
'''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__   :		Optional[Any] 		=logging.get_logger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     List[Any]												=		os.path.abspath(__SCREAMING_SNAKE_CASE )
  logger.info(F"Converting TensorFlow checkpoint from {tf_path}" )
  # Load weights from TF model
  __A		:     List[str]												=		tf.train.list_variables(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		[]
  __A		:     Dict												=		[]
  __A		:     Dict												=		[]
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    __A		:     Union[str, 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'
      __A		:     List[Any]												=		name[1:]
    # figure out how many levels deep the name is
    __A		:     int												=		0
    for _name in name:
      if _name.startswith("""layer_with_weights""" ):
        depth += 1
      else:
        break
    layer_depth.append(__SCREAMING_SNAKE_CASE )
    # read data
    __A		:     Dict												=		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 ) )})" )
  __A		:     Optional[int]												=		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 ):
    __A		:     Tuple												=		full_name.split("""/""" )
    __A		:     str												=		model
    __A		:     List[str]												=		[]
    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""" ):
        __A		:     str												=		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"""] )
          __A		:     Dict												=		getattr(__SCREAMING_SNAKE_CASE			,     """embeddings""" )
          __A		:     int												=		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 )] )
          __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """encoder""" )
          __A		:     Dict												=		getattr(__SCREAMING_SNAKE_CASE			,     """layer""" )
          __A		:     List[str]												=		pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(["""pooler""", """dense"""] )
          __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """pooler""" )
          __A		:     Any												=		getattr(__SCREAMING_SNAKE_CASE			,     """dense""" )
      elif m_name == "embeddings":
        trace.append("""embeddings""" )
        __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """embeddings""" )
        if layer_num == 0:
          trace.append("""word_embeddings""" )
          __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """word_embeddings""" )
        elif layer_num == 1:
          trace.append("""position_embeddings""" )
          __A		:     List[Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """position_embeddings""" )
        elif layer_num == 2:
          trace.append("""token_type_embeddings""" )
          __A		:     List[Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """token_type_embeddings""" )
        else:
          raise ValueError(F"Unknown embedding layer with name {full_name}" )
        trace.append("""weight""" )
        __A		:     int												=		getattr(__SCREAMING_SNAKE_CASE			,     """weight""" )
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(["""attention""", """self"""] )
        __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """attention""" )
        __A		:     Dict												=		getattr(__SCREAMING_SNAKE_CASE			,     """self""" )
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(["""attention""", """output""", """LayerNorm"""] )
        __A		:     Tuple												=		getattr(__SCREAMING_SNAKE_CASE			,     """attention""" )
        __A		:     Optional[int]												=		getattr(__SCREAMING_SNAKE_CASE			,     """output""" )
        __A		:     Dict												=		getattr(__SCREAMING_SNAKE_CASE			,     """LayerNorm""" )
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(["""attention""", """output""", """dense"""] )
        __A		:     Optional[Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """attention""" )
        __A		:     Tuple												=		getattr(__SCREAMING_SNAKE_CASE			,     """output""" )
        __A		:     Union[str, Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """dense""" )
      elif m_name == "_output_dense":
        # output dense
        trace.extend(["""output""", """dense"""] )
        __A		:     Union[str, Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """output""" )
        __A		:     Optional[int]												=		getattr(__SCREAMING_SNAKE_CASE			,     """dense""" )
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(["""output""", """LayerNorm"""] )
        __A		:     Optional[Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """output""" )
        __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """LayerNorm""" )
      elif m_name == "_key_dense":
        # attention key
        trace.append("""key""" )
        __A		:     Union[str, Any]												=		getattr(__SCREAMING_SNAKE_CASE			,     """key""" )
      elif m_name == "_query_dense":
        # attention query
        trace.append("""query""" )
        __A		:     Any												=		getattr(__SCREAMING_SNAKE_CASE			,     """query""" )
      elif m_name == "_value_dense":
        # attention value
        trace.append("""value""" )
        __A		:     str												=		getattr(__SCREAMING_SNAKE_CASE			,     """value""" )
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(["""intermediate""", """dense"""] )
        __A		:     int												=		getattr(__SCREAMING_SNAKE_CASE			,     """intermediate""" )
        __A		:     str												=		getattr(__SCREAMING_SNAKE_CASE			,     """dense""" )
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append("""output""" )
        __A		:     Optional[int]												=		getattr(__SCREAMING_SNAKE_CASE			,     """output""" )
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append("""bias""" )
        __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     """bias""" )
      elif m_name in ["kernel", "gamma"]:
        trace.append("""weight""" )
        __A		:     str												=		getattr(__SCREAMING_SNAKE_CASE			,     """weight""" )
      else:
        logger.warning(F"Ignored {m_name}" )
        # for certain layers reshape is necessary
    __A		:     int												=		""".""".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 ):
      __A		:     Tuple												=		array.reshape(pointer.data.shape )
    if "kernel" in full_name:
      __A		:     Dict												=		array.transpose()
    if pointer.shape == array.shape:
      __A		:     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						:  Any			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Any )				->       Optional[int]:





  """simple docstring"""

  logger.info(F"Loading model based on config from {config_path}..." )
  __A		:     Union[str, Any]												=		BertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  __A		:     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__":
     A__   :		Union[str, 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__   :		str 		=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 Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



