
'''simple docstring'''



import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


UpperCamelCase__					:						Union[str, Any]											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'semantic'






       def __init__(							self				,							A__=1_00_00				,							A__=True				,							A__=7_68				,							A__=False				,							A__=False				,							A__=False				,							A__=False				,							A__=0.7				,							A__=True				,							A__=1_00_48				,							A__=12_95_95				,							A__=12_95_99				,							A__=1_00_00				,							A__=2_56				,							A__=49.9				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              temperature=A__				,							do_sample=A__				,							eos_token_id=A__				,							renormalize_logits=A__				,							max_new_tokens=A__				,							output_scores=A__				,							return_dict_in_generate=A__				,							output_hidden_states=A__				,							output_attentions=A__				,							**A__				,							)

          _SCREAMING_SNAKE_CASE          = text_encoding_offset
          _SCREAMING_SNAKE_CASE          = text_pad_token
          _SCREAMING_SNAKE_CASE          = eos_token_id
          _SCREAMING_SNAKE_CASE          = semantic_infer_token
          _SCREAMING_SNAKE_CASE          = semantic_vocab_size
          _SCREAMING_SNAKE_CASE          = max_input_semantic_length
          _SCREAMING_SNAKE_CASE          = semantic_rate_hz






class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'coarse_acoustics'






       def __init__(							self				,							A__=True				,							A__=False				,							A__=False				,							A__=False				,							A__=False				,							A__=0.7				,							A__=True				,							A__=1_20_48				,							A__=75				,							A__=2				,							A__=1_20_50				,							A__=2_56				,							A__ = 6_30				,							A__ = 60				,							**A__				,							)			->	Any:
          super().__init__(
              temperature=A__				,							do_sample=A__				,							renormalize_logits=A__				,							output_scores=A__				,							return_dict_in_generate=A__				,							output_hidden_states=A__				,							output_attentions=A__				,							**A__				,							)

          _SCREAMING_SNAKE_CASE          = coarse_semantic_pad_token
          _SCREAMING_SNAKE_CASE          = coarse_rate_hz
          _SCREAMING_SNAKE_CASE          = n_coarse_codebooks
          _SCREAMING_SNAKE_CASE          = coarse_infer_token
          _SCREAMING_SNAKE_CASE          = max_coarse_input_length
          _SCREAMING_SNAKE_CASE          = max_coarse_history
          _SCREAMING_SNAKE_CASE          = sliding_window_len






class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'fine_acoustics'






       def __init__(							self				,							A__=0.5				,							A__=5_12				,							A__=10_24				,							A__=8				,							**A__				,							)			->	List[str]:
          super().__init__(temperature=A__					)

          _SCREAMING_SNAKE_CASE          = max_fine_history_length
          _SCREAMING_SNAKE_CASE          = max_fine_input_length
          _SCREAMING_SNAKE_CASE          = n_fine_codebooks






class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'bark'
       SCREAMING_SNAKE_CASE					=     True






       def __init__(							self				,							A__ = None				,							A__ = None				,							A__ = None				,							A__=2_40_00				,							A__=10_24				,							**A__				,							)			->	Dict:
          if semantic_config is None:
             _SCREAMING_SNAKE_CASE          = {}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""					)

          if coarse_acoustics_config is None:
             _SCREAMING_SNAKE_CASE          = {}
             logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""					)

          if fine_acoustics_config is None:
             _SCREAMING_SNAKE_CASE          = {}
             logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""					)

          _SCREAMING_SNAKE_CASE          = BarkSemanticGenerationConfig(**A__					)
          _SCREAMING_SNAKE_CASE          = BarkCoarseGenerationConfig(**A__					)
          _SCREAMING_SNAKE_CASE          = BarkFineGenerationConfig(**A__					)

          _SCREAMING_SNAKE_CASE          = sample_rate
          _SCREAMING_SNAKE_CASE          = codebook_size






       @classmethod
       def 				UpperCamelCase (							cls				,							A__				,							A__				,							A__				,							**A__				,							)			->	List[str]:
          return cls(
              semantic_config=semantic_config.to_dict()				,							coarse_acoustics_config=coarse_acoustics_config.to_dict()				,							fine_acoustics_config=fine_acoustics_config.to_dict()				,							**A__				,							)






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = copy.deepcopy(self.__dict__					)

          _SCREAMING_SNAKE_CASE          = self.semantic_config.to_dict()
          _SCREAMING_SNAKE_CASE          = self.coarse_acoustics_config.to_dict()
          _SCREAMING_SNAKE_CASE          = self.fine_acoustics_config.to_dict()

          _SCREAMING_SNAKE_CASE          = self.__class__.model_type
          return output








'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






