


'''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(770)


__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 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Optional[Any]=False				):
   lowerCAmelCase     =			model_type
   if use_small:
      key += "_small"
   return os.path.join(lowerCamelCase	, REMOTE_MODEL_PATHS[key]['file_name']				)





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Union[str, Any]				):
   os.makedirs(lowerCamelCase	, exist_ok=lowerCamelCase				)
   hf_hub_download(repo_id=lowerCamelCase	, filename=lowerCamelCase	, local_dir=lowerCamelCase				)





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

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

   lowerCAmelCase     =			ConfigClass(**checkpoint['model_args']				)
   lowerCAmelCase     =			ModelClass(config=lowerCamelCase				)
   lowerCAmelCase     =			GenerationConfigClass()

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

         lowerCAmelCase     =			state_dict.pop(lowerCamelCase				)

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

   return model





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

   lowerCAmelCase     =			'cpu'  # do conversion on cpu

   lowerCAmelCase     =			_get_ckpt_path(lowerCamelCase	, use_small=lowerCamelCase				)
   lowerCAmelCase     =			_load_model(lowerCamelCase	, lowerCamelCase	, model_type=lowerCamelCase	, use_small=lowerCamelCase				)

   # load bark initial model
   lowerCAmelCase     =			_bark_load_model(lowerCamelCase	, 'cpu'	, model_type=lowerCamelCase	, use_small=lowerCamelCase				)

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

   if model.num_parameters(exclude_embeddings=lowerCamelCase				) != 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
   lowerCAmelCase     =			5
   lowerCAmelCase     =			10

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

      lowerCAmelCase     =			model(lowerCamelCase				)

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

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

      lowerCAmelCase     =			model(lowerCamelCase	, lowerCamelCase				)
      lowerCAmelCase     =			bark_model(lowerCamelCase	, lowerCamelCase				)

      lowerCAmelCase     =			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(lowerCamelCase				).mkdir(exist_ok=lowerCamelCase				)
   model.save_pretrained(lowerCamelCase				)





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     int	, lowerCamelCase	:     str	, ):
   lowerCAmelCase     =			os.path.join(lowerCamelCase	, lowerCamelCase				)

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

   lowerCAmelCase     =			BarkSemanticModel.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			BarkCoarseModel.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			BarkFineModel.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			EncodecModel.from_pretrained('facebook/encodec_24khz'				)

   lowerCAmelCase     =			BarkConfig.from_sub_model_configs(
       lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

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

   lowerCAmelCase     =			BarkModel(lowerCamelCase				)

   lowerCAmelCase     =			semantic
   lowerCAmelCase     =			coarseAcoustic
   lowerCAmelCase     =			fineAcoustic
   lowerCAmelCase     =			codec

   lowerCAmelCase     =			bark_generation_config

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


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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





