


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()
lowerCamelCase : Any						   =       logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase : 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',
}


lowerCamelCase : Optional[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',
    },
}

lowerCamelCase : Tuple						   =       os.path.dirname(os.path.abspath(__file__))
lowerCamelCase : List[str]						   =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCamelCase : Tuple						   =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Union[str, Any]=False      ):
							__lowercase      :							Optional[Any]				=     model_type
							if use_small:
														key += "_small"
							return os.path.join(__UpperCamelCase       ,       REMOTE_MODEL_PATHS[key]["""file_name"""]      )
def       snake_case_		(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Dict      ):
							os.makedirs(__UpperCamelCase       ,       exist_ok=__UpperCamelCase      )
							hf_hub_download(repo_id=__UpperCamelCase       ,       filename=__UpperCamelCase       ,       local_dir=__UpperCamelCase      )
def       snake_case_		(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						int=False       ,       lowerCAmelCase_       :						Optional[int]="text"      ):







							if model_type == "text":
														__lowercase      :							Optional[int]				=     BarkSemanticModel
														__lowercase      :							str				=     BarkSemanticConfig
														__lowercase      :							int				=     BarkSemanticGenerationConfig
							elif model_type == "coarse":
														__lowercase      :							Optional[int]				=     BarkCoarseModel
														__lowercase      :							Tuple				=     BarkCoarseConfig
														__lowercase      :							Optional[int]				=     BarkCoarseGenerationConfig
							elif model_type == "fine":
														__lowercase      :							str				=     BarkFineModel
														__lowercase      :							List[Any]				=     BarkFineConfig
														__lowercase      :							Optional[Any]				=     BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							__lowercase      :							Optional[Any]				=     F"{model_type}_small" if use_small else model_type
							__lowercase      :							str				=     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"""]      )
							__lowercase      :							int				=     torch.load(__UpperCamelCase       ,       map_location=__UpperCamelCase      )
							# this is a hack
							__lowercase      :							Union[str, Any]				=     checkpoint['''model_args''']
							if "input_vocab_size" not in model_args:
														__lowercase      :							Any				=     model_args['''vocab_size''']
														__lowercase      :							int				=     model_args['''vocab_size''']
														del model_args["vocab_size"]

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

							__lowercase      :							Any				=     ConfigClass(**checkpoint["""model_args"""]      )
							__lowercase      :							Tuple				=     ModelClass(config=__UpperCamelCase      )
							__lowercase      :							Any				=     GenerationConfigClass()

							__lowercase      :							Optional[int]				=     model_generation_config
							__lowercase      :							Union[str, Any]				=     checkpoint['''model''']
							# fixup checkpoint
							__lowercase      :							List[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
																					__lowercase      :							List[Any]				=     k[len(__UpperCamelCase      ) :]
																					for old_layer_name in new_layer_name_dict:
																												__lowercase      :							List[Any]				=     new_k.replace(__UpperCamelCase       ,       new_layer_name_dict[old_layer_name]      )

																					__lowercase      :							Optional[int]				=     state_dict.pop(__UpperCamelCase      )

							__lowercase      :							List[str]				=     set(state_dict.keys()      ) - set(model.state_dict().keys()      )
							__lowercase      :							Optional[int]				=     {k for k in extra_keys if not k.endswith(""".attn.bias"""      )}
							__lowercase      :							Optional[int]				=     set(model.state_dict().keys()      ) - set(state_dict.keys()      )
							__lowercase      :							Any				=     {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      )
							__lowercase      :							Optional[Any]				=     model.num_parameters(exclude_embeddings=__UpperCamelCase      )
							__lowercase      :							str				=     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       snake_case_		(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[Any]=False       ,       lowerCAmelCase_       :						Tuple="text"      ):







							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							__lowercase      :							str				=     '''cpu'''  # do conversion on cpu

							__lowercase      :							int				=     _get_ckpt_path(__UpperCamelCase       ,       use_small=__UpperCamelCase      )
							__lowercase      :							Optional[int]				=     _load_model(__UpperCamelCase       ,       __UpperCamelCase       ,       model_type=__UpperCamelCase       ,       use_small=__UpperCamelCase      )

							# load bark initial model
							__lowercase      :							List[Any]				=     _bark_load_model(__UpperCamelCase       ,       """cpu"""       ,       model_type=__UpperCamelCase       ,       use_small=__UpperCamelCase      )

							if model_type == "text":
														__lowercase      :							Optional[int]				=     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
							__lowercase      :							Optional[Any]				=     5
							__lowercase      :							Dict				=     10

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

														__lowercase      :							int				=     model(__UpperCamelCase      )

														# take last logits
														__lowercase      :							int				=     output_new_model_total.logits[:, [-1], :]

							else:
														__lowercase      :							str				=     3
														__lowercase      :							List[str]				=     8
														__lowercase      :							List[Any]				=     torch.randint(256       ,       (batch_size, sequence_length, n_codes_total)       ,       dtype=torch.int      )

														__lowercase      :							List[Any]				=     model(__UpperCamelCase       ,       __UpperCamelCase      )
														__lowercase      :							Union[str, Any]				=     bark_model(__UpperCamelCase       ,       __UpperCamelCase      )

														__lowercase      :							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       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[int]       ,       ):
							__lowercase      :							int				=     os.path.join(__UpperCamelCase       ,       __UpperCamelCase      )

							__lowercase      :							List[str]				=     BarkSemanticConfig.from_pretrained(os.path.join(__UpperCamelCase       ,       """config.json"""      )      )
							__lowercase      :							Optional[Any]				=     BarkCoarseConfig.from_pretrained(os.path.join(__UpperCamelCase       ,       """config.json"""      )      )
							__lowercase      :							str				=     BarkFineConfig.from_pretrained(os.path.join(__UpperCamelCase       ,       """config.json"""      )      )
							__lowercase      :							int				=     EncodecConfig.from_pretrained("""facebook/encodec_24khz"""      )

							__lowercase      :							Dict				=     BarkSemanticModel.from_pretrained(__UpperCamelCase      )
							__lowercase      :							str				=     BarkCoarseModel.from_pretrained(__UpperCamelCase      )
							__lowercase      :							Optional[int]				=     BarkFineModel.from_pretrained(__UpperCamelCase      )
							__lowercase      :							int				=     EncodecModel.from_pretrained("""facebook/encodec_24khz"""      )

							__lowercase      :							str				=     BarkConfig.from_sub_model_configs(
							    __UpperCamelCase       ,       __UpperCamelCase       ,       __UpperCamelCase       ,       __UpperCamelCase      )

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

							__lowercase      :							List[Any]				=     BarkModel(__UpperCamelCase      )

							__lowercase      :							List[Any]				=     semantic
							__lowercase      :							Tuple				=     coarseAcoustic
							__lowercase      :							Union[str, Any]				=     fineAcoustic
							__lowercase      :							Any				=     codec

							__lowercase      :							Union[str, Any]				=     bark_generation_config

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


if __name__ == "__main__":
	lowerCamelCase : 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.''')

	lowerCamelCase : Tuple						   =       parser.parse_args()

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : List[str]						   =       ['''FunnelTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase : Any						   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)