


"""simple docstring"""







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		Optional[int]											=      ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  a						:		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_	)

  a						:		Dict											=      model.state_dict()

  def to_tf_var_name(A_	):
    for patt, repl in iter(A_	):
      a						:		List[Any]											=      name.replace(A_  ,       A_	)
    return F'''bert/{name}'''

  def create_tf_var(A_  ,       A_  ,       A_	):
    a						:		Union[str, Any]											=      tf.dtypes.as_dtype(tensor.dtype	)
    a						:		Union[str, 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:
      a						:		Dict											=      to_tf_var_name(A_	)
      a						:		Optional[int]											=      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose	):
        a						:		int											=      torch_tensor.T
      a						:		Optional[int]											=      create_tf_var(tensor=A_  ,       name=A_  ,       session=A_	)
      tf.keras.backend.set_value(A_  ,       A_	)
      a						:		List[str]											=      session.run(A_	)
      print(F'''Successfully created {tf_name}: {np.allclose(A_  ,       A_	)}'''	)

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


def 					lowercase      (    A_=None	)->				Optional[Any]:
  '''simple docstring'''







  a						:		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"	)
  a						:		Any											=      parser.parse_args(A_	)

  a						:		Any											=      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()










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






