

'''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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     int:
   def get_masked_lm_array(snake_case					):
      _lowercase      :				Tuple      					=       F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      _lowercase      :				Union[str, Any]      					=       tf.train.load_variable(snake_case		,							snake_case					)

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

      return torch.from_numpy(snake_case					)

   def get_encoder_array(snake_case					):
      _lowercase      :				Union[str, Any]      					=       F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      _lowercase      :				Union[str, Any]      					=       tf.train.load_variable(snake_case		,							snake_case					)

      if "kernel" in name:
         _lowercase      :				Optional[Any]      					=       array.transpose()

      return torch.from_numpy(snake_case					)

   def get_encoder_layer_array(snake_case		,							snake_case					):
      _lowercase      :				Tuple      					=       F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      _lowercase      :				List[str]      					=       tf.train.load_variable(snake_case		,							snake_case					)

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

      return torch.from_numpy(snake_case					)

   def get_encoder_attention_layer_array(snake_case		,							snake_case		,							snake_case					):
      _lowercase      :				Tuple      					=       F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      _lowercase      :				int      					=       tf.train.load_variable(snake_case		,							snake_case					)
      _lowercase      :				List[str]      					=       array.reshape(snake_case					)

      if "kernel" in name:
         _lowercase      :				Optional[Any]      					=       array.transpose()

      return torch.from_numpy(snake_case					)

   print(F'''Loading model based on config from {config_path}...'''					)
   _lowercase      :				Dict      					=       BertConfig.from_json_file(snake_case					)
   _lowercase      :				str      					=       BertForMaskedLM(snake_case					)

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

      # Self-attention
      _lowercase      :				BertSelfAttention      					=       layer.attention.self

      _lowercase      :				str      					=       get_encoder_attention_layer_array(
          snake_case		,							"_query_dense/kernel"		,							self_attn.query.weight.data.shape					)
      _lowercase      :				Dict      					=       get_encoder_attention_layer_array(
          snake_case		,							"_query_dense/bias"		,							self_attn.query.bias.data.shape					)
      _lowercase      :				List[str]      					=       get_encoder_attention_layer_array(
          snake_case		,							"_key_dense/kernel"		,							self_attn.key.weight.data.shape					)
      _lowercase      :				List[str]      					=       get_encoder_attention_layer_array(
          snake_case		,							"_key_dense/bias"		,							self_attn.key.bias.data.shape					)
      _lowercase      :				List[str]      					=       get_encoder_attention_layer_array(
          snake_case		,							"_value_dense/kernel"		,							self_attn.value.weight.data.shape					)
      _lowercase      :				Union[str, Any]      					=       get_encoder_attention_layer_array(
          snake_case		,							"_value_dense/bias"		,							self_attn.value.bias.data.shape					)

      # Self-attention Output
      _lowercase      :				BertSelfOutput      					=       layer.attention.output

      _lowercase      :				int      					=       get_encoder_attention_layer_array(
          snake_case		,							"_output_dense/kernel"		,							self_output.dense.weight.data.shape					)
      _lowercase      :				Dict      					=       get_encoder_attention_layer_array(
          snake_case		,							"_output_dense/bias"		,							self_output.dense.bias.data.shape					)

      _lowercase      :				Dict      					=       get_encoder_layer_array(snake_case		,							"_attention_layer_norm/gamma"					)
      _lowercase      :				Optional[Any]      					=       get_encoder_layer_array(snake_case		,							"_attention_layer_norm/beta"					)

      # Intermediate
      _lowercase      :				BertIntermediate      					=       layer.intermediate

      _lowercase      :				int      					=       get_encoder_layer_array(snake_case		,							"_intermediate_dense/kernel"					)
      _lowercase      :				str      					=       get_encoder_layer_array(snake_case		,							"_intermediate_dense/bias"					)

      # Output
      _lowercase      :				BertOutput      					=       layer.output

      _lowercase      :				Optional[int]      					=       get_encoder_layer_array(snake_case		,							"_output_dense/kernel"					)
      _lowercase      :				int      					=       get_encoder_layer_array(snake_case		,							"_output_dense/bias"					)

      _lowercase      :				List[Any]      					=       get_encoder_layer_array(snake_case		,							"_output_layer_norm/gamma"					)
      _lowercase      :				Union[str, Any]      					=       get_encoder_layer_array(snake_case		,							"_output_layer_norm/beta"					)

   # Embeddings
   _lowercase      :				Tuple      					=       get_encoder_array("_position_embedding_layer/embeddings"					)
   _lowercase      :				Dict      					=       get_encoder_array("_type_embedding_layer/embeddings"					)
   _lowercase      :				Optional[int]      					=       get_encoder_array("_embedding_norm_layer/gamma"					)
   _lowercase      :				Optional[int]      					=       get_encoder_array("_embedding_norm_layer/beta"					)

   # LM Head
   _lowercase      :				Any      					=       model.cls.predictions.transform

   _lowercase      :				int      					=       get_masked_lm_array("dense/kernel"					)
   _lowercase      :				Any      					=       get_masked_lm_array("dense/bias"					)

   _lowercase      :				Optional[int]      					=       get_masked_lm_array("layer_norm/gamma"					)
   _lowercase      :				Any      					=       get_masked_lm_array("layer_norm/beta"					)

   _lowercase      :				Optional[int]      					=       get_masked_lm_array("embedding_table"					)

   # Pooling
   _lowercase      :				int      					=       BertPooler(config=snake_case					)
   _lowercase      :				BertPooler      					=       get_encoder_array("_pooler_layer/kernel"					)
   _lowercase      :				BertPooler      					=       get_encoder_array("_pooler_layer/bias"					)

   # Export final model
   model.save_pretrained(snake_case					)

   # Integration test - should load without any errors ;)
   _lowercase      :				Dict      					=       BertForMaskedLM.from_pretrained(snake_case					)
   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'''
from __future__ import annotations







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     None:
   if start is None:
      _lowercase      :				Dict      					=       0

   if end is None:
      _lowercase      :				List[Any]      					=       len(snake_case					) - 1

   if start >= end:
      return

   _lowercase      :				int      					=       (start + end) // 2

   slowsort(snake_case		,							snake_case		,							snake_case					)
   slowsort(snake_case		,							mid + 1		,							snake_case					)

   if sequence[end] < sequence[mid]:
      _lowercase					,				_lowercase      :				Any      					=       sequence[mid], sequence[end]

   slowsort(snake_case		,							snake_case		,							end - 1					)


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

    testmod()






