





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							:			int         =			logging.get_logger(__name__)

set_seed(770)


__A							:			Dict         =			{
    "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							:			str         =			{
    "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							:			Optional[int]         =			os.path.dirname(os.path.abspath(__file__))
__A							:			int         =			os.path.join(os.path.expanduser("~"), ".cache")
__A							:			Union[str, Any]         =			os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> Dict:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False							,     _SCREAMING_SNAKE_CASE="text"				)							-> int:




    """simple docstring"""




    if model_type == "text":
        _A     =							BarkSemanticModel
        _A     =							BarkSemanticConfig
        _A     =							BarkSemanticGenerationConfig
    elif model_type == "coarse":
        _A     =							BarkCoarseModel
        _A     =							BarkCoarseConfig
        _A     =							BarkCoarseGenerationConfig
    elif model_type == "fine":
        _A     =							BarkFineModel
        _A     =							BarkFineConfig
        _A     =							BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    _A     =							F"{model_type}_small" if use_small else model_type
    _A     =							REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(_SCREAMING_SNAKE_CASE				):
        logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."				)
        _download(model_info['repo_id']							,     model_info['file_name']				)
    _A     =							torch.load(_SCREAMING_SNAKE_CASE							,     map_location=_SCREAMING_SNAKE_CASE				)
    # this is a hack
    _A     =							checkpoint['model_args']
    if "input_vocab_size" not in model_args:
        _A     =							model_args['vocab_size']
        _A     =							model_args['vocab_size']
        del model_args["vocab_size"]

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

    _A     =							ConfigClass(**checkpoint['model_args']				)
    _A     =							ModelClass(config=_SCREAMING_SNAKE_CASE				)
    _A     =							GenerationConfigClass()

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

            _A     =							state_dict.pop(_SCREAMING_SNAKE_CASE				)

    _A     =							set(state_dict.keys()				) - set(model.state_dict().keys()				)
    _A     =							{k for k in extra_keys if not k.endswith('.attn.bias'				)}
    _A     =							set(model.state_dict().keys()				) - set(state_dict.keys()				)
    _A     =							{k for k in missing_keys if not k.endswith('.attn.bias'				)}
    if len(_SCREAMING_SNAKE_CASE				) != 0:
        raise ValueError(F"extra keys found: {extra_keys}"				)
    if len(_SCREAMING_SNAKE_CASE				) != 0:
        raise ValueError(F"missing keys: {missing_keys}"				)
    model.load_state_dict(_SCREAMING_SNAKE_CASE							,     strict=_SCREAMING_SNAKE_CASE				)
    _A     =							model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE				)
    _A     =							checkpoint['best_val_loss'].item()
    logger.info(F"model loaded: {round(n_params/1e6							,     1				)}M params, {round(_SCREAMING_SNAKE_CASE							,     3				)} loss"				)
    model.eval()
    model.to(_SCREAMING_SNAKE_CASE				)
    del checkpoint, state_dict

    return model





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False							,     _SCREAMING_SNAKE_CASE="text"				)							-> List[str]:




    """simple docstring"""




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

    _A     =							'cpu'  # do conversion on cpu

    _A     =							_get_ckpt_path(_SCREAMING_SNAKE_CASE							,     use_small=_SCREAMING_SNAKE_CASE				)
    _A     =							_load_model(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     model_type=_SCREAMING_SNAKE_CASE							,     use_small=_SCREAMING_SNAKE_CASE				)

    # load bark initial model
    _A     =							_bark_load_model(_SCREAMING_SNAKE_CASE							,     'cpu'							,     model_type=_SCREAMING_SNAKE_CASE							,     use_small=_SCREAMING_SNAKE_CASE				)

    if model_type == "text":
        _A     =							bark_model['model']

    if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE				) != 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     =							5
    _A     =							10

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

        _A     =							model(_SCREAMING_SNAKE_CASE				)

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

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

        _A     =							model(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
        _A     =							bark_model(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

        _A     =							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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
    model.save_pretrained(_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     )							-> Dict:




    """simple docstring"""




    _A     =							os.path.join(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

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

    _A     =							BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
    _A     =							BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
    _A     =							BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
    _A     =							EncodecModel.from_pretrained('facebook/encodec_24khz'				)

    _A     =							BarkConfig.from_sub_model_configs(
        _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

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

    _A     =							BarkModel(_SCREAMING_SNAKE_CASE				)

    _A     =							semantic
    _A     =							coarseAcoustic
    _A     =							fineAcoustic
    _A     =							codec

    _A     =							bark_generation_config

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


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)












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    print('\nThe shortest path matrix using Floyd Warshall algorithm\n'				)
    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            if dist[i][j] != float('inf'				):
                print(int(dist[i][j]				)							,     end='\t'				)
            else:
                print('INF'							,     end='\t'				)
        print()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							[[float('inf'				) for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]

    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            _A     =							graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(_SCREAMING_SNAKE_CASE				):
        # looping through rows of graph array
        for i in range(_SCREAMING_SNAKE_CASE				):
            # looping through columns of graph array
            for j in range(_SCREAMING_SNAKE_CASE				):
                if (
                    dist[i][k] != float('inf'				)
                    and dist[k][j] != float('inf'				)
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    _A     =							dist[i][k] + dist[k][j]

    _print_dist(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return dist, v


if __name__ == "__main__":
 __A							:			Dict         =			int(input("Enter number of vertices: "))
 __A							:			Union[str, Any]         =			int(input("Enter number of edges: "))

 __A							:			List[str]         =			[[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  __A							:			List[Any]         =			0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  __A							:			Union[str, Any]         =			int(input("Enter source:"))
  __A							:			List[str]         =			int(input("Enter destination:"))
  __A							:			Union[str, Any]         =			float(input("Enter weight:"))
  __A							:			Any         =			weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0





