





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       :   Any				       =							logging.get_logger(__name__)







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

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

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

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


if __name__ == "__main__":
      __UpperCAmelCase       :   int				       =							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       :   Union[str, Any]				       =							parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase       :   Union[str, Any]				       =							{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase       :   str				       =							["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      __UpperCAmelCase       :   Optional[Any]				       =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






