





"""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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      def get_masked_lm_array(lowercase_			):
            _UpperCamelCase      :	Tuple						=       F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
            _UpperCamelCase      :	Tuple						=       tf.train.load_variable(__snake_case		,__snake_case			)

            if "kernel" in name:
                  _UpperCamelCase      :	List[str]						=       array.transpose()

            return torch.from_numpy(__snake_case			)

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

            if "kernel" in name:
                  _UpperCamelCase      :	Optional[int]						=       array.transpose()

            return torch.from_numpy(__snake_case			)

      def get_encoder_layer_array(lowercase_		,lowercase_			):
            _UpperCamelCase      :	Dict						=       F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
            _UpperCamelCase      :	Optional[int]						=       tf.train.load_variable(__snake_case		,__snake_case			)

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

            return torch.from_numpy(__snake_case			)

      def get_encoder_attention_layer_array(lowercase_		,lowercase_		,lowercase_			):
            _UpperCamelCase      :	List[Any]						=       F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
            _UpperCamelCase      :	Optional[Any]						=       tf.train.load_variable(__snake_case		,__snake_case			)
            _UpperCamelCase      :	Union[str, Any]						=       array.reshape(__snake_case			)

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

            return torch.from_numpy(__snake_case			)

      print(F'''Loading model based on config from {config_path}...'''			)
      _UpperCamelCase      :	Optional[Any]						=       BertConfig.from_json_file(__snake_case			)
      _UpperCamelCase      :	Any						=       BertForMaskedLM(__snake_case			)

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

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

            _UpperCamelCase      :	Union[str, Any]						=       get_encoder_attention_layer_array(
                __snake_case		,"_query_dense/kernel"		,self_attn.query.weight.data.shape			)
            _UpperCamelCase      :	Any						=       get_encoder_attention_layer_array(
                __snake_case		,"_query_dense/bias"		,self_attn.query.bias.data.shape			)
            _UpperCamelCase      :	Optional[Any]						=       get_encoder_attention_layer_array(
                __snake_case		,"_key_dense/kernel"		,self_attn.key.weight.data.shape			)
            _UpperCamelCase      :	Tuple						=       get_encoder_attention_layer_array(
                __snake_case		,"_key_dense/bias"		,self_attn.key.bias.data.shape			)
            _UpperCamelCase      :	Any						=       get_encoder_attention_layer_array(
                __snake_case		,"_value_dense/kernel"		,self_attn.value.weight.data.shape			)
            _UpperCamelCase      :	Optional[Any]						=       get_encoder_attention_layer_array(
                __snake_case		,"_value_dense/bias"		,self_attn.value.bias.data.shape			)

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

            _UpperCamelCase      :	Optional[Any]						=       get_encoder_attention_layer_array(
                __snake_case		,"_output_dense/kernel"		,self_output.dense.weight.data.shape			)
            _UpperCamelCase      :	Tuple						=       get_encoder_attention_layer_array(
                __snake_case		,"_output_dense/bias"		,self_output.dense.bias.data.shape			)

            _UpperCamelCase      :	Any						=       get_encoder_layer_array(__snake_case		,"_attention_layer_norm/gamma"			)
            _UpperCamelCase      :	Dict						=       get_encoder_layer_array(__snake_case		,"_attention_layer_norm/beta"			)

            # Intermediate
            _UpperCamelCase      :	BertIntermediate						=       layer.intermediate

            _UpperCamelCase      :	Optional[int]						=       get_encoder_layer_array(__snake_case		,"_intermediate_dense/kernel"			)
            _UpperCamelCase      :	Union[str, Any]						=       get_encoder_layer_array(__snake_case		,"_intermediate_dense/bias"			)

            # Output
            _UpperCamelCase      :	BertOutput						=       layer.output

            _UpperCamelCase      :	Any						=       get_encoder_layer_array(__snake_case		,"_output_dense/kernel"			)
            _UpperCamelCase      :	Any						=       get_encoder_layer_array(__snake_case		,"_output_dense/bias"			)

            _UpperCamelCase      :	Any						=       get_encoder_layer_array(__snake_case		,"_output_layer_norm/gamma"			)
            _UpperCamelCase      :	Any						=       get_encoder_layer_array(__snake_case		,"_output_layer_norm/beta"			)

      # Embeddings
      _UpperCamelCase      :	Dict						=       get_encoder_array("_position_embedding_layer/embeddings"			)
      _UpperCamelCase      :	Any						=       get_encoder_array("_type_embedding_layer/embeddings"			)
      _UpperCamelCase      :	Dict						=       get_encoder_array("_embedding_norm_layer/gamma"			)
      _UpperCamelCase      :	List[str]						=       get_encoder_array("_embedding_norm_layer/beta"			)

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

      _UpperCamelCase      :	List[str]						=       get_masked_lm_array("dense/kernel"			)
      _UpperCamelCase      :	Optional[int]						=       get_masked_lm_array("dense/bias"			)

      _UpperCamelCase      :	List[Any]						=       get_masked_lm_array("layer_norm/gamma"			)
      _UpperCamelCase      :	Optional[Any]						=       get_masked_lm_array("layer_norm/beta"			)

      _UpperCamelCase      :	Optional[Any]						=       get_masked_lm_array("embedding_table"			)

      # Pooling
      _UpperCamelCase      :	Union[str, Any]						=       BertPooler(config=__snake_case			)
      _UpperCamelCase      :	BertPooler						=       get_encoder_array("_pooler_layer/kernel"			)
      _UpperCamelCase      :	BertPooler						=       get_encoder_array("_pooler_layer/bias"			)

      # Export final model
      model.save_pretrained(__snake_case			)

      # Integration test - should load without any errors ;)
      _UpperCamelCase      :	str						=       BertForMaskedLM.from_pretrained(__snake_case			)
      print(new_model.eval()			)

      print("Model conversion was done sucessfully!"			)


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







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
