


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_70)


__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    snake_case_     (  snake_case			,							snake_case=False     )  ->				Any:
       lowercase__:   Optional[Any]  			=   model_type
       if use_small:
              key += "_small"
       return os.path.join(snake_case			,							REMOTE_MODEL_PATHS[key]['file_name']     )




def    snake_case_     (  snake_case			,							snake_case     )  ->				str:
       os.makedirs(snake_case			,							exist_ok=snake_case     )
       hf_hub_download(repo_id=snake_case			,							filename=snake_case			,							local_dir=snake_case     )




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

       # convert Bark model arguments to HF Bark model arguments
       lowercase__:   Any  			=   model_args.pop('n_head'     )
       lowercase__:   List[str]  			=   model_args.pop('n_embd'     )
       lowercase__:   Optional[Any]  			=   model_args.pop('n_layer'     )

       lowercase__:   List[str]  			=   ConfigClass(**checkpoint['model_args']     )
       lowercase__:   Union[str, Any]  			=   ModelClass(config=snake_case     )
       lowercase__:   List[str]  			=   GenerationConfigClass()

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

                     lowercase__:   Any  			=   state_dict.pop(snake_case     )

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

       return model




def    snake_case_     (  snake_case			,							snake_case=False			,							snake_case="text"     )  ->				str:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       lowercase__:   Optional[int]  			=   'cpu'  # do conversion on cpu

       lowercase__:   str  			=   _get_ckpt_path(snake_case			,							use_small=snake_case     )
       lowercase__:   Optional[Any]  			=   _load_model(snake_case			,							snake_case			,							model_type=snake_case			,							use_small=snake_case     )

       # load bark initial model
       lowercase__:   Dict  			=   _bark_load_model(snake_case			,							'cpu'			,							model_type=snake_case			,							use_small=snake_case     )

       if model_type == "text":
              lowercase__:   str  			=   bark_model['model']

       if model.num_parameters(exclude_embeddings=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
       lowercase__:   Any  			=   5
       lowercase__:   int  			=   10

       if model_type in ["text", "coarse"]:
              lowercase__:   int  			=   torch.randint(2_56			,							(batch_size, sequence_length)			,							dtype=torch.int     )
              lowercase__:   Optional[Any]  			=   bark_model(snake_case     )[0]

              lowercase__:   str  			=   model(snake_case     )

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

       else:
              lowercase__:   str  			=   3
              lowercase__:   Any  			=   8
              lowercase__:   Optional[int]  			=   torch.randint(2_56			,							(batch_size, sequence_length, n_codes_total)			,							dtype=torch.int     )

              lowercase__:   Optional[int]  			=   model(snake_case			,							snake_case     )
              lowercase__:   Optional[Any]  			=   bark_model(snake_case			,							snake_case     )

              lowercase__:   List[Any]  			=   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(snake_case     ).mkdir(exist_ok=snake_case     )
       model.save_pretrained(snake_case     )




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							)  ->				Optional[int]:
       lowercase__:   Optional[Any]  			=   os.path.join(snake_case			,							snake_case     )

       lowercase__:   List[Any]  			=   BarkSemanticConfig.from_pretrained(os.path.join(snake_case			,							'config.json'     )     )
       lowercase__:   List[Any]  			=   BarkCoarseConfig.from_pretrained(os.path.join(snake_case			,							'config.json'     )     )
       lowercase__:   Tuple  			=   BarkFineConfig.from_pretrained(os.path.join(snake_case			,							'config.json'     )     )
       lowercase__:   Optional[Any]  			=   EncodecConfig.from_pretrained('facebook/encodec_24khz'     )

       lowercase__:   Any  			=   BarkSemanticModel.from_pretrained(snake_case     )
       lowercase__:   Union[str, Any]  			=   BarkCoarseModel.from_pretrained(snake_case     )
       lowercase__:   Any  			=   BarkFineModel.from_pretrained(snake_case     )
       lowercase__:   Tuple  			=   EncodecModel.from_pretrained('facebook/encodec_24khz'     )

       lowercase__:   Tuple  			=   BarkConfig.from_sub_model_configs(
           snake_case			,							snake_case			,							snake_case			,							snake_case     )

       lowercase__:   List[Any]  			=   BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config			,							coarseAcoustic.generation_config			,							fineAcoustic.generation_config     )

       lowercase__:   List[str]  			=   BarkModel(snake_case     )

       lowercase__:   Any  			=   semantic
       lowercase__:   List[str]  			=   coarseAcoustic
       lowercase__:   Dict  			=   fineAcoustic
       lowercase__:   Union[str, Any]  			=   codec

       lowercase__:   Any  			=   bark_generation_config

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


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)






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase		       =							list[list[float | int]]




def    snake_case_     (  snake_case			,							snake_case     )  ->				Matrix:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(size + 1     )] for _ in range(snake_case     )]
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   float

       for row in range(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[Any]  			=   matrix[row][col]

              lowercase__:   Optional[int]  			=   vector[row][0]

       lowercase__:   str  			=   0
       lowercase__:   Any  			=   0
       while row < size and col < size:
              # pivoting
              lowercase__:   List[Any]  			=   max((abs(augmented[rowa][col]     ), rowa) for rowa in range(snake_case			,							snake_case     )     )[
                  1
              ]
              if augmented[pivot_row][col] == 0:
                     col += 1
                     continue
              else:
                     lowercase__			,						lowercase__:   Optional[int]  			=   augmented[pivot_row], augmented[row]

              for rowa in range(row + 1			,							snake_case     ):
                     lowercase__:   Any  			=   augmented[rowa][col] / augmented[row][col]
                     lowercase__:   int  			=   0
                     for cola in range(col + 1			,							size + 1     ):
                            augmented[rowa][cola] -= augmented[row][cola] * ratio

              row += 1
              col += 1

       # back substitution
       for col in range(1			,							snake_case     ):
              for row in range(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   augmented[row][col] / augmented[col][col]
                     for cola in range(snake_case			,							size + 1     ):
                            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
       return [
           [round(augmented[row][size] / augmented[row][row]			,							10     )] for row in range(snake_case     )
       ]




def    snake_case_     (  snake_case     )  ->				Callable[[int], int]:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(snake_case     )] for _ in range(snake_case     )]
       lowercase__:   Matrix  			=   [[0] for _ in range(snake_case     )]
       lowercase__:   Matrix
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int

       for x_val, y_val in enumerate(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[str]  			=   (x_val + 1) ** (size - col - 1)
              lowercase__:   str  			=   y_val

       lowercase__:   Optional[int]  			=   solve(snake_case			,							snake_case     )

       def interpolated_func(snake_case     ) -> int:
              return sum(
                  round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
                  for x_val in range(snake_case     )     )

       return interpolated_func




def    snake_case_     (  snake_case     )  ->				int:
       return (
           1
           - variable
           + variable**2
           - variable**3
           + variable**4
           - variable**5
           + variable**6
           - variable**7
           + variable**8
           - variable**9
           + variable**10
       )




def    snake_case_     (  snake_case = question_function			,							snake_case = 10     )  ->				int:
       lowercase__:   list[int]  			=   [func(snake_case     ) for x_val in range(1			,							order + 1     )]

       lowercase__:   list[Callable[[int], int]]  			=   [
           interpolate(data_points[:max_coeff]     ) for max_coeff in range(1			,							order + 1     )
       ]

       lowercase__:   int  			=   0
       lowercase__:   Callable[[int], int]
       lowercase__:   int

       for poly in polynomials:
              lowercase__:   List[str]  			=   1
              while func(snake_case     ) == poly(snake_case     ):
                     x_val += 1

              ret += poly(snake_case     )

       return ret


if __name__ == "__main__":
 print(F'''{solution() = }''')


