



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()
lowercase_													=      logging.get_logger(__name__)

set_seed(7_70)


lowercase_													=      {
    '''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''',
}


lowercase_													=      {
    '''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''',
    },
}

lowercase_													=      os.path.dirname(os.path.abspath(__file__))
lowercase_													=      os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowercase_													=      os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[Any]=False						)	->    List[Any]:
					__lowerCAmelCase						 =model_type
					if use_small:
										key += "_small"
					return os.path.join(lowerCAmelCase__					, REMOTE_MODEL_PATHS[key]["""file_name"""]						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[int]						)	->    List[Any]:
					os.makedirs(lowerCAmelCase__					, exist_ok=lowerCAmelCase__						)
					hf_hub_download(repo_id=lowerCAmelCase__					, filename=lowerCAmelCase__					, local_dir=lowerCAmelCase__						)






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

					# convert Bark model arguments to HF Bark model arguments
					__lowerCAmelCase						 =model_args.pop("""n_head"""						)
					__lowerCAmelCase						 =model_args.pop("""n_embd"""						)
					__lowerCAmelCase						 =model_args.pop("""n_layer"""						)

					__lowerCAmelCase						 =ConfigClass(**checkpoint["""model_args"""]						)
					__lowerCAmelCase						 =ModelClass(config=lowerCAmelCase__						)
					__lowerCAmelCase						 =GenerationConfigClass()

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

															__lowerCAmelCase						 =state_dict.pop(lowerCAmelCase__						)

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

					return model






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str=False					, __lowerCamelCase						:     List[str]="text"						)	->    List[Any]:
					if model_type not in ("text", "coarse", "fine"):
										raise NotImplementedError()

					__lowerCAmelCase						 ="cpu"  # do conversion on cpu

					__lowerCAmelCase						 =_get_ckpt_path(lowerCAmelCase__					, use_small=lowerCAmelCase__						)
					__lowerCAmelCase						 =_load_model(lowerCAmelCase__					, lowerCAmelCase__					, model_type=lowerCAmelCase__					, use_small=lowerCAmelCase__						)

					# load bark initial model
					__lowerCAmelCase						 =_bark_load_model(lowerCAmelCase__					, """cpu"""					, model_type=lowerCAmelCase__					, use_small=lowerCAmelCase__						)

					if model_type == "text":
										__lowerCAmelCase						 =bark_model["model"]

					if model.num_parameters(exclude_embeddings=lowerCAmelCase__						) != 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						 =5
					__lowerCAmelCase						 =10

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

										__lowerCAmelCase						 =model(lowerCAmelCase__						)

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

					else:
										__lowerCAmelCase						 =3
										__lowerCAmelCase						 =8
										__lowerCAmelCase						 =torch.randint(256					, (batch_size, sequence_length, n_codes_total)					, dtype=torch.int						)

										__lowerCAmelCase						 =model(lowerCAmelCase__					, lowerCAmelCase__						)
										__lowerCAmelCase						 =bark_model(lowerCAmelCase__					, lowerCAmelCase__						)

										__lowerCAmelCase						 =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(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
					model.save_pretrained(lowerCAmelCase__						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     List[str]					, )	->    Any:
					__lowerCAmelCase						 =os.path.join(lowerCAmelCase__					, lowerCAmelCase__						)

					__lowerCAmelCase						 =BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase__					, """config.json"""						)						)
					__lowerCAmelCase						 =BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase__					, """config.json"""						)						)
					__lowerCAmelCase						 =BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase__					, """config.json"""						)						)
					__lowerCAmelCase						 =EncodecConfig.from_pretrained("""facebook/encodec_24khz"""						)

					__lowerCAmelCase						 =BarkSemanticModel.from_pretrained(lowerCAmelCase__						)
					__lowerCAmelCase						 =BarkCoarseModel.from_pretrained(lowerCAmelCase__						)
					__lowerCAmelCase						 =BarkFineModel.from_pretrained(lowerCAmelCase__						)
					__lowerCAmelCase						 =EncodecModel.from_pretrained("""facebook/encodec_24khz"""						)

					__lowerCAmelCase						 =BarkConfig.from_sub_model_configs(
					    lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__						)

					__lowerCAmelCase						 =BarkGenerationConfig.from_sub_model_configs(
					    semantic.generation_config					, coarseAcoustic.generation_config					, fineAcoustic.generation_config						)

					__lowerCAmelCase						 =BarkModel(lowerCAmelCase__						)

					__lowerCAmelCase						 =semantic
					__lowerCAmelCase						 =coarseAcoustic
					__lowerCAmelCase						 =fineAcoustic
					__lowerCAmelCase						 =codec

					__lowerCAmelCase						 =bark_generation_config

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


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

				lowercase_													=      parser.parse_args()

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











"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__  :	Tuple		 =			prime_factors(lowerCAmelCase__			)
			if is_square_free(lowerCAmelCase__			):
						return -1 if len(lowerCAmelCase__			) % 2 else 1
			return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()