
"""simple docstring"""




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  A__   :					Dict							=   argparse.ArgumentParser(
      description=(
          'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
          ' Distillation'
      )
  )
  parser.add_argument('--model_type', default='bert', choices=['bert'])
  parser.add_argument('--model_name', default='bert-base-uncased', type=str)
  parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
  parser.add_argument('--vocab_transform', action='store_true')
  A__   :					List[Any]							=   parser.parse_args()

  if args.model_type == "bert":
    A__   :					Dict							=   BertForMaskedLM.from_pretrained(args.model_name)
    A__   :					Optional[Any]							=   'bert'
  else:
    raise ValueError('args.model_type should be "bert".')

  A__   :					List[Any]							=   model.state_dict()
  A__   :					Any							=   {}

  for w in ["word_embeddings", "position_embeddings"]:
    A__   :					Optional[Any]							=   state_dict[f"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    A__   :					Dict							=   state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

  A__   :					Dict							=   0
  for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
    for w in ["weight", "bias"]:
      A__   :					List[Any]							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
      ]
      A__   :					Any							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
      ]
      A__   :					int							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
      ]

      A__   :					List[Any]							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      A__   :					List[str]							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      A__   :					Dict							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      A__   :					Optional[int]							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      A__   :					Union[str, Any]							=   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  A__   :					Optional[int]							=   state_dict['cls.predictions.decoder.weight']
  A__   :					int							=   state_dict['cls.predictions.bias']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      A__   :					Optional[Any]							=   state_dict[f"""cls.predictions.transform.dense.{w}"""]
      A__   :					Tuple							=   state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

  print(f"""N layers selected for distillation: {std_idx}""")
  print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

  print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
  torch.save(compressed_sd, args.dump_checkpoint)




"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A__   :					Dict							=   logging.get_logger(__name__)

A__   :					int							=   {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''dpt'''
       def __init__(     self ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.0 ,				A_=0.0 ,				A_=0.02 ,				A_=1E-12 ,				A_=384 ,				A_=16 ,				A_=3 ,				A_=False ,				A_=True ,				A_=[2, 5, 8, 11] ,				A_="project" ,				A_=[4, 2, 1, 0.5] ,				A_=[96, 192, 384, 768] ,				A_=256 ,				A_=-1 ,				A_=False ,				A_=True ,				A_=0.4 ,				A_=255 ,				A_=0.1 ,				A_=[1, 1024, 24, 24] ,				A_=[0, 1] ,				A_=None ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Union[str, Any]       =     hidden_size
         _lowercase:			str       =     is_hybrid

         if self.is_hybrid:
           if backbone_config is None:
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }
             _lowercase:			List[str]       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             _lowercase:			str       =     backbone_config
           else:
             raise ValueError(
                 f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

           _lowercase:			Dict       =     backbone_featmap_shape
           _lowercase:			Optional[int]       =     neck_ignore_stages

           if readout_type != "project":
             raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''       )
         else:
           _lowercase:			Any       =     None
           _lowercase:			str       =     None
           _lowercase:			Optional[Any]       =     []

         _lowercase:			Tuple       =     num_hidden_layers
         _lowercase:			Optional[int]       =     num_attention_heads
         _lowercase:			Any       =     intermediate_size
         _lowercase:			List[Any]       =     hidden_act
         _lowercase:			Union[str, Any]       =     hidden_dropout_prob
         _lowercase:			Tuple       =     attention_probs_dropout_prob
         _lowercase:			Dict       =     initializer_range
         _lowercase:			Dict       =     layer_norm_eps
         _lowercase:			Any       =     image_size
         _lowercase:			Optional[int]       =     patch_size
         _lowercase:			Optional[int]       =     num_channels
         _lowercase:			int       =     qkv_bias
         _lowercase:			List[str]       =     backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
           raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''       )
         _lowercase:			Union[str, Any]       =     readout_type
         _lowercase:			Optional[Any]       =     reassemble_factors
         _lowercase:			List[str]       =     neck_hidden_sizes
         _lowercase:			Tuple       =     fusion_hidden_size
         _lowercase:			int       =     head_in_index
         _lowercase:			Optional[int]       =     use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         _lowercase:			int       =     use_auxiliary_head
         _lowercase:			Dict       =     auxiliary_loss_weight
         _lowercase:			List[Any]       =     semantic_loss_ignore_index
         _lowercase:			Dict       =     semantic_classifier_dropout



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[int]       =     copy.deepcopy(self.__dict__       )

         if output["backbone_config"] is not None:
           _lowercase:			List[str]       =     self.backbone_config.to_dict()

         _lowercase:			List[Any]       =     self.__class__.model_type
         return output
