

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


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """beit"""

  def __init__(  self		:			List[Any]					,A		:			Any=8_192					,A		:			Union[str, Any]=768					,A		:			Any=12					,A		:			Dict=12					,A		:			Any=3_072					,A		:			Union[str, Any]="gelu"					,A		:			Any=0.0					,A		:			List[str]=0.0					,A		:			Tuple=0.0_2					,A		:			List[Any]=1e-12					,A		:			Union[str, Any]=224					,A		:			Union[str, Any]=16					,A		:			Optional[Any]=3					,A		:			int=False					,A		:			Optional[Any]=False					,A		:			Any=False					,A		:			List[str]=False					,A		:			List[Any]=0.1					,A		:			List[str]=0.1					,A		:			List[Any]=True					,A		:			Tuple=[3, 5, 7, 11]					,A		:			Optional[Any]=[1, 2, 3, 6]					,A		:			List[str]=True					,A		:			List[Any]=0.4					,A		:			Optional[Any]=256					,A		:			str=1					,A		:			Tuple=False					,A		:			Tuple=255					,**A		:			Dict					,):






   '''simple docstring'''
   super().__init__(**A    )

   UpperCAmelCase__					:  str					   =					vocab_size
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_size
   UpperCAmelCase__					:  int					   =					num_hidden_layers
   UpperCAmelCase__					:  List[str]					   =					num_attention_heads
   UpperCAmelCase__					:  List[str]					   =					intermediate_size
   UpperCAmelCase__					:  int					   =					hidden_act
   UpperCAmelCase__					:  List[str]					   =					hidden_dropout_prob
   UpperCAmelCase__					:  Union[str, Any]					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  Any					   =					initializer_range
   UpperCAmelCase__					:  List[Any]					   =					layer_norm_eps

   UpperCAmelCase__					:  int					   =					image_size
   UpperCAmelCase__					:  Union[str, Any]					   =					patch_size
   UpperCAmelCase__					:  List[Any]					   =					num_channels
   UpperCAmelCase__					:  Union[str, Any]					   =					use_mask_token
   UpperCAmelCase__					:  Any					   =					use_absolute_position_embeddings
   UpperCAmelCase__					:  Tuple					   =					use_relative_position_bias
   UpperCAmelCase__					:  List[Any]					   =					use_shared_relative_position_bias
   UpperCAmelCase__					:  Any					   =					layer_scale_init_value
   UpperCAmelCase__					:  List[str]					   =					drop_path_rate
   UpperCAmelCase__					:  Optional[int]					   =					use_mean_pooling
   # decode head attributes (semantic segmentation)
   UpperCAmelCase__					:  List[str]					   =					out_indices
   UpperCAmelCase__					:  Dict					   =					pool_scales
   # auxiliary head attributes (semantic segmentation)
   UpperCAmelCase__					:  Any					   =					use_auxiliary_head
   UpperCAmelCase__					:  List[str]					   =					auxiliary_loss_weight
   UpperCAmelCase__					:  List[Any]					   =					auxiliary_channels
   UpperCAmelCase__					:  int					   =					auxiliary_num_convs
   UpperCAmelCase__					:  Any					   =					auxiliary_concat_input
   UpperCAmelCase__					:  Optional[int]					   =					semantic_loss_ignore_index







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       version.parse("""1.11"""					)

  @property
  def    __lowercase       (  self		:			Tuple    ):






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




  @property
  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   return 1e-4








"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





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

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

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

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					convert_checkpoint_helper(config.max_position_embeddings							,    __UpperCamelCase      )

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

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


if __name__ == "__main__":
      __UpperCAmelCase =				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.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





