

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      				=     logging.get_logger(__name__)

set_seed(7_7_0)


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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							model_type
					if use_small:
										key += "_small"
					return os.path.join(UpperCAmelCase__		,					REMOTE_MODEL_PATHS[key]['file_name']      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					os.makedirs(UpperCAmelCase__		,					exist_ok=UpperCAmelCase__      )
					hf_hub_download(repo_id=UpperCAmelCase__		,					filename=UpperCAmelCase__		,					local_dir=UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=False		,					UpperCAmelCase__="text"      ):

					"""simple docstring"""






					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(UpperCAmelCase__      ):
										logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""      )
										_download(model_info['repo_id']		,					model_info['file_name']      )
					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location=UpperCAmelCase__      )
					# 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=UpperCAmelCase__      )
					__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(UpperCAmelCase__      ):
															# replace part of the key with corresponding layer name in HF implementation
															__lowerCAmelCase					=							k[len(UpperCAmelCase__      ) :]
															for old_layer_name in new_layer_name_dict:
																				__lowerCAmelCase					=							new_k.replace(UpperCAmelCase__		,					new_layer_name_dict[old_layer_name]      )

															__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )

					__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(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					=							model.num_parameters(exclude_embeddings=UpperCAmelCase__      )
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=False		,					UpperCAmelCase__="text"      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							"""cpu"""  # do conversion on cpu

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

					# load bark initial model
					__lowerCAmelCase					=							_bark_load_model(UpperCAmelCase__		,					'cpu'		,					model_type=UpperCAmelCase__		,					use_small=UpperCAmelCase__      )

					if model_type == "text":
										__lowerCAmelCase					=							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					=							5
					__lowerCAmelCase					=							10

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

										__lowerCAmelCase					=							model(UpperCAmelCase__      )

										# 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(UpperCAmelCase__		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							bark_model(UpperCAmelCase__		,					UpperCAmelCase__      )

										__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(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
					model.save_pretrained(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							os.path.join(UpperCAmelCase__		,					UpperCAmelCase__      )

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

					__lowerCAmelCase					=							BarkSemanticModel.from_pretrained(UpperCAmelCase__      )
					__lowerCAmelCase					=							BarkCoarseModel.from_pretrained(UpperCAmelCase__      )
					__lowerCAmelCase					=							BarkFineModel.from_pretrained(UpperCAmelCase__      )
					__lowerCAmelCase					=							EncodecModel.from_pretrained('facebook/encodec_24khz'      )

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

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

					__lowerCAmelCase					=							BarkModel(UpperCAmelCase__      )

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

					__lowerCAmelCase					=							bark_generation_config

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


if __name__ == "__main__":
		lowerCamelCase      				=     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      				=     parser.parse_args()

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









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






