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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    '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							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		'''poolformer'''



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=16		,				lowerCAmelCase_=16		,				lowerCAmelCase_=3		,				lowerCAmelCase_=4.0		,				lowerCAmelCase_=[2, 2, 6, 2]		,				lowerCAmelCase_=[64, 1_28, 3_20, 5_12]		,				lowerCAmelCase_=[7, 3, 3, 3]		,				lowerCAmelCase_=[4, 2, 2, 2]		,				lowerCAmelCase_=[2, 1, 1, 1]		,				lowerCAmelCase_=4		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=True		,				lowerCAmelCase_=1E-5		,				lowerCAmelCase_=0.02		,				**lowerCAmelCase_		,				)  ->					int:
        _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__(**lowerCAmelCase_							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 2E-3





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(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__":
    _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(
        '--albert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained ALBERT 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.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




