





"""simple docstring"""





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


__A											=		logging.get_logger(__name__)

__A											=		{
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :Tuple		  =					"""switch_transformers"""
       __magic_name__     :int		  =					["""past_key_values"""]
       __magic_name__     :List[Any]		  =					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






       def __init__(    self    , __UpperCAmelCase=3_2_1_2_8    , __UpperCAmelCase=7_6_8    , __UpperCAmelCase=6_4    , __UpperCAmelCase=2_0_4_8    , __UpperCAmelCase=6_4    , __UpperCAmelCase=1_2    , __UpperCAmelCase=3    , __UpperCAmelCase=1_2    , __UpperCAmelCase=3    , __UpperCAmelCase=1_2    , __UpperCAmelCase=8    , __UpperCAmelCase=False    , __UpperCAmelCase=0.01    , __UpperCAmelCase="float32"    , __UpperCAmelCase=False    , __UpperCAmelCase=3_2    , __UpperCAmelCase=1_2_8    , __UpperCAmelCase=0.1    , __UpperCAmelCase=1E-6    , __UpperCAmelCase=0.0_01    , __UpperCAmelCase=0.0_01    , __UpperCAmelCase=1.0    , __UpperCAmelCase="relu"    , __UpperCAmelCase=True    , __UpperCAmelCase=False    , __UpperCAmelCase=True    , __UpperCAmelCase=0    , __UpperCAmelCase=1    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							vocab_size
         lowerCAmelCase__			:Dict											=							d_model
         lowerCAmelCase__			:Union[str, Any]											=							d_kv
         lowerCAmelCase__			:Union[str, Any]											=							d_ff

         lowerCAmelCase__			:List[str]											=							num_sparse_encoder_layers

         lowerCAmelCase__			:Dict											=							num_layers
         lowerCAmelCase__			:str											=							(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowerCAmelCase__			:List[Any]											=							num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
           lowerCAmelCase__			:int											=							self.num_layers // self.num_sparse_encoder_layers
         else:
           lowerCAmelCase__			:Any											=							self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
           lowerCAmelCase__			:int											=							self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
           lowerCAmelCase__			:List[str]											=							self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowerCAmelCase__			:Optional[int]											=							num_heads
         lowerCAmelCase__			:Union[str, Any]											=							num_experts
         lowerCAmelCase__			:Dict											=							expert_capacity
         lowerCAmelCase__			:str											=							router_bias
         lowerCAmelCase__			:int											=							router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"      )
         lowerCAmelCase__			:Union[str, Any]											=							router_dtype

         lowerCAmelCase__			:List[Any]											=							router_ignore_padding_tokens
         lowerCAmelCase__			:str											=							relative_attention_num_buckets
         lowerCAmelCase__			:Dict											=							relative_attention_max_distance

         lowerCAmelCase__			:Optional[Any]											=							dropout_rate
         lowerCAmelCase__			:Optional[int]											=							layer_norm_epsilon
         lowerCAmelCase__			:List[str]											=							initializer_factor
         lowerCAmelCase__			:str											=							feed_forward_proj
         lowerCAmelCase__			:Union[str, Any]											=							use_cache
         lowerCAmelCase__			:Tuple											=							add_router_probs

         lowerCAmelCase__			:Dict											=							router_z_loss_coef
         lowerCAmelCase__			:Any											=							router_aux_loss_coef

         lowerCAmelCase__			:int											=							self.feed_forward_proj.split('-'      )
         lowerCAmelCase__			:List[Any]											=							act_info[-1]
         lowerCAmelCase__			:Dict											=							act_info[0] == 'gated'

         if len(__UpperCAmelCase      ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase      ) > 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":
           lowerCAmelCase__			:List[str]											=							'gelu_new'

         super().__init__(
             pad_token_id=__UpperCAmelCase    , eos_token_id=__UpperCAmelCase    , is_encoder_decoder=__UpperCAmelCase    , **__UpperCAmelCase    , )







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

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

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
