



"""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  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Tuple						=     """switch_transformers"""
					_A		:			Tuple						=     ["""past_key_values"""]
					_A		:			Tuple						=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(       self:							Union[str, Any]						,     snake_case:							Optional[Any]=32_128						,     snake_case:							List[Any]=768						,     snake_case:							List[str]=64						,     snake_case:							str=2_048						,     snake_case:							Optional[int]=64						,     snake_case:							str=12						,     snake_case:							int=3						,     snake_case:							Tuple=12						,     snake_case:							Dict=3						,     snake_case:							Dict=12						,     snake_case:							Tuple=8						,     snake_case:							int=False						,     snake_case:							Tuple=0.0_1						,     snake_case:							int="float32"						,     snake_case:							Optional[Any]=False						,     snake_case:							Union[str, Any]=32						,     snake_case:							str=128						,     snake_case:							Tuple=0.1						,     snake_case:							Optional[int]=1E-6						,     snake_case:							List[str]=0.0_0_1						,     snake_case:							List[str]=0.0_0_1						,     snake_case:							List[str]=1.0						,     snake_case:							Dict="relu"						,     snake_case:							str=True						,     snake_case:							Union[str, Any]=False						,     snake_case:							Any=True						,     snake_case:							List[Any]=0						,     snake_case:							Tuple=1						,     **snake_case:							int						,     )   ->	Dict:
											snake_case_ :List[str]						=			vocab_size
											snake_case_ :List[str]						=			d_model
											snake_case_ :Optional[Any]						=			d_kv
											snake_case_ :Union[str, Any]						=			d_ff

											snake_case_ :Union[str, Any]						=			num_sparse_encoder_layers

											snake_case_ :Any						=			num_layers
											snake_case_ :Optional[Any]						=			(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											snake_case_ :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:
																	snake_case_ :Optional[int]						=			self.num_layers // self.num_sparse_encoder_layers
											else:
																	snake_case_ :int						=			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:
																	snake_case_ :Tuple						=			self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
																	snake_case_ :Any						=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

											snake_case_ :int						=			num_heads
											snake_case_ :Any						=			num_experts
											snake_case_ :Optional[Any]						=			expert_capacity
											snake_case_ :int						=			router_bias
											snake_case_ :Optional[Any]						=			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}"""						)
											snake_case_ :str						=			router_dtype

											snake_case_ :Optional[Any]						=			router_ignore_padding_tokens
											snake_case_ :str						=			relative_attention_num_buckets
											snake_case_ :Tuple						=			relative_attention_max_distance

											snake_case_ :List[str]						=			dropout_rate
											snake_case_ :List[str]						=			layer_norm_epsilon
											snake_case_ :Dict						=			initializer_factor
											snake_case_ :Tuple						=			feed_forward_proj
											snake_case_ :List[str]						=			use_cache
											snake_case_ :List[str]						=			add_router_probs

											snake_case_ :Optional[Any]						=			router_z_loss_coef
											snake_case_ :List[str]						=			router_aux_loss_coef

											snake_case_ :Dict						=			self.feed_forward_proj.split("""-"""						)
											snake_case_ :int						=			act_info[-1]
											snake_case_ :Union[str, Any]						=			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":
																	snake_case_ :Tuple						=			"""gelu_new"""

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





import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT 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.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



