

'''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()
UpperCAmelCase       =     logging.get_logger(__name__)

set_seed(770)


UpperCAmelCase       =     {
    '''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''',
}


UpperCAmelCase       =     {
    '''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''',
    },
}

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

def  __UpperCamelCase							(		lowercase__  :     List[Any],    lowercase__  :     Optional[int]=False       ):






     '''simple docstring'''





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

def  __UpperCamelCase							(		lowercase__  :     Union[str, Any],    lowercase__  :     List[Any]       ):






     '''simple docstring'''





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

def  __UpperCamelCase							(		lowercase__  :     int,    lowercase__  :     Optional[int],    lowercase__  :     int=False,    lowercase__  :     List[Any]="text"       ):






     '''simple docstring'''





     if model_type == "text":
          __lowercase							=BarkSemanticModel
          __lowercase							=BarkSemanticConfig
          __lowercase							=BarkSemanticGenerationConfig
     elif model_type == "coarse":
          __lowercase							=BarkCoarseModel
          __lowercase							=BarkCoarseConfig
          __lowercase							=BarkCoarseGenerationConfig
     elif model_type == "fine":
          __lowercase							=BarkFineModel
          __lowercase							=BarkFineConfig
          __lowercase							=BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     __lowercase							=F'''{model_type}_small''' if use_small else model_type
     __lowercase							=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']       )
     __lowercase							=torch.load(UpperCamelCase_,    map_location=UpperCamelCase_       )
     # this is a hack
     __lowercase							=checkpoint['model_args']
     if "input_vocab_size" not in model_args:
          __lowercase							=model_args['vocab_size']
          __lowercase							=model_args['vocab_size']
          del model_args["vocab_size"]

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

     __lowercase							=ConfigClass(**checkpoint['model_args']       )
     __lowercase							=ModelClass(config=UpperCamelCase_       )
     __lowercase							=GenerationConfigClass()

     __lowercase							=model_generation_config
     __lowercase							=checkpoint['model']
     # fixup checkpoint
     __lowercase							='_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
               __lowercase							=k[len(UpperCamelCase_       ) :]
               for old_layer_name in new_layer_name_dict:
                    __lowercase							=new_k.replace(UpperCamelCase_,    new_layer_name_dict[old_layer_name]       )

               __lowercase							=state_dict.pop(UpperCamelCase_       )

     __lowercase							=set(state_dict.keys()       ) - set(model.state_dict().keys()       )
     __lowercase							={k for k in extra_keys if not k.endswith('.attn.bias'       )}
     __lowercase							=set(model.state_dict().keys()       ) - set(state_dict.keys()       )
     __lowercase							={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_       )
     __lowercase							=model.num_parameters(exclude_embeddings=UpperCamelCase_       )
     __lowercase							=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  __UpperCamelCase							(		lowercase__  :     Dict,    lowercase__  :     Dict=False,    lowercase__  :     int="text"       ):






     '''simple docstring'''





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

     __lowercase							='cpu'  # do conversion on cpu

     __lowercase							=_get_ckpt_path(UpperCamelCase_,    use_small=UpperCamelCase_       )
     __lowercase							=_load_model(UpperCamelCase_,    UpperCamelCase_,    model_type=UpperCamelCase_,    use_small=UpperCamelCase_       )

     # load bark initial model
     __lowercase							=_bark_load_model(UpperCamelCase_,    'cpu',    model_type=UpperCamelCase_,    use_small=UpperCamelCase_       )

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

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

          __lowercase							=model(UpperCamelCase_       )

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

     else:
          __lowercase							=3
          __lowercase							=8
          __lowercase							=torch.randint(2_56,    (batch_size, sequence_length, n_codes_total),    dtype=torch.int       )

          __lowercase							=model(UpperCamelCase_,    UpperCamelCase_       )
          __lowercase							=bark_model(UpperCamelCase_,    UpperCamelCase_       )

          __lowercase							=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  __UpperCamelCase							(		lowercase__  :     List[Any],    lowercase__  :     Dict,    lowercase__  :     int,    lowercase__  :     int,    lowercase__  :     Optional[Any],    lowercase__  :     Optional[Any],    ):






     '''simple docstring'''





     __lowercase							=os.path.join(UpperCamelCase_,    UpperCamelCase_       )

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

     __lowercase							=BarkSemanticModel.from_pretrained(UpperCamelCase_       )
     __lowercase							=BarkCoarseModel.from_pretrained(UpperCamelCase_       )
     __lowercase							=BarkFineModel.from_pretrained(UpperCamelCase_       )
     __lowercase							=EncodecModel.from_pretrained('facebook/encodec_24khz'       )

     __lowercase							=BarkConfig.from_sub_model_configs(
         UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_       )

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

     __lowercase							=BarkModel(UpperCamelCase_       )

     __lowercase							=semantic
     __lowercase							=coarseAcoustic
     __lowercase							=fineAcoustic
     __lowercase							=codec

     __lowercase							=bark_generation_config

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


if __name__ == "__main__":
       UpperCAmelCase       =     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.''')

       UpperCAmelCase       =     parser.parse_args()

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




"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


if __name__ == "__main__":
							from doctest import testmod
							from timeit import timeit

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




