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				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				str	):





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

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

										return torch.from_numpy(UpperCAmelCase__	)

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

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

										return torch.from_numpy(UpperCAmelCase__	)

					def get_encoder_layer_array(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				str	):
										SCREAMING_SNAKE_CASE           =							F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
										SCREAMING_SNAKE_CASE           =							tf.train.load_variable(UpperCAmelCase__   ,			UpperCAmelCase__	)

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

										return torch.from_numpy(UpperCAmelCase__	)

					def get_encoder_attention_layer_array(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				Optional[Any]	):
										SCREAMING_SNAKE_CASE           =							F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
										SCREAMING_SNAKE_CASE           =							tf.train.load_variable(UpperCAmelCase__   ,			UpperCAmelCase__	)
										SCREAMING_SNAKE_CASE           =							array.reshape(UpperCAmelCase__	)

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

										return torch.from_numpy(UpperCAmelCase__	)

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

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

										# Self-attention
										SCREAMING_SNAKE_CASE           =							layer.attention.self

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

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

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

										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_attention_layer_norm/gamma"	)
										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_attention_layer_norm/beta"	)

										# Intermediate
										SCREAMING_SNAKE_CASE           =							layer.intermediate

										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_intermediate_dense/kernel"	)
										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_intermediate_dense/bias"	)

										# Output
										SCREAMING_SNAKE_CASE           =							layer.output

										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_output_dense/kernel"	)
										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_output_dense/bias"	)

										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_output_layer_norm/gamma"	)
										SCREAMING_SNAKE_CASE           =							get_encoder_layer_array(UpperCAmelCase__   ,			"_output_layer_norm/beta"	)

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

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

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

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

					SCREAMING_SNAKE_CASE           =							get_masked_lm_array("embedding_table"	)

					# Pooling
					SCREAMING_SNAKE_CASE           =							BertPooler(config=UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							get_encoder_array("_pooler_layer/kernel"	)
					SCREAMING_SNAKE_CASE           =							get_encoder_array("_pooler_layer/bias"	)

					# Export final model
					model.save_pretrained(UpperCAmelCase__	)

					# Integration test - should load without any errors ;)
					SCREAMING_SNAKE_CASE           =							BertForMaskedLM.from_pretrained(UpperCAmelCase__	)
					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       : Any      					=		parser.parse_args()
							convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





