


'''simple docstring'''






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__A   :    Any											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Union[str, Any]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Union[str, Any]     =  os.path.abspath(A__   )
  logger.info(f'Converting TensorFlow checkpoint from {tf_path}'   )
  # Load weights from TF model
  lowerCAmelCase_     : Any     =  tf.train.list_variables(A__   )
  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_     : Dict     =  []
  lowerCAmelCase_     : Any     =  []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    lowerCAmelCase_     : Optional[Any]     =  full_name.split("""/"""   )
    if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
      logger.info(f'Skipping non-model layer {full_name}'   )
      continue
    if "optimizer" in full_name:
      logger.info(f'Skipping optimization layer {full_name}'   )
      continue
    if name[0] == "model":
      # ignore initial 'model'
      lowerCAmelCase_     : str     =  name[1:]
    # figure out how many levels deep the name is
    lowerCAmelCase_     : Tuple     =  0
    for _name in name:
      if _name.startswith("""layer_with_weights"""   ):
        depth += 1
      else:
        break
    layer_depth.append(A__   )
    # read data
    lowerCAmelCase_     : int     =  tf.train.load_variable(A__				,       A__   )
    names.append("""/""".join(A__   )   )
    arrays.append(A__   )
  logger.info(f'Read a total of {len(A__   ):,} layers'   )

  # Sanity check
  if len(set(A__   )   ) != 1:
    raise ValueError(f'Found layer names with different depths (layer depth {list(set(A__   )   )})'   )
  lowerCAmelCase_     : Tuple     =  list(set(A__   )   )[0]
  if layer_depth != 1:
    raise ValueError(
        """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
        """ heads."""   )

  # convert layers
  logger.info("""Converting weights..."""   )
  for full_name, array in zip(A__				,       A__   ):
    lowerCAmelCase_     : Optional[Any]     =  full_name.split("""/"""   )
    lowerCAmelCase_     : Tuple     =  model
    lowerCAmelCase_     : List[Any]     =  []
    for i, m_name in enumerate(A__   ):
      if m_name == ".ATTRIBUTES":
        # variable names end with .ATTRIBUTES/VARIABLE_VALUE
        break
      if m_name.startswith("""layer_with_weights"""   ):
        lowerCAmelCase_     : List[Any]     =  int(m_name.split("""-"""   )[-1]   )
        if layer_num <= 2:
          # embedding layers
          # layer_num 0: word_embeddings
          # layer_num 1: position_embeddings
          # layer_num 2: token_type_embeddings
          continue
        elif layer_num == 3:
          # embedding LayerNorm
          trace.extend(["""embeddings""", """LayerNorm"""]   )
          lowerCAmelCase_     : Optional[Any]     =  getattr(A__				,       """embeddings"""   )
          lowerCAmelCase_     : str     =  getattr(A__				,       """LayerNorm"""   )
        elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
          # encoder layers
          trace.extend(["""encoder""", """layer""", str(layer_num - 4   )]   )
          lowerCAmelCase_     : Tuple     =  getattr(A__				,       """encoder"""   )
          lowerCAmelCase_     : List[str]     =  getattr(A__				,       """layer"""   )
          lowerCAmelCase_     : Optional[Any]     =  pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(["""pooler""", """dense"""]   )
          lowerCAmelCase_     : List[Any]     =  getattr(A__				,       """pooler"""   )
          lowerCAmelCase_     : List[str]     =  getattr(A__				,       """dense"""   )
      elif m_name == "embeddings":
        trace.append("""embeddings"""   )
        lowerCAmelCase_     : Optional[int]     =  getattr(A__				,       """embeddings"""   )
        if layer_num == 0:
          trace.append("""word_embeddings"""   )
          lowerCAmelCase_     : Any     =  getattr(A__				,       """word_embeddings"""   )
        elif layer_num == 1:
          trace.append("""position_embeddings"""   )
          lowerCAmelCase_     : List[Any]     =  getattr(A__				,       """position_embeddings"""   )
        elif layer_num == 2:
          trace.append("""token_type_embeddings"""   )
          lowerCAmelCase_     : Optional[Any]     =  getattr(A__				,       """token_type_embeddings"""   )
        else:
          raise ValueError(f'Unknown embedding layer with name {full_name}'   )
        trace.append("""weight"""   )
        lowerCAmelCase_     : Union[str, Any]     =  getattr(A__				,       """weight"""   )
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(["""attention""", """self"""]   )
        lowerCAmelCase_     : Tuple     =  getattr(A__				,       """attention"""   )
        lowerCAmelCase_     : str     =  getattr(A__				,       """self"""   )
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(["""attention""", """output""", """LayerNorm"""]   )
        lowerCAmelCase_     : List[Any]     =  getattr(A__				,       """attention"""   )
        lowerCAmelCase_     : Optional[int]     =  getattr(A__				,       """output"""   )
        lowerCAmelCase_     : Optional[int]     =  getattr(A__				,       """LayerNorm"""   )
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(["""attention""", """output""", """dense"""]   )
        lowerCAmelCase_     : str     =  getattr(A__				,       """attention"""   )
        lowerCAmelCase_     : List[str]     =  getattr(A__				,       """output"""   )
        lowerCAmelCase_     : Any     =  getattr(A__				,       """dense"""   )
      elif m_name == "_output_dense":
        # output dense
        trace.extend(["""output""", """dense"""]   )
        lowerCAmelCase_     : Union[str, Any]     =  getattr(A__				,       """output"""   )
        lowerCAmelCase_     : Any     =  getattr(A__				,       """dense"""   )
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(["""output""", """LayerNorm"""]   )
        lowerCAmelCase_     : int     =  getattr(A__				,       """output"""   )
        lowerCAmelCase_     : List[str]     =  getattr(A__				,       """LayerNorm"""   )
      elif m_name == "_key_dense":
        # attention key
        trace.append("""key"""   )
        lowerCAmelCase_     : str     =  getattr(A__				,       """key"""   )
      elif m_name == "_query_dense":
        # attention query
        trace.append("""query"""   )
        lowerCAmelCase_     : Dict     =  getattr(A__				,       """query"""   )
      elif m_name == "_value_dense":
        # attention value
        trace.append("""value"""   )
        lowerCAmelCase_     : Optional[Any]     =  getattr(A__				,       """value"""   )
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(["""intermediate""", """dense"""]   )
        lowerCAmelCase_     : Optional[int]     =  getattr(A__				,       """intermediate"""   )
        lowerCAmelCase_     : Optional[Any]     =  getattr(A__				,       """dense"""   )
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append("""output"""   )
        lowerCAmelCase_     : Dict     =  getattr(A__				,       """output"""   )
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append("""bias"""   )
        lowerCAmelCase_     : List[Any]     =  getattr(A__				,       """bias"""   )
      elif m_name in ["kernel", "gamma"]:
        trace.append("""weight"""   )
        lowerCAmelCase_     : Any     =  getattr(A__				,       """weight"""   )
      else:
        logger.warning(f'Ignored {m_name}'   )
        # for certain layers reshape is necessary
    lowerCAmelCase_     : List[Any]     =  """.""".join(A__   )
    if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""				,       A__   ) or re.match(
        R"""(\S+)\.attention\.output\.dense\.weight"""				,       A__   ):
      lowerCAmelCase_     : Optional[Any]     =  array.reshape(pointer.data.shape   )
    if "kernel" in full_name:
      lowerCAmelCase_     : int     =  array.transpose()
    if pointer.shape == array.shape:
      lowerCAmelCase_     : Any     =  torch.from_numpy(A__   )
    else:
      raise ValueError(
          f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
          f' {array.shape}'   )
    logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'   )
  return model







def 							UpperCamelCase_     (       A__   :  Any				,       A__   :  Union[str, Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  logger.info(f'Loading model based on config from {config_path}...'   )
  lowerCAmelCase_     : Dict     =  BertConfig.from_json_file(A__   )
  lowerCAmelCase_     : Any     =  BertModel(A__   )

  # Load weights from checkpoint
  logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'   )
  load_tfa_weights_in_bert(A__				,       A__				,       A__   )

  # Save pytorch-model
  logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'   )
  torch.save(model.state_dict()				,       A__   )


if __name__ == "__main__":
  __A   :    Optional[Any]											= argparse.ArgumentParser()
  parser.add_argument(
      "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
  )
  __A   :    Optional[int]											= parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''






from collections.abc import Callable






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Callable | None = None						)  ->    None:
      # Stores actual heap items.
      lowerCAmelCase_     : list     =  []
      # Stores indexes of each item for supporting updates and deletion.
      lowerCAmelCase_     : dict     =  {}
      # Stores current size of heap.
      lowerCAmelCase_     : List[Any]     =  0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      lowerCAmelCase_     : Tuple     =  key or (lambda lowerCamelCase						: x)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      return int((i - 1) / 2						) if i > 0 else None

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[str]     =  int(2 * i + 1						)
      return left if 0 < left < self.size else None

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[Any]     =  int(2 * i + 2						)
      return right if 0 < right < self.size else None

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  self.arr[j], self.arr[i]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    bool:
      return self.arr[i][1] < self.arr[j][1]

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self._left(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  self._right(lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  i

      if left is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : int     =  left
      if right is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  right

      return valid_parent

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Tuple     =  self._parent(lowerCamelCase						)
      while parent is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : str     =  parent, self._parent(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Optional[Any]     =  self._get_valid_parent(lowerCamelCase						)
      while valid_parent != index:
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  valid_parent, self._get_valid_parent(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : Dict     =  self.pos_map[item]
      lowerCAmelCase_     : Dict     =  [item, self.key(lowerCamelCase						)]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(lowerCamelCase						)
      self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : List[str]     =  self.pos_map[item]
      del self.pos_map[item]
      lowerCAmelCase_     : Tuple     =  self.arr[self.size - 1]
      lowerCAmelCase_     : List[str]     =  index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
        self._heapify_up(lowerCamelCase						)
        self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Any     =  len(self.arr						)
      if arr_len == self.size:
        self.arr.append([item, self.key(lowerCamelCase						)]						)
      else:
        lowerCAmelCase_     : str     =  [item, self.key(lowerCamelCase						)]
      lowerCAmelCase_     : Optional[Any]     =  self.size
      self.size += 1
      self._heapify_up(self.size - 1						)

    def __lowercase  (  self       :    str						)  ->    tuple | None:
      return self.arr[0] if self.size else None





    def __lowercase  (  self       :    Optional[Any]						)  ->    tuple | None:
      lowerCAmelCase_     : int     =  self.get_top()
      if top_item_tuple:
        self.delete_item(top_item_tuple[0]						)
      return top_item_tuple







def 							UpperCamelCase_     (       ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



