





'''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()
lowerCAmelCase_					:    Dict      	=			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_					:    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',
    },
}

lowerCAmelCase_					:    Union[str, Any]      	=			os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase_					:    Tuple      	=			os.path.join(os.path.expanduser('~'), '.cache')
lowerCAmelCase_					:    Optional[int]      	=			os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Dict=False						)	->	Any:
				_a								= model_type
				if use_small:
								key += "_small"
				return os.path.join(lowercase__    ,							REMOTE_MODEL_PATHS[key]["file_name"]						)






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str						)	->	List[Any]:
				os.makedirs(lowercase__    ,							exist_ok=lowercase__						)
				hf_hub_download(repo_id=lowercase__    ,							filename=lowercase__    ,							local_dir=lowercase__						)






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Tuple    ,							lowercase						:      Union[str, Any]=False    ,							lowercase						:      Any="text"						)	->	Any:
				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(lowercase__						):
								logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.'						)
								_download(model_info["repo_id"]    ,							model_info["file_name"]						)
				_a								= torch.load(lowercase__    ,							map_location=lowercase__						)
				# 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=lowercase__						)
				_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(lowercase__						):
												# replace part of the key with corresponding layer name in HF implementation
												_a								= k[len(lowercase__						) :]
												for old_layer_name in new_layer_name_dict:
																_a								= new_k.replace(lowercase__    ,							new_layer_name_dict[old_layer_name]						)

												_a								= state_dict.pop(lowercase__						)

				_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(lowercase__						) != 0:
								raise ValueError(F'extra keys found: {extra_keys}'						)
				if len(lowercase__						) != 0:
								raise ValueError(F'missing keys: {missing_keys}'						)
				model.load_state_dict(lowercase__    ,							strict=lowercase__						)
				_a								= model.num_parameters(exclude_embeddings=lowercase__						)
				_a								= checkpoint['''best_val_loss'''].item()
				logger.info(F'model loaded: {round(n_params/1E6    ,							1						)}M params, {round(lowercase__    ,							3						)} loss'						)
				model.eval()
				model.to(lowercase__						)
				del checkpoint, state_dict

				return model






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

				_a								= '''cpu'''  # do conversion on cpu

				_a								= _get_ckpt_path(lowercase__    ,							use_small=lowercase__						)
				_a								= _load_model(lowercase__    ,							lowercase__    ,							model_type=lowercase__    ,							use_small=lowercase__						)

				# load bark initial model
				_a								= _bark_load_model(lowercase__    ,							"cpu"    ,							model_type=lowercase__    ,							use_small=lowercase__						)

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

				if model.num_parameters(exclude_embeddings=lowercase__						) != 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(lowercase__						)[0]

								_a								= model(lowercase__						)

								# 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(lowercase__    ,							lowercase__						)
								_a								= bark_model(lowercase__    ,							lowercase__						)

								_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(lowercase__						).mkdir(exist_ok=lowercase__						)
				model.save_pretrained(lowercase__						)






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Any    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Union[str, Any]    ,							)	->	Optional[int]:
				_a								= os.path.join(lowercase__    ,							lowercase__						)

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

				_a								= BarkSemanticModel.from_pretrained(lowercase__						)
				_a								= BarkCoarseModel.from_pretrained(lowercase__						)
				_a								= BarkFineModel.from_pretrained(lowercase__						)
				_a								= EncodecModel.from_pretrained("facebook/encodec_24khz"						)

				_a								= BarkConfig.from_sub_model_configs(
				    lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__						)

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

				_a								= BarkModel(lowercase__						)

				_a								= semantic
				_a								= coarseAcoustic
				_a								= fineAcoustic
				_a								= codec

				_a								= bark_generation_config

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


if __name__ == "__main__":
					lowerCAmelCase_					:    Union[str, Any]      	=			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_					:    List[Any]      	=			parser.parse_args()

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



def 		_lowercase (     lowercase__						):
      if not all(x.isalpha() for x in string						):
            raise ValueError('''String must only contain alphabetic characters.'''						)

      __lowerCAmelCase					:						int					=					sorted(string.lower()						)
      return len(lowercase__						) == len(set(lowercase__						)						)


if __name__ == "__main__":
 _UpperCamelCase 			=		input("Enter a string ").strip()

 _UpperCamelCase 			=		is_isogram(input_str)
 print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")


