



"""simple docstring"""





import os

import numpy
import onnx
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				a.name
      UpperCAmelCase_     					=				b.name

      UpperCAmelCase_     					=				""
      UpperCAmelCase_     					=				""

      UpperCAmelCase_     					=				a == b

      UpperCAmelCase_     					=				name_a
      UpperCAmelCase_     					=				name_b

      return res
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for i, input_name in enumerate(node_proto.input ):
            if input_name == name:
                  node_proto.input.insert(__UpperCAmelCase      ,     __UpperCAmelCase )
                  node_proto.input.pop(i + 1 )

      if node_proto.op_type == "If":
            _graph_replace_input_with(node_proto.attribute[0].g      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
            _graph_replace_input_with(node_proto.attribute[1].g      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
      if node_proto.op_type == "Loop":
            _graph_replace_input_with(node_proto.attribute[0].g      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for n in graph_proto.node:
            _node_replace_input_with(__UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				list(model.graph.initializer )
      UpperCAmelCase_     					=				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

            UpperCAmelCase_     					=				inits[i].name
            UpperCAmelCase_     					=				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      ,     __UpperCAmelCase      ,     __UpperCAmelCase )


def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				os.path.dirname(__UpperCAmelCase )
      UpperCAmelCase_     					=				os.path.basename(__UpperCAmelCase )

      UpperCAmelCase_     					=				onnx.load(os.path.join(__UpperCAmelCase      ,     __UpperCAmelCase ) )

      UpperCAmelCase_     					=				list(model.graph.initializer )

      UpperCAmelCase_     					=				set()
      UpperCAmelCase_     					=				{}
      UpperCAmelCase_     					=				[]

      UpperCAmelCase_     					=				0

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

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

                        UpperCAmelCase_     					=				inits[j].data_type
                        UpperCAmelCase_     					=				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: "      ,     __UpperCAmelCase )
                        total_reduced_size += mem_size

                        UpperCAmelCase_     					=				inits[i].name
                        UpperCAmelCase_     					=				inits[j].name

                        if name_i in dup_map:
                              dup_map[name_i].append(__UpperCAmelCase )
                        else:
                              UpperCAmelCase_     					=				[name_j]
                        ind_to_replace.append((j, i) )

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

      UpperCAmelCase_     					=				sorted(__UpperCAmelCase )
      _remove_dup_initializers_from_model(__UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase )

      UpperCAmelCase_     					=				"optimized_" + model_file_name
      UpperCAmelCase_     					=				os.path.join(__UpperCAmelCase      ,     __UpperCAmelCase )
      onnx.save(__UpperCAmelCase      ,     __UpperCAmelCase )

      return new_model









import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 a__												= (
     ('''layer.''', '''layer_'''),
     ('''word_embeddings.weight''', '''word_embeddings'''),
     ('''position_embeddings.weight''', '''position_embeddings'''),
     ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
     ('''.''', '''/'''),
     ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
     ('''LayerNorm/bias''', '''LayerNorm/beta'''),
     ('''weight''', '''kernel'''),
 )

 if not os.path.isdir(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   a__												= state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose       ):
    a__												= torch_tensor.T
   a__												= create_tf_var(tensor=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 a__												= BertModel.from_pretrained(
     pretrained_model_name_or_path=args.model_name							,  state_dict=torch.load(args.pytorch_model_path       )							,  cache_dir=args.cache_dir							,  )

 convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





