


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  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__":
   lowerCamelCase__							:						List[str]							     =   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__							:						int							     =   parser.parse_args()
   convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




