



'''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 lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     def get_masked_lm_array(__A):
          _a											=						F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          _a											=						tf.train.load_variable(__A						,			__A)

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

          return torch.from_numpy(__A)

     def get_encoder_array(__A):
          _a											=						F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          _a											=						tf.train.load_variable(__A						,			__A)

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

          return torch.from_numpy(__A)

     def get_encoder_layer_array(__A						,			__A):
          _a											=						F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          _a											=						tf.train.load_variable(__A						,			__A)

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

          return torch.from_numpy(__A)

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

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

          return torch.from_numpy(__A)

     print(F'''Loading model based on config from {config_path}...''')
     _a											=						BertConfig.from_json_file(__A)
     _a											=						BertForMaskedLM(__A)

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

          # Self-attention
          _a											=						layer.attention.self

          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_query_dense/kernel'''						,			self_attn.query.weight.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_query_dense/bias'''						,			self_attn.query.bias.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_key_dense/kernel'''						,			self_attn.key.weight.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_key_dense/bias'''						,			self_attn.key.bias.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_value_dense/kernel'''						,			self_attn.value.weight.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_value_dense/bias'''						,			self_attn.value.bias.data.shape)

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

          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_output_dense/kernel'''						,			self_output.dense.weight.data.shape)
          _a											=						get_encoder_attention_layer_array(
              __A						,			'''_output_dense/bias'''						,			self_output.dense.bias.data.shape)

          _a											=						get_encoder_layer_array(__A						,			'''_attention_layer_norm/gamma''')
          _a											=						get_encoder_layer_array(__A						,			'''_attention_layer_norm/beta''')

          # Intermediate
          _a											=						layer.intermediate

          _a											=						get_encoder_layer_array(__A						,			'''_intermediate_dense/kernel''')
          _a											=						get_encoder_layer_array(__A						,			'''_intermediate_dense/bias''')

          # Output
          _a											=						layer.output

          _a											=						get_encoder_layer_array(__A						,			'''_output_dense/kernel''')
          _a											=						get_encoder_layer_array(__A						,			'''_output_dense/bias''')

          _a											=						get_encoder_layer_array(__A						,			'''_output_layer_norm/gamma''')
          _a											=						get_encoder_layer_array(__A						,			'''_output_layer_norm/beta''')

     # Embeddings
     _a											=						get_encoder_array('''_position_embedding_layer/embeddings''')
     _a											=						get_encoder_array('''_type_embedding_layer/embeddings''')
     _a											=						get_encoder_array('''_embedding_norm_layer/gamma''')
     _a											=						get_encoder_array('''_embedding_norm_layer/beta''')

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

     _a											=						get_masked_lm_array('''dense/kernel''')
     _a											=						get_masked_lm_array('''dense/bias''')

     _a											=						get_masked_lm_array('''layer_norm/gamma''')
     _a											=						get_masked_lm_array('''layer_norm/beta''')

     _a											=						get_masked_lm_array('''embedding_table''')

     # Pooling
     _a											=						BertPooler(config=__A)
     _a											=						get_encoder_array('''_pooler_layer/kernel''')
     _a											=						get_encoder_array('''_pooler_layer/bias''')

     # Export final model
     model.save_pretrained(__A)

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

     print('''Model conversion was done sucessfully!''')


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








'''simple docstring'''





lowercase_	 =						256
# Modulus to hash a string
lowercase_	 =						1_000_003




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						len(__A)
     _a											=						len(__A)
     if p_len > t_len:
          return False

     _a											=						0
     _a											=						0
     _a											=						1

     # Calculating the hash of pattern and substring of text
     for i in range(__A):
          _a											=						(ord(pattern[i]) + p_hash * alphabet_size) % modulus
          _a											=						(ord(text[i]) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a											=						(modulus_power * alphabet_size) % modulus

     for i in range(0						,			t_len - p_len + 1):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a											=						(
              (text_hash - ord(text[i]) * modulus_power) * alphabet_size
              + ord(text[i + p_len])
          ) % modulus
     return False




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						'''abc1abc12'''
     _a											=						'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     _a											=						'''alskfjaldsk23adsfabcabc'''
     assert rabin_karp(__A						,			__A) and not rabin_karp(__A						,			__A)

     # Test 2)
     _a											=						'''ABABX'''
     _a											=						'''ABABZABABYABABX'''
     assert rabin_karp(__A						,			__A)

     # Test 3)
     _a											=						'''AAAB'''
     _a											=						'''ABAAAAAB'''
     assert rabin_karp(__A						,			__A)

     # Test 4)
     _a											=						'''abcdabcy'''
     _a											=						'''abcxabcdabxabcdabcdabcy'''
     assert rabin_karp(__A						,			__A)

     # Test 5)
     _a											=						'''Lü'''
     _a											=						'''Lüsai'''
     assert rabin_karp(__A						,			__A)
     _a											=						'''Lue'''
     assert not rabin_karp(__A						,			__A)
     print('''Success.''')


if __name__ == "__main__":
  test_rabin_karp()



