



"""simple docstring"""






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 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Any:
  def get_masked_lm_array(lowerCAmelCase_	):
    _snake_case   	=      f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    _snake_case   	=      tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_	)

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

    return torch.from_numpy(lowerCAmelCase_	)

  def get_encoder_array(lowerCAmelCase_	):
    _snake_case   	=      f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    _snake_case   	=      tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_	)

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

    return torch.from_numpy(lowerCAmelCase_	)

  def get_encoder_layer_array(lowerCAmelCase_  ,						lowerCAmelCase_	):
    _snake_case   	=      f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    _snake_case   	=      tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_	)

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

    return torch.from_numpy(lowerCAmelCase_	)

  def get_encoder_attention_layer_array(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	):
    _snake_case   	=      f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    _snake_case   	=      tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_	)
    _snake_case   	=      array.reshape(lowerCAmelCase_	)

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

    return torch.from_numpy(lowerCAmelCase_	)

  print(f"""Loading model based on config from {config_path}..."""	)
  _snake_case   	=      BertConfig.from_json_file(lowerCAmelCase_	)
  _snake_case   	=      BertForMaskedLM(lowerCAmelCase_	)

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

    # Self-attention
    _snake_case   	=      layer.attention.self

    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_query_dense/kernel'''  ,						self_attn.query.weight.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_query_dense/bias'''  ,						self_attn.query.bias.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_key_dense/kernel'''  ,						self_attn.key.weight.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_key_dense/bias'''  ,						self_attn.key.bias.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_value_dense/kernel'''  ,						self_attn.value.weight.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_value_dense/bias'''  ,						self_attn.value.bias.data.shape	)

    # Self-attention Output
    _snake_case   	=      layer.attention.output

    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_output_dense/kernel'''  ,						self_output.dense.weight.data.shape	)
    _snake_case   	=      get_encoder_attention_layer_array(
        lowerCAmelCase_  ,						'''_output_dense/bias'''  ,						self_output.dense.bias.data.shape	)

    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_attention_layer_norm/gamma'''	)
    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_attention_layer_norm/beta'''	)

    # Intermediate
    _snake_case   	=      layer.intermediate

    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_intermediate_dense/kernel'''	)
    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_intermediate_dense/bias'''	)

    # Output
    _snake_case   	=      layer.output

    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_output_dense/kernel'''	)
    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_output_dense/bias'''	)

    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_output_layer_norm/gamma'''	)
    _snake_case   	=      get_encoder_layer_array(lowerCAmelCase_  ,						'''_output_layer_norm/beta'''	)

  # Embeddings
  _snake_case   	=      get_encoder_array('''_position_embedding_layer/embeddings'''	)
  _snake_case   	=      get_encoder_array('''_type_embedding_layer/embeddings'''	)
  _snake_case   	=      get_encoder_array('''_embedding_norm_layer/gamma'''	)
  _snake_case   	=      get_encoder_array('''_embedding_norm_layer/beta'''	)

  # LM Head
  _snake_case   	=      model.cls.predictions.transform

  _snake_case   	=      get_masked_lm_array('''dense/kernel'''	)
  _snake_case   	=      get_masked_lm_array('''dense/bias'''	)

  _snake_case   	=      get_masked_lm_array('''layer_norm/gamma'''	)
  _snake_case   	=      get_masked_lm_array('''layer_norm/beta'''	)

  _snake_case   	=      get_masked_lm_array('''embedding_table'''	)

  # Pooling
  _snake_case   	=      BertPooler(config=lowerCAmelCase_	)
  _snake_case   	=      get_encoder_array('''_pooler_layer/kernel'''	)
  _snake_case   	=      get_encoder_array('''_pooler_layer/bias'''	)

  # Export final model
  model.save_pretrained(lowerCAmelCase_	)

  # Integration test - should load without any errors ;)
  _snake_case   	=      BertForMaskedLM.from_pretrained(lowerCAmelCase_	)
  print(new_model.eval()	)

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


if __name__ == "__main__":
     snake_case   					=				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.''',
     )
     snake_case   					=				parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










"""simple docstring"""






import operator




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None	)						->				list:
  _snake_case   	=      operator.lt if reverse else operator.gt
  _snake_case   	=      solution or []

  if not arr:
    return solution

  _snake_case   	=      [arr.pop(0	)]
  for i, item in enumerate(lowerCAmelCase_	):
    if _operator(lowerCAmelCase_  ,						sublist[-1]	):
      sublist.append(lowerCAmelCase_	)
      arr.pop(lowerCAmelCase_	)

    #  merging sublist into solution list
  if not solution:
    solution.extend(lowerCAmelCase_	)
  else:
    while sublist:
      _snake_case   	=      sublist.pop(0	)
      for i, xx in enumerate(lowerCAmelCase_	):
        if not _operator(lowerCAmelCase_  ,						lowerCAmelCase_	):
          solution.insert(lowerCAmelCase_  ,						lowerCAmelCase_	)
          break
      else:
        solution.append(lowerCAmelCase_	)

  strand_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





