



from collections import defaultdict





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				first_str.lower().strip()
    __a						       =				second_str.lower().strip()

    # Remove whitespace
    __a						       =				first_str.replace(''' '''   , ''''''				)
    __a						       =				second_str.replace(''' '''   , ''''''				)

    # Strings of different lengths are not anagrams
    if len(_UpperCAmelCase				) != len(_UpperCAmelCase				):
        return False

    # Default values for count should be 0
    __a						       =				defaultdict(_UpperCAmelCase				)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(_UpperCAmelCase				)				):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Any      		=     input('''Enter the first string ''').strip()
 __snake_case						:int      		=     input('''Enter the second string ''').strip()

 __snake_case						:int      		=     check_anagrams(input_a, input_b)
 print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.')










import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				LxmertForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

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


if __name__ == "__main__":
 __snake_case						:List[Any]      		=     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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis 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						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





