


"""simple docstring"""






from random import randint
from tempfile import TemporaryFile

import numpy as np
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				0
     if start < end:
          _a    	=				randint(_lowerCAmelCase,     _lowerCAmelCase       )
          _a    	=				a[end]
          _a    	=				a[pivot]
          _a    	=				temp

          _a	,      _a    	=				_in_place_partition(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )
          count += _in_place_quick_sort(_lowerCAmelCase,     _lowerCAmelCase,     p - 1       )
          count += _in_place_quick_sort(_lowerCAmelCase,     p + 1,     _lowerCAmelCase       )
     return count
def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Optional[int]       ):






     """simple docstring"""

     _a    	=				0
     _a    	=				randint(_lowerCAmelCase,     _lowerCAmelCase       )
     _a    	=				a[end]
     _a    	=				a[pivot]
     _a    	=				temp
     _a    	=				start - 1
     for index in range(_lowerCAmelCase,     _lowerCAmelCase       ):
          count += 1
          if a[index] < a[end]:  # check if current val is less than pivot value
               _a    	=				new_pivot_index + 1
               _a    	=				a[new_pivot_index]
               _a    	=				a[index]
               _a    	=				temp

     _a    	=				a[new_pivot_index + 1]
     _a    	=				a[end]
     _a    	=				temp
     return new_pivot_index + 1, count


__snake_case		=       TemporaryFile()
__snake_case		=       100  # 1000 elements are to be sorted


__snake_case	,__snake_case		=       0, 1  # mean and standard deviation
__snake_case		=       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
__snake_case		=       np.load(outfile)
__snake_case		=       len(M) - 1
__snake_case		=       _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)

print(z)



"""simple docstring"""






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				BertForPreTraining(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     torch.save(model.state_dict(),     _lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)