



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:str			,       UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		TaConfig.from_json_file(UpperCAmelCase__       )
 print(F"""Building PyTorch model from configuration: {config}"""       )
 a							=		TaForConditionalGeneration(UpperCAmelCase__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
 model.save_pretrained(UpperCAmelCase__       )


if __name__ == "__main__":
     A_		:  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 T5 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.'''
     )
     A_		:  Any										=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


