


'''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__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   def get_masked_lm_array(__lowerCamelCase	:							str							):
      _UpperCAmelCase       :       Union[str, Any]							   =f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
      _UpperCAmelCase       :       Dict							   =tf.train.load_variable(__lowerCamelCase	,   __lowerCamelCase							)

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

      return torch.from_numpy(__lowerCamelCase							)

   def get_encoder_array(__lowerCamelCase	:							str							):
      _UpperCAmelCase       :       Optional[int]							   =f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
      _UpperCAmelCase       :       Any							   =tf.train.load_variable(__lowerCamelCase	,   __lowerCamelCase							)

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

      return torch.from_numpy(__lowerCamelCase							)

   def get_encoder_layer_array(__lowerCamelCase	:							int	,   __lowerCamelCase	:							str							):
      _UpperCAmelCase       :       Tuple							   =f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
      _UpperCAmelCase       :       Tuple							   =tf.train.load_variable(__lowerCamelCase	,   __lowerCamelCase							)

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

      return torch.from_numpy(__lowerCamelCase							)

   def get_encoder_attention_layer_array(__lowerCamelCase	:							int	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							str							):
      _UpperCAmelCase       :       List[str]							   =f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
      _UpperCAmelCase       :       Dict							   =tf.train.load_variable(__lowerCamelCase	,   __lowerCamelCase							)
      _UpperCAmelCase       :       List[Any]							   =array.reshape(__lowerCamelCase							)

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

      return torch.from_numpy(__lowerCamelCase							)

   print(f"Loading model based on config from {config_path}..."							)
   _UpperCAmelCase       :       str							   =BertConfig.from_json_file(__lowerCamelCase							)
   _UpperCAmelCase       :       Optional[int]							   =BertForMaskedLM(__lowerCamelCase							)

   # 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       :       str							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_query_dense/kernel'	,   self_attn.query.weight.data.shape							)
      _UpperCAmelCase       :       Dict							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_query_dense/bias'	,   self_attn.query.bias.data.shape							)
      _UpperCAmelCase       :       Union[str, Any]							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_key_dense/kernel'	,   self_attn.key.weight.data.shape							)
      _UpperCAmelCase       :       List[str]							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_key_dense/bias'	,   self_attn.key.bias.data.shape							)
      _UpperCAmelCase       :       List[str]							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_value_dense/kernel'	,   self_attn.value.weight.data.shape							)
      _UpperCAmelCase       :       Tuple							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_value_dense/bias'	,   self_attn.value.bias.data.shape							)

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

      _UpperCAmelCase       :       List[str]							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_output_dense/kernel'	,   self_output.dense.weight.data.shape							)
      _UpperCAmelCase       :       Optional[Any]							   =get_encoder_attention_layer_array(
          __lowerCamelCase	,   '_output_dense/bias'	,   self_output.dense.bias.data.shape							)

      _UpperCAmelCase       :       int							   =get_encoder_layer_array(__lowerCamelCase	,   '_attention_layer_norm/gamma'							)
      _UpperCAmelCase       :       int							   =get_encoder_layer_array(__lowerCamelCase	,   '_attention_layer_norm/beta'							)

      # Intermediate
      _UpperCAmelCase       :       BertIntermediate							   =layer.intermediate

      _UpperCAmelCase       :       str							   =get_encoder_layer_array(__lowerCamelCase	,   '_intermediate_dense/kernel'							)
      _UpperCAmelCase       :       int							   =get_encoder_layer_array(__lowerCamelCase	,   '_intermediate_dense/bias'							)

      # Output
      _UpperCAmelCase       :       BertOutput							   =layer.output

      _UpperCAmelCase       :       List[str]							   =get_encoder_layer_array(__lowerCamelCase	,   '_output_dense/kernel'							)
      _UpperCAmelCase       :       List[str]							   =get_encoder_layer_array(__lowerCamelCase	,   '_output_dense/bias'							)

      _UpperCAmelCase       :       str							   =get_encoder_layer_array(__lowerCamelCase	,   '_output_layer_norm/gamma'							)
      _UpperCAmelCase       :       str							   =get_encoder_layer_array(__lowerCamelCase	,   '_output_layer_norm/beta'							)

   # Embeddings
   _UpperCAmelCase       :       List[str]							   =get_encoder_array('_position_embedding_layer/embeddings'							)
   _UpperCAmelCase       :       Tuple							   =get_encoder_array('_type_embedding_layer/embeddings'							)
   _UpperCAmelCase       :       Optional[Any]							   =get_encoder_array('_embedding_norm_layer/gamma'							)
   _UpperCAmelCase       :       str							   =get_encoder_array('_embedding_norm_layer/beta'							)

   # LM Head
   _UpperCAmelCase       :       int							   =model.cls.predictions.transform

   _UpperCAmelCase       :       str							   =get_masked_lm_array('dense/kernel'							)
   _UpperCAmelCase       :       List[str]							   =get_masked_lm_array('dense/bias'							)

   _UpperCAmelCase       :       Dict							   =get_masked_lm_array('layer_norm/gamma'							)
   _UpperCAmelCase       :       Tuple							   =get_masked_lm_array('layer_norm/beta'							)

   _UpperCAmelCase       :       Tuple							   =get_masked_lm_array('embedding_table'							)

   # Pooling
   _UpperCAmelCase       :       List[Any]							   =BertPooler(config=__lowerCamelCase							)
   _UpperCAmelCase       :       BertPooler							   =get_encoder_array('_pooler_layer/kernel'							)
   _UpperCAmelCase       :       BertPooler							   =get_encoder_array('_pooler_layer/bias'							)

   # Export final model
   model.save_pretrained(__lowerCamelCase							)

   # Integration test - should load without any errors ;)
   _UpperCAmelCase       :       Tuple							   =BertForMaskedLM.from_pretrained(__lowerCamelCase							)
   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'''






from __future__ import annotations

import bisect





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       int							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       Dict							   =lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         _UpperCAmelCase       :       int							   =mid + 1
      else:
         _UpperCAmelCase       :       Union[str, Any]							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       str							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       List[Any]							   =lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         _UpperCAmelCase       :       Dict							   =mid + 1
      else:
         _UpperCAmelCase       :       Tuple							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_left(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_right(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =0
   _UpperCAmelCase       :       Any							   =len(__lowerCamelCase							) - 1

   while left <= right:
      _UpperCAmelCase       :       int							   =left + (right - left) // 2
      _UpperCAmelCase       :       Union[str, Any]							   =sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         _UpperCAmelCase       :       Optional[Any]							   =midpoint - 1
      else:
         _UpperCAmelCase       :       int							   =midpoint + 1
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =bisect.bisect_left(__lowerCamelCase	,   __lowerCamelCase							)
   if index != len(__lowerCamelCase							) and sorted_collection[index] == item:
      return index
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if right < left:
      return None

   _UpperCAmelCase       :       str							   =left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   midpoint - 1							)
   else:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   midpoint + 1	,   __lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=input('Enter numbers separated by comma:\n').strip()
 lowercase							=sorted(int(item) for item in user_input.split(','))
 lowercase							=int(input('Enter a single number to be found in the list:\n'))
 lowercase							=binary_search(collection, target)
 if result is None:
  print(F"""{target} was not found in {collection}.""")
 else:
  print(F"""{target} was found at position {result} in {collection}.""")

