


'''simple docstring'''






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 	_A				(lowerCAmelCase__  :Namespace		)   ->  Optional[int]:




     '''simple docstring'''



     return ConvertCommand(
         args.model_type				,						args.tf_checkpoint				,						args.pytorch_dump_output				,						args.config				,						args.finetuning_task_name		)


a_					:    Dict          =    "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class  a     (     _SCREAMING_SNAKE_CASE       ):
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__						)					->							Optional[Any]:
      _a			      =							parser.add_parser(
          'convert'      ,				help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'      ,				)
      train_parser.add_argument('--model_type'      ,				type=__magic_name__      ,				required=__magic_name__      ,				help='Model\'s type.'						)
      train_parser.add_argument(
          '--tf_checkpoint'      ,				type=__magic_name__      ,				required=__magic_name__      ,				help='TensorFlow checkpoint path or folder.'						)
      train_parser.add_argument(
          '--pytorch_dump_output'      ,				type=__magic_name__      ,				required=__magic_name__      ,				help='Path to the PyTorch saved model output.'						)
      train_parser.add_argument('--config'      ,				type=__magic_name__      ,				default=''      ,				help='Configuration file path or folder.'						)
      train_parser.add_argument(
          '--finetuning_task_name'      ,				type=__magic_name__      ,				default=__magic_name__      ,				help='Optional fine-tuning task name if the TF model was a finetuned model.'      ,				)
      train_parser.set_defaults(func=__magic_name__						)
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				*__magic_name__      ,				)					->							Dict:
      _a			      =							logging.get_logger('transformers-cli/converting'						)

      self._logger.info(f'Loading model {model_type}'						)
      _a			      =							model_type
      _a			      =							tf_checkpoint
      _a			      =							pytorch_dump_output
      _a			      =							config
      _a			      =							finetuning_task_name




 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      if self._model_type == "albert":
           try:
                from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                    convert_tf_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "bert":
           try:
                from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                    convert_tf_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "funnel":
           try:
                from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                    convert_tf_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "t5":
           try:
                from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "gpt":
           from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
               convert_openai_checkpoint_to_pytorch,
           )

           convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "transfo_xl":
           try:
                from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                    convert_transfo_xl_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           if "ckpt" in self._tf_checkpoint.lower():
                _a			      =							self._tf_checkpoint
                _a			      =							''
           else:
                _a			      =							self._tf_checkpoint
                _a			      =							''
           convert_transfo_xl_checkpoint_to_pytorch(
               __magic_name__      ,				self._config      ,				self._pytorch_dump_output      ,				__magic_name__						)
      elif self._model_type == "gpt2":
           try:
                from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                    convert_gpta_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      elif self._model_type == "xlnet":
           try:
                from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                    convert_xlnet_checkpoint_to_pytorch,
                )
           except ImportError:
                raise ImportError(__magic_name__						)

           convert_xlnet_checkpoint_to_pytorch(
               self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output      ,				self._finetuning_task_name						)
      elif self._model_type == "xlm":
           from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
               convert_xlm_checkpoint_to_pytorch,
           )

           convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._pytorch_dump_output						)
      elif self._model_type == "lxmert":
           from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
               convert_lxmert_checkpoint_to_pytorch,
           )

           convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._pytorch_dump_output						)
      elif self._model_type == "rembert":
           from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
               convert_rembert_tf_checkpoint_to_pytorch,
           )

           convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,				self._config      ,				self._pytorch_dump_output						)
      else:
           raise ValueError(
               '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'						)





'''simple docstring'''






import numpy as np
import qiskit






def 	_A				(lowerCAmelCase__  :int = 8				,						lowerCAmelCase__  :int | None = None		)   ->  str:




     '''simple docstring'''



     _a			      =							np.random.default_rng(seed=lowerCAmelCase__		)

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     _a			      =							6 * key_len
     # Measurement basis for Alice's qubits.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)
     # The set of states Alice will prepare.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)
     # Measurement basis for Bob's qubits.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)

     # Quantum Circuit to simulate BB84
     _a			      =							qiskit.QuantumCircuit(lowerCAmelCase__				,						name='BB84'		)

     # Alice prepares her qubits according to rules above.
     for index, _ in enumerate(lowerCAmelCase__		):
          if alice_state[index] == 1:
               bbaa_circ.x(lowerCAmelCase__		)
          if alice_basis[index] == 1:
               bbaa_circ.h(lowerCAmelCase__		)
     bbaa_circ.barrier()

     # Bob measures the received qubits according to rules above.
     for index, _ in enumerate(lowerCAmelCase__		):
          if bob_basis[index] == 1:
               bbaa_circ.h(lowerCAmelCase__		)

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     _a			      =							qiskit.Aer.get_backend('aer_simulator'		)
     # We only need to run one shot because the key is unique.
     # Multiple shots will produce the same key.
     _a			      =							qiskit.execute(lowerCAmelCase__				,						lowerCAmelCase__				,						shots=1				,						seed_simulator=lowerCAmelCase__		)
     # Returns the result of measurement.
     _a			      =							job.result().get_counts(lowerCAmelCase__		).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     _a			      =							''.join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
             if alice_basis_bit == bob_basis_bit
         ]		)

     # Get final key. Pad with 0 if too short, otherwise truncate.
     _a			      =							gen_key[:key_len] if len(lowerCAmelCase__		) >= key_len else gen_key.ljust(lowerCAmelCase__				,						'0'		)
     return key


if __name__ == "__main__":
 print(f'''The generated key is : {bbaa(8, seed=0)}''')
 from doctest import testmod

 testmod()

