
	A__							:   int					       =		checkpoint["""model"""]
	# fixup checkpoint
	A__							:   List[Any]					       =		"""_orig_mod."""
	for k, v in list(state_dict.items() ):
		if k.startswith(lowercase_ ):
			# replace part of the key with corresponding layer name in HF implementation
			A__							:   Any					       =		k[len(lowercase_ ) :]
			for old_layer_name in new_layer_name_dict:
				A__							:   Any					       =		new_k.replace(lowercase_   ,				new_layer_name_dict[old_layer_name] )

			A__							:   Optional[int]					       =		state_dict.pop(lowercase_ )

	A__							:   Optional[Any]					       =		set(state_dict.keys() ) - set(model.state_dict().keys() )
	A__							:   int					       =		{k for k in extra_keys if not k.endswith(""".attn.bias""" )}
	A__							:   int					       =		set(model.state_dict().keys() ) - set(state_dict.keys() )
	A__							:   Optional[Any]					       =		{k for k in missing_keys if not k.endswith(""".attn.bias""" )}
	if len(lowercase_ ) != 0:
		raise ValueError(f"""extra keys found: {extra_keys}""" )
	if len(lowercase_ ) != 0:
		raise ValueError(f"""missing keys: {missing_keys}""" )
	model.load_state_dict(lowercase_   ,				strict=lowercase_ )
	A__							:   int					       =		model.num_parameters(exclude_embeddings=lowercase_ )
	A__							:   Union[str, Any]					       =		checkpoint["""best_val_loss"""].item()
	logger.info(f"""model loaded: {round(n_params/1E6   ,				1 )}M params, {round(lowercase_   ,				3 )} loss""" )
	model.eval()
	model.to(lowercase_ )
	del checkpoint, state_dict

	return model

def 						UpperCamelCase (lowercase_:			Tuple   ,				lowercase_:			Any=False   ,				lowercase_:			Optional[Any]="text" )					->				Union[str, Any]:
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	A__							:   Union[str, Any]					       =		"""cpu"""  # do conversion on cpu

	A__							:   List[str]					       =		_get_ckpt_path(lowercase_   ,				use_small=lowercase_ )
	A__							:   str					       =		_load_model(lowercase_   ,				lowercase_   ,				model_type=lowercase_   ,				use_small=lowercase_ )

	# load bark initial model
	A__							:   Union[str, Any]					       =		_bark_load_model(lowercase_   ,				"""cpu"""   ,				model_type=lowercase_   ,				use_small=lowercase_ )

	if model_type == "text":
		A__							:   str					       =		bark_model["""model"""]

	if model.num_parameters(exclude_embeddings=lowercase_ ) != bark_model.get_num_params():
		raise ValueError("""initial and new models don't have the same number of parameters""" )

	# check if same output as the bark model
	A__							:   List[Any]					       =		5
	A__							:   List[Any]					       =		10

	if model_type in ["text", "coarse"]:
		A__							:   int					       =		torch.randint(256   ,				(batch_size, sequence_length)   ,				dtype=torch.int )
		A__							:   Union[str, Any]					       =		bark_model(lowercase_ )[0]

		A__							:   Any					       =		model(lowercase_ )

		# take last logits
		A__							:   List[str]					       =		output_new_model_total.logits[:, [-1], :]

	else:
		A__							:   Optional[int]					       =		3
		A__							:   Optional[int]					       =		8
		A__							:   Tuple					       =		torch.randint(256   ,				(batch_size, sequence_length, n_codes_total)   ,				dtype=torch.int )

		A__							:   List[Any]					       =		model(lowercase_   ,				lowercase_ )
		A__							:   Union[str, Any]					       =		bark_model(lowercase_   ,				lowercase_ )

		A__							:   List[Any]					       =		output_new_model_total.logits

	# output difference should come from the difference of self-attention implementation design
	if output_new_model.shape != output_old_model.shape:
		raise ValueError("""initial and new outputs don't have the same shape""" )
	if (output_new_model - output_old_model).abs().max().item() > 1E-3:
		raise ValueError("""initial and new outputs are not equal""" )

	Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
	model.save_pretrained(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Any   ,				lowercase_:			List[Any]   ,				lowercase_:			Optional[int]   ,				lowercase_:			int   ,				lowercase_:			List[str]   ,				)					->				Any:
	A__							:   Tuple					       =		os.path.join(lowercase_   ,				lowercase_ )

	A__							:   Dict					       =		BarkSemanticConfig.from_pretrained(os.path.join(lowercase_   ,				"""config.json""" ) )
	A__							:   List[Any]					       =		BarkCoarseConfig.from_pretrained(os.path.join(lowercase_   ,				"""config.json""" ) )
	A__							:   str					       =		BarkFineConfig.from_pretrained(os.path.join(lowercase_   ,				"""config.json""" ) )
	A__							:   str					       =		EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

	A__							:   str					       =		BarkSemanticModel.from_pretrained(lowercase_ )
	A__							:   List[str]					       =		BarkCoarseModel.from_pretrained(lowercase_ )
	A__							:   Tuple					       =		BarkFineModel.from_pretrained(lowercase_ )
	A__							:   List[Any]					       =		EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

	A__							:   int					       =		BarkConfig.from_sub_model_configs(
	    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )

	A__							:   Any					       =		BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config   ,				coarseAcoustic.generation_config   ,				fineAcoustic.generation_config )

	A__							:   Any					       =		BarkModel(lowercase_ )

	A__							:   str					       =		semantic
	A__							:   Optional[Any]					       =		coarseAcoustic
	A__							:   int					       =		fineAcoustic
	A__							:   Any					       =		codec

	A__							:   str					       =		bark_generation_config

	Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
	bark.save_pretrained(lowercase_   ,				repo_id=lowercase_   ,				push_to_hub=lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[int]  	=      argparse.ArgumentParser()
	# Required parameters

	parser.add_argument('model_type', type=str, help='text, coarse or fine.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

	A_	:			Optional[int]  	=      parser.parse_args()

	load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






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


A_	:			str  	=      logging.get_logger(__name__)

A_	:			Any  	=      {
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''falcon'''
					UpperCAmelCase__:       Any  							=	['''past_key_values''']






					def __init__(			self   ,	A__=6_5024   ,	A__=4544   ,	A__=32   ,	A__=71   ,	A__=1e-5   ,	A__=0.0_2   ,	A__=True   ,	A__=0.0   ,	A__=0.0   ,	A__=None   ,	A__=False   ,	A__=False   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=11   ,	A__=11   ,	**A__   ,	):
						A__							:   Dict					       =		vocab_size
						# Backward compatibility with n_embed kwarg
						A__							:   Union[str, Any]					       =		kwargs.pop("""n_embed"""   ,	A__      )
						A__							:   Optional[Any]					       =		hidden_size if n_embed is None else n_embed
						A__							:   Any					       =		num_hidden_layers
						A__							:   Any					       =		num_attention_heads
						A__							:   Optional[Any]					       =		layer_norm_epsilon
						A__							:   Tuple					       =		initializer_range
						A__							:   Tuple					       =		use_cache
						A__							:   str					       =		hidden_dropout
						A__							:   List[str]					       =		attention_dropout

						A__							:   List[Any]					       =		bos_token_id
						A__							:   Optional[Any]					       =		eos_token_id
						A__							:   Optional[Any]					       =		num_attention_heads if num_kv_heads is None else num_kv_heads
						A__							:   List[str]					       =		alibi
						A__							:   Tuple					       =		new_decoder_architecture
						A__							:   List[str]					       =		multi_query  # Ignored when new_decoder_architecture is True
						A__							:   List[Any]					       =		parallel_attn
						A__							:   int					       =		bias

						super().__init__(bos_token_id=A__   ,	eos_token_id=A__   ,	**A__      )






					@property
					def   __A      (			self      ):
						return self.hidden_size // self.num_attention_heads






					@property
					def   __A      (			self      ):
						return not self.alibi



