
"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
      UpperCamelCase      :							str          =					None


UpperCamelCase      :							int          =					logging.get_logger(__name__)

UpperCamelCase      :							List[str]          =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase      :							Any          =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase      :							Any          =					{
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}

class __lowerCAmelCase						( _a     ):
     lowercase		  =		VOCAB_FILES_NAMES
     lowercase		  =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		  =		["""input_ids""", """attention_mask"""]
     lowercase		  =		XLMRobertaTokenizer





     def __init__(      self      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase="<s>"      ,	__UpperCAmelCase="</s>"      ,	__UpperCAmelCase="</s>"      ,	__UpperCAmelCase="<s>"      ,	__UpperCAmelCase="<unk>"      ,	__UpperCAmelCase="<pad>"      ,	__UpperCAmelCase="<mask>"      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			AddedToken(__lowerCamelCase      ,	lstrip=__lowerCamelCase      ,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,	__lowerCamelCase ) else mask_token

      super().__init__(
          __lowerCamelCase      ,	tokenizer_file=__lowerCamelCase      ,	bos_token=__lowerCamelCase      ,	eos_token=__lowerCamelCase      ,	sep_token=__lowerCamelCase      ,	cls_token=__lowerCamelCase      ,	unk_token=__lowerCamelCase      ,	pad_token=__lowerCamelCase      ,	mask_token=__lowerCamelCase      ,	**__lowerCamelCase      ,	)

      __UpperCamelCase							    =			vocab_file
      __UpperCamelCase							    =			False if not self.vocab_file else True





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __UpperCamelCase							    =			[self.cls_token_id]
      __UpperCamelCase							    =			[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      __UpperCamelCase							    =			[self.sep_token_id]
      __UpperCamelCase							    =			[self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.' )

      if not os.path.isdir(__lowerCamelCase ):
       logger.error(F'Vocabulary path ({save_directory}) should be a directory.' )
       return
      __UpperCamelCase							    =			os.path.join(
          __lowerCamelCase      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

      if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ):
       copyfile(self.vocab_file      ,	__lowerCamelCase )

      return (out_vocab_file,)











import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2   )						->     qiskit.result.counts.Counts:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

	UpperCamelCase	:List[Any]		=      QuantumCircuit(__magic_name__      ,     __magic_name__   )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     0   )  # Quantum State to teleport
	quantum_circuit.h(1   )  # add hadamard gate
	quantum_circuit.cx(
	    1      ,     2   )  # add control gate with qubit 1 as control and 2 as target.
	quantum_circuit.cx(0      ,     1   )
	quantum_circuit.h(0   )
	quantum_circuit.cz(0      ,     2   )  # add control z gate.
	quantum_circuit.cx(1      ,     2   )

	quantum_circuit.measure([2]      ,     [0]   )  # measure the qubit.

	# Simulate the circuit using qasm simulator
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


if __name__ == "__main__":
	print(
	    '''Total count for teleported state is: '''
	    F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
	)
