
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  __magic_name__								=			(
      ('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(lowerCAmelCase_   ):
    os.makedirs(lowerCAmelCase_   )

  __magic_name__								=			model.state_dict()

  def to_tf_var_name(A_   ):
    for patt, repl in iter(lowerCAmelCase_   ):
      __magic_name__								=			name.replace(lowerCAmelCase_,	lowerCAmelCase_   )
    return f'''bert/{name}'''

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

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      __magic_name__								=			to_tf_var_name(lowerCAmelCase_   )
      __magic_name__								=			state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose   ):
        __magic_name__								=			torch_tensor.T
      __magic_name__								=			create_tf_var(tensor=lowerCAmelCase_,	name=lowerCAmelCase_,	session=lowerCAmelCase_   )
      tf.keras.backend.set_value(lowerCAmelCase_,	lowerCAmelCase_   )
      __magic_name__								=			session.run(lowerCAmelCase_   )
      print(f'''Successfully created {tf_name}: {np.allclose(lowerCAmelCase_,	lowerCAmelCase_   )}'''   )

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

def a__       (			A_=None   ):






  '''simple docstring'''





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

  __magic_name__								=			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=lowerCAmelCase_,	ckpt_dir=args.tf_cache_dir,	model_name=args.model_name   )


if __name__ == "__main__":
    main()






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Optional[int]      ,*_UpperCAmelCase   :			List[Any]      ,**_UpperCAmelCase   :			Union[str, Any]						):
									warnings.warn(
									    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use GLPNImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




