"""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()
UpperCAmelCase__  		=						logging.get_logger(__name__)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      os.path.abspath(lowercase		)
  logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''		)
  # Load weights from TF model
  _UpperCAmelCase						 =      tf.train.list_variables(lowercase		)
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    _UpperCAmelCase						 =      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'
      _UpperCAmelCase						 =      name[1:]
    # figure out how many levels deep the name is
    _UpperCAmelCase						 =      0
    for _name in name:
      if _name.startswith("""layer_with_weights"""		):
        depth += 1
      else:
        break
    layer_depth.append(lowercase		)
    # read data
    _UpperCAmelCase						 =      tf.train.load_variable(lowercase   ,lowercase		)
    names.append("""/""".join(lowercase		)		)
    arrays.append(lowercase		)
  logger.info(f'''Read a total of {len(lowercase		):,} layers'''		)

  # Sanity check
  if len(set(lowercase		)		) != 1:
    raise ValueError(f'''Found layer names with different depths (layer depth {list(set(lowercase		)		)})'''		)
  _UpperCAmelCase						 =      list(set(lowercase		)		)[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(lowercase   ,lowercase		):
    _UpperCAmelCase						 =      full_name.split("""/"""		)
    _UpperCAmelCase						 =      model
    _UpperCAmelCase						 =      []
    for i, m_name in enumerate(lowercase		):
      if m_name == ".ATTRIBUTES":
        # variable names end with .ATTRIBUTES/VARIABLE_VALUE
        break
      if m_name.startswith("""layer_with_weights"""		):
        _UpperCAmelCase						 =      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"""]		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""embeddings"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""LayerNorm"""		)
        elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
          # encoder layers
          trace.extend(["""encoder""", """layer""", str(layer_num - 4		)]		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""encoder"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""layer"""		)
          _UpperCAmelCase						 =      pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(["""pooler""", """dense"""]		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""pooler"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""dense"""		)
      elif m_name == "embeddings":
        trace.append("""embeddings"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""embeddings"""		)
        if layer_num == 0:
          trace.append("""word_embeddings"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""word_embeddings"""		)
        elif layer_num == 1:
          trace.append("""position_embeddings"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""position_embeddings"""		)
        elif layer_num == 2:
          trace.append("""token_type_embeddings"""		)
          _UpperCAmelCase						 =      getattr(lowercase   ,"""token_type_embeddings"""		)
        else:
          raise ValueError(f'''Unknown embedding layer with name {full_name}'''		)
        trace.append("""weight"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""weight"""		)
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(["""attention""", """self"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""attention"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""self"""		)
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(["""attention""", """output""", """LayerNorm"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""attention"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""output"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""LayerNorm"""		)
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(["""attention""", """output""", """dense"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""attention"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""output"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""dense"""		)
      elif m_name == "_output_dense":
        # output dense
        trace.extend(["""output""", """dense"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""output"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""dense"""		)
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(["""output""", """LayerNorm"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""output"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""LayerNorm"""		)
      elif m_name == "_key_dense":
        # attention key
        trace.append("""key"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""key"""		)
      elif m_name == "_query_dense":
        # attention query
        trace.append("""query"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""query"""		)
      elif m_name == "_value_dense":
        # attention value
        trace.append("""value"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""value"""		)
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(["""intermediate""", """dense"""]		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""intermediate"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""dense"""		)
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append("""output"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""output"""		)
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append("""bias"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""bias"""		)
      elif m_name in ["kernel", "gamma"]:
        trace.append("""weight"""		)
        _UpperCAmelCase						 =      getattr(lowercase   ,"""weight"""		)
      else:
        logger.warning(f'''Ignored {m_name}'''		)
        # for certain layers reshape is necessary
    _UpperCAmelCase						 =      """.""".join(lowercase		)
    if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""   ,lowercase		) or re.match(
        R"""(\S+)\.attention\.output\.dense\.weight"""   ,lowercase		):
      _UpperCAmelCase						 =      array.reshape(pointer.data.shape		)
    if "kernel" in full_name:
      _UpperCAmelCase						 =      array.transpose()
    if pointer.shape == array.shape:
      _UpperCAmelCase						 =      torch.from_numpy(lowercase		)
    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					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  logger.info(f'''Loading model based on config from {config_path}...'''		)
  _UpperCAmelCase						 =      BertConfig.from_json_file(lowercase		)
  _UpperCAmelCase						 =      BertModel(lowercase		)

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

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


if __name__ == "__main__":
    UpperCAmelCase__  		=						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).""",
    )
    UpperCAmelCase__  		=						parser.parse_args()
    convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()
