





'''simple docstring'''






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()
snake_case_     =	logging.get_logger(__name__)

set_seed(7_70)


snake_case_     =	{
    """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""",
}


snake_case_     =	{
    """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""",
    },
}

snake_case_     =	os.path.dirname(os.path.abspath(__file__))
snake_case_     =	os.path.join(os.path.expanduser("""~"""), """.cache""")
snake_case_     =	os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : str=False		)		->  Optional[int]:
    A       :       int							=						model_type
    if use_small:
        key += "_small"
    return os.path.join(UpperCamelCase__    ,       REMOTE_MODEL_PATHS[key]['''file_name''']		)
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Union[str, Any]		)		->  Dict:
    os.makedirs(UpperCamelCase__    ,       exist_ok=UpperCamelCase__		)
    hf_hub_download(repo_id=UpperCamelCase__    ,       filename=UpperCamelCase__    ,       local_dir=UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : Union[str, Any]=False    ,       UpperCamelCase__    : List[str]="text"		)		->  List[str]:
    if model_type == "text":
        A       :       Tuple							=						BarkSemanticModel
        A       :       str							=						BarkSemanticConfig
        A       :       int							=						BarkSemanticGenerationConfig
    elif model_type == "coarse":
        A       :       Any							=						BarkCoarseModel
        A       :       Dict							=						BarkCoarseConfig
        A       :       Tuple							=						BarkCoarseGenerationConfig
    elif model_type == "fine":
        A       :       Optional[Any]							=						BarkFineModel
        A       :       Any							=						BarkFineConfig
        A       :       List[str]							=						BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    A       :       Any							=						F'''{model_type}_small''' if use_small else model_type
    A       :       int							=						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''']		)
    A       :       int							=						torch.load(UpperCamelCase__    ,       map_location=UpperCamelCase__		)
    # this is a hack
    A       :       Dict							=						checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        A       :       Union[str, Any]							=						model_args['''vocab_size''']
        A       :       Tuple							=						model_args['''vocab_size''']
        del model_args["vocab_size"]

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

    A       :       int							=						ConfigClass(**checkpoint['''model_args''']		)
    A       :       List[Any]							=						ModelClass(config=UpperCamelCase__		)
    A       :       Optional[int]							=						GenerationConfigClass()

    A       :       int							=						model_generation_config
    A       :       Any							=						checkpoint['''model''']
    # fixup checkpoint
    A       :       Optional[int]							=						'''_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
            A       :       Dict							=						k[len(UpperCamelCase__		) :]
            for old_layer_name in new_layer_name_dict:
                A       :       List[str]							=						new_k.replace(UpperCamelCase__    ,       new_layer_name_dict[old_layer_name]		)

            A       :       str							=						state_dict.pop(UpperCamelCase__		)

    A       :       List[str]							=						set(state_dict.keys()		) - set(model.state_dict().keys()		)
    A       :       Optional[int]							=						{k for k in extra_keys if not k.endswith('''.attn.bias'''		)}
    A       :       str							=						set(model.state_dict().keys()		) - set(state_dict.keys()		)
    A       :       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__		)
    A       :       Tuple							=						model.num_parameters(exclude_embeddings=UpperCamelCase__		)
    A       :       Optional[int]							=						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 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Optional[int]=False    ,       UpperCamelCase__    : Optional[int]="text"		)		->  Optional[Any]:
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    A       :       Union[str, Any]							=						'''cpu'''  # do conversion on cpu

    A       :       Optional[Any]							=						_get_ckpt_path(UpperCamelCase__    ,       use_small=UpperCamelCase__		)
    A       :       Optional[Any]							=						_load_model(UpperCamelCase__    ,       UpperCamelCase__    ,       model_type=UpperCamelCase__    ,       use_small=UpperCamelCase__		)

    # load bark initial model
    A       :       Tuple							=						_bark_load_model(UpperCamelCase__    ,       '''cpu'''    ,       model_type=UpperCamelCase__    ,       use_small=UpperCamelCase__		)

    if model_type == "text":
        A       :       List[Any]							=						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
    A       :       List[Any]							=						5
    A       :       int							=						10

    if model_type in ["text", "coarse"]:
        A       :       Optional[int]							=						torch.randint(256    ,       (batch_size, sequence_length)    ,       dtype=torch.int		)
        A       :       List[Any]							=						bark_model(UpperCamelCase__		)[0]

        A       :       str							=						model(UpperCamelCase__		)

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

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

        A       :       Optional[Any]							=						model(UpperCamelCase__    ,       UpperCamelCase__		)
        A       :       str							=						bark_model(UpperCamelCase__    ,       UpperCamelCase__		)

        A       :       Dict							=						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 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : Optional[Any]    ,       )		->  Dict:
    A       :       List[Any]							=						os.path.join(UpperCamelCase__    ,       UpperCamelCase__		)

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

    A       :       Tuple							=						BarkSemanticModel.from_pretrained(UpperCamelCase__		)
    A       :       int							=						BarkCoarseModel.from_pretrained(UpperCamelCase__		)
    A       :       Optional[int]							=						BarkFineModel.from_pretrained(UpperCamelCase__		)
    A       :       str							=						EncodecModel.from_pretrained('''facebook/encodec_24khz'''		)

    A       :       List[Any]							=						BarkConfig.from_sub_model_configs(
        UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

    A       :       List[str]							=						BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config    ,       coarseAcoustic.generation_config    ,       fineAcoustic.generation_config		)

    A       :       Tuple							=						BarkModel(UpperCamelCase__		)

    A       :       Dict							=						semantic
    A       :       Any							=						coarseAcoustic
    A       :       int							=						fineAcoustic
    A       :       Optional[Any]							=						codec

    A       :       Optional[Any]							=						bark_generation_config

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


if __name__ == "__main__":
     snake_case_     =	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.""")

     snake_case_     =	parser.parse_args()

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







'''simple docstring'''






import requests

snake_case_     =	"""YOUR API KEY"""
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : str = giphy_api_key		)		->  list:
    A       :       Optional[Any]							=						'''+'''.join(query.split()		)
    A       :       List[Any]							=						F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
    A       :       int							=						requests.get(UpperCamelCase__		).json()['''data''']
    return [gif["url"] for gif in gifs]


if __name__ == "__main__":
     print("""\n""".join(get_gifs("""space ship""")))
