'''simple docstring'''





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def      lowerCAmelCase_    ( snake_case_	:			BertModel ,   snake_case_	:			str ,   snake_case_	:			str      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     UpperCAmelCase_	   =       (
         ("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(snake_case_      ):
          os.makedirs(snake_case_      )

     UpperCAmelCase_	   =       model.state_dict()

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

     def create_tf_var(snake_case_	:			np.ndarray ,   snake_case_	:			str ,   snake_case_	:			tf.Session      ):
          UpperCAmelCase_	   =       tf.dtypes.as_dtype(tensor.dtype      )
          UpperCAmelCase_	   =       tf.get_variable(dtype=snake_case_ ,   shape=tensor.shape ,   name=snake_case_ ,   initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(snake_case_      )
          return tf_var

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

          UpperCAmelCase_	   =       tf.train.Saver(tf.trainable_variables()      )
          saver.save(snake_case_ ,   os.path.join(snake_case_ ,   model_name.replace("-" ,   "_"      ) + ".ckpt"      )      )





def      lowerCAmelCase_    ( snake_case_	:			Tuple=None      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser()
     parser.add_argument("--model_name" ,   type=snake_case_ ,   required=snake_case_ ,   help="model name e.g. bert-base-uncased"      )
     parser.add_argument(
         "--cache_dir" ,   type=snake_case_ ,   default=snake_case_ ,   required=snake_case_ ,   help="Directory containing pytorch model"      )
     parser.add_argument("--pytorch_model_path" ,   type=snake_case_ ,   required=snake_case_ ,   help="/path/to/<pytorch-model-name>.bin"      )
     parser.add_argument("--tf_cache_dir" ,   type=snake_case_ ,   required=snake_case_ ,   help="Directory in which to save tensorflow model"      )
     UpperCAmelCase_	   =       parser.parse_args(snake_case_      )

     UpperCAmelCase_	   =       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=snake_case_ ,   ckpt_dir=args.tf_cache_dir ,   model_name=args.model_name      )


if __name__ == "__main__":
    main()





'''simple docstring'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''simple docstring'''




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

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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     for n in graph_proto.node:
          _node_replace_input_with(snake_case_ ,   snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




     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 ,   snake_case_ ,   snake_case_      )






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

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

     UpperCAmelCase_	   =       list(model.graph.initializer      )

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

     UpperCAmelCase_	   =       0

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

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

                    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: " ,   snake_case_      )
                    total_reduced_size += mem_size

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

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

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

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

     return new_model




