




'''simple docstring'''


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 						lowerCamelCase__   (				a    ,	a    ,	a							):
						# Initialise PyTorch model
						__snake_case						 =   MobileBertConfig.from_json_file(a							)
						print(f'Building PyTorch model from configuration: {config}'							)
						__snake_case						 =   MobileBertForPreTraining(a							)
						# Load weights from tf checkpoint
						__snake_case						 =   load_tf_weights_in_mobilebert(a    ,	a    ,	a							)
						# Save pytorch-model
						print(f'Save PyTorch model to {pytorch_dump_path}'							)
						torch.save(model.state_dict()    ,	a							)


if __name__ == "__main__":
	_lowercase  =			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(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT 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."""
	)
	_lowercase  =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)







'''simple docstring'''


from collections import defaultdict
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   first_str.lower().strip()
						__snake_case						 =   second_str.lower().strip()

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

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

						# Default values for count should be 0
						__snake_case						 =   defaultdict(a							)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(a							)							):
												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()
	_lowercase  =			input("""Enter the first string """).strip()
	_lowercase  =			input("""Enter the second string """).strip()

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

