



"""simple docstring"""
import os

import numpy
import onnx





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=a.name
   SCREAMING_SNAKE_CASE_: Dict     			=b.name

   SCREAMING_SNAKE_CASE_: Dict     			=""""""
   SCREAMING_SNAKE_CASE_: List[str]     			=""""""

   SCREAMING_SNAKE_CASE_: Optional[int]     			=a == b

   SCREAMING_SNAKE_CASE_: int     			=name_a
   SCREAMING_SNAKE_CASE_: Optional[int]     			=name_b

   return res





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   for i, input_name in enumerate(node_proto.input ):
      if input_name == name:
         node_proto.input.insert(lowercase							,			lowercase )
         node_proto.input.pop(i + 1 )

   if node_proto.op_type == "If":
      _graph_replace_input_with(node_proto.attribute[0].g							,			lowercase							,			lowercase )
      _graph_replace_input_with(node_proto.attribute[1].g							,			lowercase							,			lowercase )
   if node_proto.op_type == "Loop":
      _graph_replace_input_with(node_proto.attribute[0].g							,			lowercase							,			lowercase )





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   for n in graph_proto.node:
      _node_replace_input_with(lowercase							,			lowercase							,			lowercase )





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=list(model.graph.initializer )
   SCREAMING_SNAKE_CASE_: Dict     			=list(model_without_ext.graph.initializer )
   for i, ref_i in ind_to_replace:
      assert inits_with_data[i].name == inits[i].name
      assert inits_with_data[ref_i].name == inits[ref_i].name
      assert i > ref_i

      SCREAMING_SNAKE_CASE_: List[Any]     			=inits[i].name
      SCREAMING_SNAKE_CASE_: List[Any]     			=inits[ref_i].name

      model_without_ext.graph.initializer.remove(inits[i] )

      # for n in model.graph.node:
      _graph_replace_input_with(model_without_ext.graph							,			lowercase							,			lowercase )





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=os.path.dirname(lowercase )
   SCREAMING_SNAKE_CASE_: List[str]     			=os.path.basename(lowercase )

   SCREAMING_SNAKE_CASE_: List[str]     			=onnx.load(os.path.join(lowercase							,			lowercase ) )

   SCREAMING_SNAKE_CASE_: Dict     			=list(model.graph.initializer )

   SCREAMING_SNAKE_CASE_: str     			=set()
   SCREAMING_SNAKE_CASE_: Tuple     			={}
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]

   SCREAMING_SNAKE_CASE_: List[str]     			=0

   for i in range(len(lowercase ) ):
      if i in dup_set:
         continue

      for j in range(i + 1							,			len(lowercase ) ):
         if j in dup_set:
            continue
         if _is_equal_tensor_proto(inits[i]							,			inits[j] ):
            dup_set.add(lowercase )
            dup_set.add(lowercase )

            SCREAMING_SNAKE_CASE_: Any     			=inits[j].data_type
            SCREAMING_SNAKE_CASE_: Any     			=numpy.prod(inits[j].dims )
            if dtype == 1:
               mem_size *= 4
            elif dtype == 6:
               mem_size *= 4
            elif dtype == 7 or dtype == 11:
               mem_size *= 8
            else:
               print("""unexpected data type: """							,			lowercase )
            total_reduced_size += mem_size

            SCREAMING_SNAKE_CASE_: Tuple     			=inits[i].name
            SCREAMING_SNAKE_CASE_: Dict     			=inits[j].name

            if name_i in dup_map:
               dup_map[name_i].append(lowercase )
            else:
               SCREAMING_SNAKE_CASE_: int     			=[name_j]
            ind_to_replace.append((j, i) )

   print("""total reduced size: """							,			total_reduced_size / 1024 / 1024 / 1024							,			"""GB""" )

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=sorted(lowercase )
   _remove_dup_initializers_from_model(lowercase							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""optimized_""" + model_file_name
   SCREAMING_SNAKE_CASE_: str     			=os.path.join(lowercase							,			lowercase )
   onnx.save(lowercase							,			lowercase )

   return new_model








"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			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""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

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

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

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			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)



