






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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     def get_masked_lm_array(__snake_case       ):
          __lowerCAmelCase								=					F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

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

          return torch.from_numpy(__snake_case       )

     def get_encoder_array(__snake_case       ):
          __lowerCAmelCase								=					F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

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

          return torch.from_numpy(__snake_case       )

     def get_encoder_layer_array(__snake_case					, __snake_case       ):
          __lowerCAmelCase								=					F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

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

          return torch.from_numpy(__snake_case       )

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

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

          return torch.from_numpy(__snake_case       )

     print(F"""Loading model based on config from {config_path}..."""       )
     __lowerCAmelCase								=					BertConfig.from_json_file(__snake_case       )
     __lowerCAmelCase								=					BertForMaskedLM(__snake_case       )

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

          # Self-attention
          __lowerCAmelCase								=					layer.attention.self

          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_query_dense/kernel"					, self_attn.query.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_query_dense/bias"					, self_attn.query.bias.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_key_dense/kernel"					, self_attn.key.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_key_dense/bias"					, self_attn.key.bias.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_value_dense/kernel"					, self_attn.value.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_value_dense/bias"					, self_attn.value.bias.data.shape       )

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

          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_output_dense/kernel"					, self_output.dense.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_output_dense/bias"					, self_output.dense.bias.data.shape       )

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_attention_layer_norm/gamma"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_attention_layer_norm/beta"       )

          # Intermediate
          __lowerCAmelCase								=					layer.intermediate

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_intermediate_dense/kernel"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_intermediate_dense/bias"       )

          # Output
          __lowerCAmelCase								=					layer.output

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_dense/kernel"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_dense/bias"       )

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_layer_norm/gamma"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_layer_norm/beta"       )

     # Embeddings
     __lowerCAmelCase								=					get_encoder_array("_position_embedding_layer/embeddings"       )
     __lowerCAmelCase								=					get_encoder_array("_type_embedding_layer/embeddings"       )
     __lowerCAmelCase								=					get_encoder_array("_embedding_norm_layer/gamma"       )
     __lowerCAmelCase								=					get_encoder_array("_embedding_norm_layer/beta"       )

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

     __lowerCAmelCase								=					get_masked_lm_array("dense/kernel"       )
     __lowerCAmelCase								=					get_masked_lm_array("dense/bias"       )

     __lowerCAmelCase								=					get_masked_lm_array("layer_norm/gamma"       )
     __lowerCAmelCase								=					get_masked_lm_array("layer_norm/beta"       )

     __lowerCAmelCase								=					get_masked_lm_array("embedding_table"       )

     # Pooling
     __lowerCAmelCase								=					BertPooler(config=__snake_case       )
     __lowerCAmelCase								=					get_encoder_array("_pooler_layer/kernel"       )
     __lowerCAmelCase								=					get_encoder_array("_pooler_layer/bias"       )

     # Export final model
     model.save_pretrained(__snake_case       )

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

     print("Model conversion was done sucessfully!"       )


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














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






