






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[Any]					  ={
    '''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			:    Union[str, Any]					  ={
    '''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			:    Any					  =os.path.dirname(os.path.abspath(__file__))
_A			:    Tuple					  =os.path.join(os.path.expanduser('''~'''), '''.cache''')
_A			:    Dict					  =os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Optional[Any]:
							lowerCamelCase__      :			List[str] 						= model_type
							if use_small:
														key += "_small"
							return os.path.join(UpperCamelCase       ,    REMOTE_MODEL_PATHS[key]["""file_name"""]   )





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





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

							# convert Bark model arguments to HF Bark model arguments
							lowerCamelCase__      :			Optional[Any] 						= model_args.pop("""n_head"""   )
							lowerCamelCase__      :			List[Any] 						= model_args.pop("""n_embd"""   )
							lowerCamelCase__      :			Tuple 						= model_args.pop("""n_layer"""   )

							lowerCamelCase__      :			str 						= ConfigClass(**checkpoint["""model_args"""]   )
							lowerCamelCase__      :			Dict 						= ModelClass(config=UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= GenerationConfigClass()

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

																					lowerCamelCase__      :			List[str] 						= state_dict.pop(UpperCamelCase   )

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

							return model





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False       ,    UpperCamelCase="text"   )   -> Any:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							lowerCamelCase__      :			List[str] 						= """cpu"""  # do conversion on cpu

							lowerCamelCase__      :			Optional[int] 						= _get_ckpt_path(UpperCamelCase       ,    use_small=UpperCamelCase   )
							lowerCamelCase__      :			Any 						= _load_model(UpperCamelCase       ,    UpperCamelCase       ,    model_type=UpperCamelCase       ,    use_small=UpperCamelCase   )

							# load bark initial model
							lowerCamelCase__      :			List[str] 						= _bark_load_model(UpperCamelCase       ,    """cpu"""       ,    model_type=UpperCamelCase       ,    use_small=UpperCamelCase   )

							if model_type == "text":
														lowerCamelCase__      :			Tuple 						= bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=UpperCamelCase   ) != 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
							lowerCamelCase__      :			Union[str, Any] 						= 5
							lowerCamelCase__      :			List[Any] 						= 10

							if model_type in ["text", "coarse"]:
														lowerCamelCase__      :			Optional[int] 						= torch.randint(256       ,    (batch_size, sequence_length)       ,    dtype=torch.int   )
														lowerCamelCase__      :			Tuple 						= bark_model(UpperCamelCase   )[0]

														lowerCamelCase__      :			List[str] 						= model(UpperCamelCase   )

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

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

														lowerCamelCase__      :			Dict 						= model(UpperCamelCase       ,    UpperCamelCase   )
														lowerCamelCase__      :			List[str] 						= bark_model(UpperCamelCase       ,    UpperCamelCase   )

														lowerCamelCase__      :			Optional[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(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							model.save_pretrained(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> Dict:
							lowerCamelCase__      :			List[str] 						= os.path.join(UpperCamelCase       ,    UpperCamelCase   )

							lowerCamelCase__      :			int 						= BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			Union[str, Any] 						= BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			int 						= BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			Tuple 						= EncodecConfig.from_pretrained("""facebook/encodec_24khz"""   )

							lowerCamelCase__      :			Any 						= BarkSemanticModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= BarkCoarseModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			str 						= BarkFineModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			Optional[Any] 						= EncodecModel.from_pretrained("""facebook/encodec_24khz"""   )

							lowerCamelCase__      :			int 						= BarkConfig.from_sub_model_configs(
							    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							lowerCamelCase__      :			Tuple 						= BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config       ,    coarseAcoustic.generation_config       ,    fineAcoustic.generation_config   )

							lowerCamelCase__      :			Any 						= BarkModel(UpperCamelCase   )

							lowerCamelCase__      :			List[Any] 						= semantic
							lowerCamelCase__      :			List[Any] 						= coarseAcoustic
							lowerCamelCase__      :			str 						= fineAcoustic
							lowerCamelCase__      :			List[str] 						= codec

							lowerCamelCase__      :			Tuple 						= bark_generation_config

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


if __name__ == "__main__":
			_A			:    List[Any]					  =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			:    Dict					  =parser.parse_args()

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









'''simple docstring'''







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


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

_A			:    List[str]					  ={
    '''MIT/ast-finetuned-audioset-10-10-0.4593''': (
        '''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
    ),
}

class _lowercase			(       _lowercase	):
							a          = """audio-spectrogram-transformer"""




							def __init__(							self:       str    ,							UpperCamelCase__:       Any=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       List[Any]=12    ,							UpperCamelCase__:       int=3_072    ,							UpperCamelCase__:       Optional[Any]="gelu"    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       Union[str, Any]=0.02    ,							UpperCamelCase__:       Dict=1e-12    ,							UpperCamelCase__:       List[str]=16    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       List[str]=10    ,							UpperCamelCase__:       Any=1_024    ,							UpperCamelCase__:       Optional[Any]=128    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
														super().__init__(**UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			List[str] 						= num_attention_heads
														lowerCamelCase__      :			Optional[int] 						= intermediate_size
														lowerCamelCase__      :			List[Any] 						= hidden_act
														lowerCamelCase__      :			List[Any] 						= hidden_dropout_prob
														lowerCamelCase__      :			str 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[str] 						= layer_norm_eps
														lowerCamelCase__      :			List[Any] 						= patch_size
														lowerCamelCase__      :			List[str] 						= qkv_bias
														lowerCamelCase__      :			Dict 						= frequency_stride
														lowerCamelCase__      :			List[Any] 						= time_stride
														lowerCamelCase__      :			str 						= max_length
														lowerCamelCase__      :			Dict 						= num_mel_bins

