"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Optional[Any]													=						{
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}

class       a_      (     lowercase_			):
					UpperCamelCase_ :	int			=    '''poolformer'''




					def __init__(	self						:     Any  ,						snake_case__						:     str=3  ,						snake_case__						:     Tuple=16  ,						snake_case__						:     List[Any]=16  ,						snake_case__						:     List[Any]=3  ,						snake_case__						:     Optional[Any]=4.0  ,						snake_case__						:     Optional[int]=[2, 2, 6, 2]  ,						snake_case__						:     Dict=[64, 128, 320, 512]  ,						snake_case__						:     int=[7, 3, 3, 3]  ,						snake_case__						:     Optional[Any]=[4, 2, 2, 2]  ,						snake_case__						:     Dict=[2, 1, 1, 1]  ,						snake_case__						:     List[str]=4  ,						snake_case__						:     int=0.0  ,						snake_case__						:     Optional[int]="gelu"  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     Union[str, Any]=1E-5  ,						snake_case__						:     Dict=0.02  ,						**snake_case__						:     Any  ,						):
							A        =   num_channels
							A        =   patch_size
							A        =   stride
							A        =   padding
							A        =   pool_size
							A        =   hidden_sizes
							A        =   mlp_ratio
							A        =   depths
							A        =   patch_sizes
							A        =   strides
							A        =   num_encoder_blocks
							A        =   drop_path_rate
							A        =   hidden_act
							A        =   use_layer_scale
							A        =   layer_scale_init_value
							A        =   initializer_range
							super().__init__(**snake_case__     )

class       a_      (     lowercase_			):
					UpperCamelCase_ :	Dict			=    version.parse("1.11"			)




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							return 2E-3





"""simple docstring"""




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		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




