





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		  =			{
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Tuple						      =     "plbart"
     __snake_case       :      str						      =     ["past_key_values"]
     __snake_case       :      Any						      =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(				self		:       Any		,lowerCamelCase__		:       List[str]=50005		,lowerCamelCase__		:       Any=1024		,lowerCamelCase__		:       Optional[int]=6		,lowerCamelCase__		:       str=3072		,lowerCamelCase__		:       Optional[int]=12		,lowerCamelCase__		:       str=6		,lowerCamelCase__		:       Optional[Any]=3072		,lowerCamelCase__		:       List[str]=12		,lowerCamelCase__		:       str=0.0		,lowerCamelCase__		:       List[Any]=0.0		,lowerCamelCase__		:       int=True		,lowerCamelCase__		:       str=True		,lowerCamelCase__		:       Optional[int]="gelu"		,lowerCamelCase__		:       str=768		,lowerCamelCase__		:       Any=0.1		,lowerCamelCase__		:       Dict=0.1		,lowerCamelCase__		:       List[Any]=0.0		,lowerCamelCase__		:       str=0.02		,lowerCamelCase__		:       str=0.0		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       Dict=1		,lowerCamelCase__		:       Optional[int]=0		,lowerCamelCase__		:       Optional[int]=2		,lowerCamelCase__		:       int=2		,**lowerCamelCase__		:       Tuple		,)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		vocab_size
           SCREAMING_SNAKE_CASE						=		max_position_embeddings
           SCREAMING_SNAKE_CASE						=		d_model
           SCREAMING_SNAKE_CASE						=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE						=		encoder_layers
           SCREAMING_SNAKE_CASE						=		encoder_attention_heads
           SCREAMING_SNAKE_CASE						=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE						=		decoder_layers
           SCREAMING_SNAKE_CASE						=		decoder_attention_heads
           SCREAMING_SNAKE_CASE						=		dropout
           SCREAMING_SNAKE_CASE						=		attention_dropout
           SCREAMING_SNAKE_CASE						=		activation_dropout
           SCREAMING_SNAKE_CASE						=		activation_function
           SCREAMING_SNAKE_CASE						=		init_std
           SCREAMING_SNAKE_CASE						=		encoder_layerdrop
           SCREAMING_SNAKE_CASE						=		decoder_layerdrop
           SCREAMING_SNAKE_CASE						=		classifier_dropout
           SCREAMING_SNAKE_CASE						=		use_cache
           SCREAMING_SNAKE_CASE						=		encoder_layers
           SCREAMING_SNAKE_CASE						=		scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__		,bos_token_id=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,is_encoder_decoder=lowerCamelCase__		,forced_eos_token_id=lowerCamelCase__		,**lowerCamelCase__		,)

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           return OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
               ]			)






     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           if self.use_past:
                 return OrderedDict(
                     [
                         ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                         ("""past_keys""", {0: """batch""", 2: """sequence"""}),
                         ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                     ]			)
           else:
                 return OrderedDict(
                     [
                         ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                         ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                     ]			)









import argparse

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


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


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

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			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."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


