

'''simple docstring'''






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      						=			logging.get_logger(__name__)

lowerCAmelCase__      						=			{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class     lowercase_			(_UpperCAmelCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    '''mvp'''
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    ['''past_key_values''']
  SCREAMING_SNAKE_CASE					:						List[str]           =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






  def __init__(   self     :  List[Any]      ,lowercase__     :  Optional[Any]=5_0_2_6_7      ,lowercase__     :  Union[str, Any]=1_0_2_4      ,lowercase__     :  List[str]=1_2      ,lowercase__     :  Dict=4_0_9_6      ,lowercase__     :  int=1_6      ,lowercase__     :  Optional[Any]=1_2      ,lowercase__     :  Optional[int]=4_0_9_6      ,lowercase__     :  Tuple=1_6      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  Any=0.0      ,lowercase__     :  Optional[Any]="gelu"      ,lowercase__     :  Tuple=1_0_2_4      ,lowercase__     :  str=0.1      ,lowercase__     :  List[str]=0.0      ,lowercase__     :  Union[str, Any]=0.0      ,lowercase__     :  Union[str, Any]=0.0_2      ,lowercase__     :  str=0.0      ,lowercase__     :  Optional[int]=False      ,lowercase__     :  Any=True      ,lowercase__     :  List[str]=1      ,lowercase__     :  int=0      ,lowercase__     :  Dict=2      ,lowercase__     :  int=True      ,lowercase__     :  Optional[int]=2      ,lowercase__     :  Any=2      ,lowercase__     :  Optional[int]=False      ,lowercase__     :  Any=1_0_0      ,lowercase__     :  str=8_0_0      ,**lowercase__     :  Optional[Any]      ,):
   __lowercase													=				vocab_size
   __lowercase													=				max_position_embeddings
   __lowercase													=				d_model
   __lowercase													=				encoder_ffn_dim
   __lowercase													=				encoder_layers
   __lowercase													=				encoder_attention_heads
   __lowercase													=				decoder_ffn_dim
   __lowercase													=				decoder_layers
   __lowercase													=				decoder_attention_heads
   __lowercase													=				dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				activation_function
   __lowercase													=				init_std
   __lowercase													=				encoder_layerdrop
   __lowercase													=				decoder_layerdrop
   __lowercase													=				classifier_dropout
   __lowercase													=				use_cache
   __lowercase													=				encoder_layers
   __lowercase													=				scale_embedding  # scale factor will be sqrt(d_model) if True
   __lowercase													=				use_prompt
   __lowercase													=				prompt_length
   __lowercase													=				prompt_mid_dim

   super().__init__(
       pad_token_id=A_      ,bos_token_id=A_      ,eos_token_id=A_      ,is_encoder_decoder=A_      ,decoder_start_token_id=A_      ,forced_eos_token_id=A_      ,**A_      ,)

   if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''      ,A_    ):
    __lowercase													=				self.bos_token_id
    warnings.warn(
        F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
        '''The config can simply be saved and uploaded again to be fixed.'''    )




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

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


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

