





"""simple docstring"""
from typing import Mapping

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


lowerCAmelCase:	List[Any]							      =logging.get_logger(__name__)






class lowerCamelCase__  (    _a				):
   __UpperCAmelCase        =					'mt5'
   __UpperCAmelCase        =					['past_key_values']



   def __init__(       self      ,   snake_case=2_5_0_1_1_2      ,   snake_case=5_1_2      ,   snake_case=6_4      ,   snake_case=1_0_2_4      ,   snake_case=8      ,   snake_case=None      ,   snake_case=6      ,   snake_case=3_2      ,   snake_case=1_2_8      ,   snake_case=0.1      ,   snake_case=1E-6      ,   snake_case=1.0      ,   snake_case="gated-gelu"      ,   snake_case=True      ,   snake_case=True      ,   snake_case="T5Tokenizer"      ,   snake_case=False      ,   snake_case=0      ,   snake_case=1      ,   snake_case=0      ,   **snake_case      ,   ) ->    List[str]:







      """simple docstring"""
      super().__init__(
          is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
      lowercase    :			List[str]  =       vocab_size
      lowercase    :			str  =       d_model
      lowercase    :			Any  =       d_kv
      lowercase    :			int  =       d_ff
      lowercase    :			Union[str, Any]  =       num_layers
      lowercase    :			str  =       (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      lowercase    :			str  =       num_heads
      lowercase    :			Tuple  =       relative_attention_num_buckets
      lowercase    :			Union[str, Any]  =       relative_attention_max_distance
      lowercase    :			Any  =       dropout_rate
      lowercase    :			int  =       layer_norm_epsilon
      lowercase    :			int  =       initializer_factor
      lowercase    :			Union[str, Any]  =       feed_forward_proj
      lowercase    :			Tuple  =       use_cache

      lowercase    :			Optional[int]  =       self.feed_forward_proj.split("""-"""	)
      lowercase    :			int  =       act_info[-1]
      lowercase    :			Optional[int]  =       act_info[0] == """gated"""

      if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'"""	)

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
         lowercase    :			int  =       """gelu_new"""



   @property
   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      return self.d_model



   @property
   def    _UpperCAmelCase	(       self	) ->    List[str]:







      """simple docstring"""
      return self.num_heads





   @property
   def    _UpperCAmelCase	(       self	) ->    List[str]:







      """simple docstring"""
      return self.num_layers






class lowerCamelCase__  (    _a				):



   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def    _UpperCAmelCase	(       self	) ->    Mapping[str, Mapping[int, str]]:







      """simple docstring"""
      lowercase    :			Optional[int]  =       {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
         lowercase    :			str  =       """past_encoder_sequence + sequence"""
         lowercase    :			List[str]  =       {0: """batch"""}
         lowercase    :			Optional[Any]  =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
         lowercase    :			Optional[Any]  =       {0: """batch""", 1: """decoder_sequence"""}
         lowercase    :			List[str]  =       {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
         self.fill_with_past_key_values_(snake_case      ,   direction="""inputs"""	)

      return common_inputs



   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      return 1_3





   @property
   def    _UpperCAmelCase	(       self	) ->    float:







      """simple docstring"""
      return 5E-4









"""simple docstring"""
import argparse

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


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

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

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


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =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=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[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,
   )


