






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
       if "model" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""model."""   ,      """""" )
       if "norm1" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""norm1"""   ,      """attention.output.LayerNorm""" )
       if "norm2" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""norm2"""   ,      """output.LayerNorm""" )
       if "norm" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""norm"""   ,      """LayerNorm""" )
       if "transformer" in orig_key:
              _UpperCAmelCase							=    orig_key.split(""".""" )[0].split("""_""" )[-1]
              _UpperCAmelCase							=    orig_key.replace(f"""transformer_{layer_num}"""   ,      f"""encoder.layer.{layer_num}""" )
       if "mha.attn" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""mha.attn"""   ,      """attention.self""" )
       if "mha" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""mha"""   ,      """attention""" )
       if "W_q" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""W_q"""   ,      """self.query""" )
       if "W_k" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""W_k"""   ,      """self.key""" )
       if "W_v" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""W_v"""   ,      """self.value""" )
       if "ff1" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""ff1"""   ,      """intermediate.dense""" )
       if "ff2" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""ff2"""   ,      """output.dense""" )
       if "ff" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""ff"""   ,      """output.dense""" )
       if "mlm_class" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""mlm.mlm_class"""   ,      """cls.predictions.decoder""" )
       if "mlm" in orig_key:
              _UpperCAmelCase							=    orig_key.replace("""mlm"""   ,      """cls.predictions.transform""" )
       if "cls" not in orig_key:
              _UpperCAmelCase							=    """yoso.""" + orig_key

       return orig_key




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase							=    orig_state_dict.pop(__snake_case )

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     _UpperCAmelCase							=    val

       _UpperCAmelCase							=    orig_state_dict["""cls.predictions.decoder.bias"""]
       _UpperCAmelCase							=    torch.arange(__snake_case ).expand((1, -1) ) + 2

       return orig_state_dict




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )["""model_state_dict"""]
       _UpperCAmelCase							=    YosoConfig.from_json_file(__snake_case )
       _UpperCAmelCase							=    YosoForMaskedLM(__snake_case )

       _UpperCAmelCase							=    convert_checkpoint_helper(config.max_position_embeddings   ,      __snake_case )

       print(model.load_state_dict(__snake_case ) )
       model.eval()
       model.save_pretrained(__snake_case )

       print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
  __a:     Union[str, Any]   					=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The json file for YOSO model config.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  __a:     Union[str, Any]   					=					parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__a:     Dict   					=					logging.get_logger(__name__)

__a:     Optional[int]   					=					{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''efficientnet'''


  def __init__(      self		:			Dict       ,				lowerCamelCase		:			int = 3       ,				lowerCamelCase		:			int = 600       ,				lowerCamelCase		:			float = 2.0       ,				lowerCamelCase		:			float = 3.1       ,				lowerCamelCase		:			int = 8       ,				lowerCamelCase		:			List[int] = [3, 3, 5, 3, 5, 5, 3]       ,				lowerCamelCase		:			List[int] = [32, 16, 24, 40, 80, 112, 192]       ,				lowerCamelCase		:			List[int] = [16, 24, 40, 80, 112, 192, 320]       ,				lowerCamelCase		:			List[int] = []       ,				lowerCamelCase		:			List[int] = [1, 2, 2, 2, 1, 2, 1]       ,				lowerCamelCase		:			List[int] = [1, 2, 2, 3, 3, 4, 1]       ,				lowerCamelCase		:			List[int] = [1, 6, 6, 6, 6, 6, 6]       ,				lowerCamelCase		:			float = 0.25       ,				lowerCamelCase		:			str = "swish"       ,				lowerCamelCase		:			int = 2560       ,				lowerCamelCase		:			str = "mean"       ,				lowerCamelCase		:			float = 0.02       ,				lowerCamelCase		:			float = 0.001       ,				lowerCamelCase		:			float = 0.99       ,				lowerCamelCase		:			float = 0.5       ,				lowerCamelCase		:			float = 0.2       ,				**lowerCamelCase		:			List[str]       ,				)						->	Union[str, Any]:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)

         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    width_coefficient
         _UpperCAmelCase							=    depth_coefficient
         _UpperCAmelCase							=    depth_divisor
         _UpperCAmelCase							=    kernel_sizes
         _UpperCAmelCase							=    in_channels
         _UpperCAmelCase							=    out_channels
         _UpperCAmelCase							=    depthwise_padding
         _UpperCAmelCase							=    strides
         _UpperCAmelCase							=    num_block_repeats
         _UpperCAmelCase							=    expand_ratios
         _UpperCAmelCase							=    squeeze_expansion_ratio
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dim
         _UpperCAmelCase							=    pooling_type
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    batch_norm_eps
         _UpperCAmelCase							=    batch_norm_momentum
         _UpperCAmelCase							=    dropout_rate
         _UpperCAmelCase							=    drop_connect_rate
         _UpperCAmelCase							=    sum(lowerCamelCase							) * 4



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  version.parse('''1.11'''     )


  @property
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Mapping[str, Mapping[int, str]]:


         """simple docstring"""
         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)





  @property
  def  lowerCamelCase       (      self		:			Dict							)						->	float:


         """simple docstring"""
         return 1E-5