





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase		):







 '''simple docstring'''
 lowercase_		=      BioGptTokenizer
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				str        =[
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "w</w>",
       "r</w>",
       "t</w>",
       "lo",
       "low",
       "er</w>",
       "low</w>",
       "lowest</w>",
       "newer</w>",
       "wider</w>",
       "<unk>",
   ]
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				Optional[Any]        =["l o 123", "lo w 1456", "e r</w> 1789", ""]

   lowerCamelCase__:				List[Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				Any        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w") as fp:
     fp.write(json.dumps(UpperCAmelCase_))
   with open(self.merges_file	,			"w") as fp:
     fp.write("\n".join(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Union[str, Any])	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        ="lower newer"
   lowerCamelCase__:				Optional[int]        ="lower newer"
   return input_text, output_text

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   lowerCamelCase__:				str        =BioGptTokenizer(self.vocab_file	,			self.merges_file)

   lowerCamelCase__:				Union[str, Any]        ="lower"
   lowerCamelCase__:				Optional[Any]        =["low", "er</w>"]
   lowerCamelCase__:				Optional[Any]        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				str        =tokens + ["<unk>"]
   lowerCamelCase__:				Any        =[14, 15, 20]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =BioGptTokenizer.from_pretrained("microsoft/biogpt")

   lowerCamelCase__:				str        =tokenizer.encode("sequence builders"	,			add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =tokenizer.encode("multi-sequence build"	,			add_special_tokens=UpperCAmelCase_)

   lowerCamelCase__:				int        =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
   lowerCamelCase__:				Any        =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_	,			UpperCAmelCase_)

   self.assertTrue(encoded_sentence == [2] + text)
   self.assertTrue(encoded_pair == [2] + text + [2] + text_a)








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				int        =("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  lowerCamelCase__:				Any        =(
      ("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(__a		):
    os.makedirs(__a		)

  lowerCamelCase__:				Tuple        =model.state_dict()

  def to_tf_var_name(__a		):
    for patt, repl in iter(__a		):
      lowerCamelCase__:				Tuple        =name.replace(__a     ,   __a		)
    return F"""bert/{name}"""

  def create_tf_var(__a     ,   __a     ,   __a		):
    lowerCamelCase__:				Union[str, Any]        =tf.dtypes.as_dtype(tensor.dtype		)
    lowerCamelCase__:				Any        =tf.get_variable(dtype=__a     ,   shape=tensor.shape     ,   name=__a     ,   initializer=tf.zeros_initializer()		)
    session.run(tf.variables_initializer([tf_var]		)		)
    session.run(__a		)
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowerCamelCase__:				Tuple        =to_tf_var_name(__a		)
      lowerCamelCase__:				str        =state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        lowerCamelCase__:				List[Any]        =torch_tensor.T
      lowerCamelCase__:				Optional[int]        =create_tf_var(tensor=__a     ,   name=__a     ,   session=__a		)
      tf.keras.backend.set_value(__a     ,   __a		)
      lowerCamelCase__:				Union[str, Any]        =session.run(__a		)
      print(F"""Successfully created {tf_name}: {np.allclose(__a     ,   __a		)}"""		)

    lowerCamelCase__:				List[Any]        =tf.train.Saver(tf.trainable_variables()		)
    saver.save(__a     ,   os.path.join(__a     ,   model_name.replace("-"     ,   "_"		) + ".ckpt"		)		)





def 					lowerCAmelCase_			(	__a=None		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =argparse.ArgumentParser()
  parser.add_argument("--model_name"     ,   type=__a     ,   required=__a     ,   help="model name e.g. bert-base-uncased"		)
  parser.add_argument(
      "--cache_dir"     ,   type=__a     ,   default=__a     ,   required=__a     ,   help="Directory containing pytorch model"		)
  parser.add_argument("--pytorch_model_path"     ,   type=__a     ,   required=__a     ,   help="/path/to/<pytorch-model-name>.bin"		)
  parser.add_argument("--tf_cache_dir"     ,   type=__a     ,   required=__a     ,   help="Directory in which to save tensorflow model"		)
  lowerCamelCase__:				Optional[Any]        =parser.parse_args(__a		)

  lowerCamelCase__:				Dict        =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=__a     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name		)


if __name__ == "__main__":
     main()

