






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					str		,  _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 def get_masked_lm_array(_a  :					str	):
  snake_case__  :    List[Any]							       =f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  snake_case__  :    Any							       =tf.train.load_variable(_a		,  _a	)

  if "kernel" in name:
   snake_case__  :    Dict							       =array.transpose()

  return torch.from_numpy(_a	)

 def get_encoder_array(_a  :					str	):
  snake_case__  :    List[str]							       =f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  snake_case__  :    List[str]							       =tf.train.load_variable(_a		,  _a	)

  if "kernel" in name:
   snake_case__  :    str							       =array.transpose()

  return torch.from_numpy(_a	)

 def get_encoder_layer_array(_a  :					int		,  _a  :					str	):
  snake_case__  :    Any							       =f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  snake_case__  :    List[Any]							       =tf.train.load_variable(_a		,  _a	)

  if "kernel" in name:
   snake_case__  :    List[str]							       =array.transpose()

  return torch.from_numpy(_a	)

 def get_encoder_attention_layer_array(_a  :					int		,  _a  :					str		,  _a  :					List[str]	):
  snake_case__  :    Optional[Any]							       =f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  snake_case__  :    Union[str, Any]							       =tf.train.load_variable(_a		,  _a	)
  snake_case__  :    Optional[int]							       =array.reshape(_a	)

  if "kernel" in name:
   snake_case__  :    int							       =array.transpose()

  return torch.from_numpy(_a	)

 print(f"Loading model based on config from {config_path}..."	)
 snake_case__  :    List[str]							       =BertConfig.from_json_file(_a	)
 snake_case__  :    Union[str, Any]							       =BertForMaskedLM(_a	)

 # Layers
 for layer_index in range(0		,  config.num_hidden_layers	):
  snake_case__  :    BertLayer							       =model.bert.encoder.layer[layer_index]

  # Self-attention
  snake_case__  :    BertSelfAttention							       =layer.attention.self

  snake_case__  :    Dict							       =get_encoder_attention_layer_array(
      _a		,  """_query_dense/kernel"""		,  self_attn.query.weight.data.shape	)
  snake_case__  :    Tuple							       =get_encoder_attention_layer_array(
      _a		,  """_query_dense/bias"""		,  self_attn.query.bias.data.shape	)
  snake_case__  :    Tuple							       =get_encoder_attention_layer_array(
      _a		,  """_key_dense/kernel"""		,  self_attn.key.weight.data.shape	)
  snake_case__  :    Union[str, Any]							       =get_encoder_attention_layer_array(
      _a		,  """_key_dense/bias"""		,  self_attn.key.bias.data.shape	)
  snake_case__  :    Any							       =get_encoder_attention_layer_array(
      _a		,  """_value_dense/kernel"""		,  self_attn.value.weight.data.shape	)
  snake_case__  :    Optional[int]							       =get_encoder_attention_layer_array(
      _a		,  """_value_dense/bias"""		,  self_attn.value.bias.data.shape	)

  # Self-attention Output
  snake_case__  :    BertSelfOutput							       =layer.attention.output

  snake_case__  :    Optional[Any]							       =get_encoder_attention_layer_array(
      _a		,  """_output_dense/kernel"""		,  self_output.dense.weight.data.shape	)
  snake_case__  :    int							       =get_encoder_attention_layer_array(
      _a		,  """_output_dense/bias"""		,  self_output.dense.bias.data.shape	)

  snake_case__  :    Any							       =get_encoder_layer_array(_a		,  """_attention_layer_norm/gamma"""	)
  snake_case__  :    List[str]							       =get_encoder_layer_array(_a		,  """_attention_layer_norm/beta"""	)

  # Intermediate
  snake_case__  :    BertIntermediate							       =layer.intermediate

  snake_case__  :    List[str]							       =get_encoder_layer_array(_a		,  """_intermediate_dense/kernel"""	)
  snake_case__  :    Optional[Any]							       =get_encoder_layer_array(_a		,  """_intermediate_dense/bias"""	)

  # Output
  snake_case__  :    BertOutput							       =layer.output

  snake_case__  :    Dict							       =get_encoder_layer_array(_a		,  """_output_dense/kernel"""	)
  snake_case__  :    Tuple							       =get_encoder_layer_array(_a		,  """_output_dense/bias"""	)

  snake_case__  :    Optional[int]							       =get_encoder_layer_array(_a		,  """_output_layer_norm/gamma"""	)
  snake_case__  :    int							       =get_encoder_layer_array(_a		,  """_output_layer_norm/beta"""	)

 # Embeddings
 snake_case__  :    Optional[Any]							       =get_encoder_array("""_position_embedding_layer/embeddings"""	)
 snake_case__  :    Optional[Any]							       =get_encoder_array("""_type_embedding_layer/embeddings"""	)
 snake_case__  :    str							       =get_encoder_array("""_embedding_norm_layer/gamma"""	)
 snake_case__  :    Union[str, Any]							       =get_encoder_array("""_embedding_norm_layer/beta"""	)

 # LM Head
 snake_case__  :    Dict							       =model.cls.predictions.transform

 snake_case__  :    Optional[int]							       =get_masked_lm_array("""dense/kernel"""	)
 snake_case__  :    Dict							       =get_masked_lm_array("""dense/bias"""	)

 snake_case__  :    str							       =get_masked_lm_array("""layer_norm/gamma"""	)
 snake_case__  :    Any							       =get_masked_lm_array("""layer_norm/beta"""	)

 snake_case__  :    Tuple							       =get_masked_lm_array("""embedding_table"""	)

 # Pooling
 snake_case__  :    Union[str, Any]							       =BertPooler(config=_a	)
 snake_case__  :    BertPooler							       =get_encoder_array("""_pooler_layer/kernel"""	)
 snake_case__  :    BertPooler							       =get_encoder_array("""_pooler_layer/bias"""	)

 # Export final model
 model.save_pretrained(_a	)

 # Integration test - should load without any errors ;)
 snake_case__  :    str							       =BertForMaskedLM.from_pretrained(_a	)
 print(new_model.eval()	)

 print("""Model conversion was done sucessfully!"""	)


if __name__ == "__main__":
       __lowerCamelCase	:		Any										=   argparse.ArgumentParser()
       parser.add_argument(
           """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
       )
       parser.add_argument(
           """--bert_config_file""",
           type=str,
           required=True,
           help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""",
           type=str,
           required=True,
           help="""Path to the output PyTorch model.""",
       )
       __lowerCamelCase	:		int										=   parser.parse_args()
       convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					int		,  _a  :					Any	):





 '''simple docstring'''







 if height >= 1:
  move_tower(height - 1		,  _a		,  _a		,  _a	)
  move_disk(_a		,  _a	)
  move_tower(height - 1		,  _a		,  _a		,  _a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					str	):





 '''simple docstring'''







 print("""moving disk from"""		,  _a		,  """to"""		,  _a	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =int(input("""Height of hanoi: """	).strip()	)
 move_tower(_a		,  """A"""		,  """B"""		,  """C"""	)


if __name__ == "__main__":
       main()





