
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__						:	Any	     = None


UpperCAmelCase__						:	List[Any]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Tuple	     = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:	List[str]	     = {
    '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__						:	int	     = {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Any												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       Tuple												=       ['''input_ids''', '''attention_mask''']
   __UpperCamelCase						:       Dict												=       XLMRobertaTokenizer


   def __init__(   self	:				int     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				Optional[Any]="<s>"     ,     lowerCAmelCase_	:				Tuple="</s>"     ,     lowerCAmelCase_	:				Dict="</s>"     ,     lowerCAmelCase_	:				List[str]="<s>"     ,     lowerCAmelCase_	:				List[str]="<unk>"     ,     lowerCAmelCase_	:				Tuple="<pad>"     ,     lowerCAmelCase_	:				List[Any]="<mask>"     ,     **lowerCAmelCase_	:				List[str]     ,     ):

      """simple docstring"""
      # Mask token behave like a normal word, i.e. include the space before it
      _A:    Dict						     =			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_     ,     )

      _A:    Union[str, Any]						     =			vocab_file
      _A:    Union[str, Any]						     =			False if not self.vocab_file else True


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""

      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _A:    Union[str, Any]						     =			[self.cls_token_id]
      _A:    int						     =			[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    List[Any]						     =			[self.sep_token_id]
      _A:    str						     =			[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 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = 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
      _A:    Dict						     =			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






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

   # Return the histogram data of the results of the experiment.
   return job.result().get_counts(a )


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



