






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	:		str										=   logging.get_logger(__name__)

set_seed(7_70)


__lowerCamelCase	:		List[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	:		Union[str, Any]										=   {
    """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	:		Optional[Any]										=   os.path.dirname(os.path.abspath(__file__))
__lowerCamelCase	:		Optional[int]										=   os.path.join(os.path.expanduser("""~"""), """.cache""")
__lowerCamelCase	:		List[Any]										=   os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def 			A__							(      _a  :					Union[str, Any]		,  _a  :					List[str]=False	):





 '''simple docstring'''







 snake_case__  :    List[Any]							       =model_type
 if use_small:
  key += "_small"
 return os.path.join(_a		,  REMOTE_MODEL_PATHS[key]["""file_name"""]	)







def 			A__							(      _a  :					List[Any]		,  _a  :					Any	):





 '''simple docstring'''







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







def 			A__							(      _a  :					Optional[int]		,  _a  :					Tuple		,  _a  :					Optional[int]=False		,  _a  :					int="text"	):





 '''simple docstring'''







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

 # convert Bark model arguments to HF Bark model arguments
 snake_case__  :    Optional[Any]							       =model_args.pop("""n_head"""	)
 snake_case__  :    Any							       =model_args.pop("""n_embd"""	)
 snake_case__  :    Optional[Any]							       =model_args.pop("""n_layer"""	)

 snake_case__  :    Union[str, Any]							       =ConfigClass(**checkpoint["""model_args"""]	)
 snake_case__  :    Any							       =ModelClass(config=_a	)
 snake_case__  :    int							       =GenerationConfigClass()

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

   snake_case__  :    List[Any]							       =state_dict.pop(_a	)

 snake_case__  :    List[Any]							       =set(state_dict.keys()	) - set(model.state_dict().keys()	)
 snake_case__  :    Dict							       ={k for k in extra_keys if not k.endswith(""".attn.bias"""	)}
 snake_case__  :    Dict							       =set(model.state_dict().keys()	) - set(state_dict.keys()	)
 snake_case__  :    int							       ={k for k in missing_keys if not k.endswith(""".attn.bias"""	)}
 if len(_a	) != 0:
  raise ValueError(f"extra keys found: {extra_keys}"	)
 if len(_a	) != 0:
  raise ValueError(f"missing keys: {missing_keys}"	)
 model.load_state_dict(_a		,  strict=_a	)
 snake_case__  :    Union[str, Any]							       =model.num_parameters(exclude_embeddings=_a	)
 snake_case__  :    str							       =checkpoint["""best_val_loss"""].item()
 logger.info(f"model loaded: {round(n_params/1E6		,  1	)}M params, {round(_a		,  3	)} loss"	)
 model.eval()
 model.to(_a	)
 del checkpoint, state_dict

 return model







def 			A__							(      _a  :					str		,  _a  :					List[Any]=False		,  _a  :					str="text"	):





 '''simple docstring'''







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

 snake_case__  :    Any							       ="""cpu"""  # do conversion on cpu

 snake_case__  :    List[str]							       =_get_ckpt_path(_a		,  use_small=_a	)
 snake_case__  :    str							       =_load_model(_a		,  _a		,  model_type=_a		,  use_small=_a	)

 # load bark initial model
 snake_case__  :    Optional[int]							       =_bark_load_model(_a		,  """cpu"""		,  model_type=_a		,  use_small=_a	)

 if model_type == "text":
  snake_case__  :    Optional[int]							       =bark_model["""model"""]

 if model.num_parameters(exclude_embeddings=_a	) != 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
 snake_case__  :    List[str]							       =5
 snake_case__  :    List[str]							       =10

 if model_type in ["text", "coarse"]:
  snake_case__  :    Union[str, Any]							       =torch.randint(256		,  (batch_size, sequence_length)		,  dtype=torch.int	)
  snake_case__  :    Optional[int]							       =bark_model(_a	)[0]

  snake_case__  :    List[Any]							       =model(_a	)

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

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

  snake_case__  :    Any							       =model(_a		,  _a	)
  snake_case__  :    List[str]							       =bark_model(_a		,  _a	)

  snake_case__  :    Union[str, 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(_a	).mkdir(exist_ok=_a	)
 model.save_pretrained(_a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					List[Any]		,  _a  :					List[Any]		,  _a  :					List[Any]		,  _a  :					List[str]		,  _a  :					List[Any]		,  ):





 '''simple docstring'''







 snake_case__  :    str							       =os.path.join(_a		,  _a	)

 snake_case__  :    Optional[int]							       =BarkSemanticConfig.from_pretrained(os.path.join(_a		,  """config.json"""	)	)
 snake_case__  :    int							       =BarkCoarseConfig.from_pretrained(os.path.join(_a		,  """config.json"""	)	)
 snake_case__  :    List[Any]							       =BarkFineConfig.from_pretrained(os.path.join(_a		,  """config.json"""	)	)
 snake_case__  :    Optional[int]							       =EncodecConfig.from_pretrained("""facebook/encodec_24khz"""	)

 snake_case__  :    List[str]							       =BarkSemanticModel.from_pretrained(_a	)
 snake_case__  :    List[str]							       =BarkCoarseModel.from_pretrained(_a	)
 snake_case__  :    Dict							       =BarkFineModel.from_pretrained(_a	)
 snake_case__  :    Union[str, Any]							       =EncodecModel.from_pretrained("""facebook/encodec_24khz"""	)

 snake_case__  :    List[str]							       =BarkConfig.from_sub_model_configs(
     _a		,  _a		,  _a		,  _a	)

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

 snake_case__  :    List[str]							       =BarkModel(_a	)

 snake_case__  :    Dict							       =semantic
 snake_case__  :    Dict							       =coarseAcoustic
 snake_case__  :    Tuple							       =fineAcoustic
 snake_case__  :    Tuple							       =codec

 snake_case__  :    Union[str, Any]							       =bark_generation_config

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


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	:		str										=   parser.parse_args()

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













import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )







class  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=3_2				,			a=5				,			a=4				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=4				,			):
       snake_case__  :    Any							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    List[Any]							       =seq_length
       snake_case__  :    str							       =is_training
       snake_case__  :    Union[str, Any]							       =use_attention_mask
       snake_case__  :    str							       =use_token_type_ids
       snake_case__  :    int							       =use_labels
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    List[str]							       =hidden_size
       snake_case__  :    Dict							       =num_hidden_layers
       snake_case__  :    Optional[Any]							       =num_attention_heads
       snake_case__  :    List[str]							       =intermediate_size
       snake_case__  :    str							       =hidden_act
       snake_case__  :    Union[str, Any]							       =hidden_dropout_prob
       snake_case__  :    Tuple							       =attention_probs_dropout_prob
       snake_case__  :    Tuple							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Optional[Any]							       =type_sequence_label_size
       snake_case__  :    str							       =initializer_range
       snake_case__  :    List[Any]							       =num_choices
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Tuple							       =None
       if self.use_attention_mask:
        snake_case__  :    Union[str, Any]							       =random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__  :    Union[str, Any]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Tuple							       =RoFormerConfig(
           vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			is_decoder=a				,			initializer_range=self.initializer_range				,			)

       return config, input_ids, token_type_ids, attention_mask



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Optional[Any]							       =config_and_inputs
       snake_case__  :    Dict							       ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_flax
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						str										=							True

      _a							:						Optional[Any]										=							(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =FlaxRoFormerModelTester(self   )



      @slow
      def 						lowercase__					(     self   ):
       for model_class_name in self.all_model_classes:
        snake_case__  :    Tuple							       =model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,			from_pt=a   )
        snake_case__  :    List[Any]							       =model(np.ones((1, 1)   )   )
        self.assertIsNotNone(a   )







@require_flax
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
       snake_case__  :    Tuple							       =jnp.array([[0, 1, 2, 3, 4, 5]]   )
       snake_case__  :    str							       =model(a   )[0]

       snake_case__  :    List[str]							       =5_0_0_0_0

       snake_case__  :    str							       =(1, 6, vocab_size)
       self.assertEqual(output.shape				,			a   )

       snake_case__  :    Optional[int]							       =jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,			a				,			atol=1e-4   )   )





