

'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase :      str					 =     logging.get_logger(__name__)


lowerCamelCase :      List[Any]					 =     {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase :      Union[str, Any]					 =     {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

lowerCamelCase :      List[Any]					 =     {
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}


@lru_cache()
def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =(
         list(range(ord('!'				)							, ord('~'				) + 1				)				) + list(range(ord('¡'				)							, ord('¬'				) + 1				)				) + list(range(ord('®'				)							, ord('ÿ'				) + 1				)				)
     )
     _SCREAMING_SNAKE_CASE							    =bs[:]
     _SCREAMING_SNAKE_CASE							    =0
     for b in range(2**8				):
          if b not in bs:
               bs.append(_UpperCamelCase				)
               cs.append(2**8 + n				)
               n += 1
     _SCREAMING_SNAKE_CASE							    =[chr(_UpperCamelCase				) for n in cs]
     return dict(zip(_UpperCamelCase							, _UpperCamelCase				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =set()
     _SCREAMING_SNAKE_CASE							    =word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          _SCREAMING_SNAKE_CASE							    =char
     return pairs







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				['input_ids', 'attention_mask']





  def __init__(		self      :	str							,					_a      :	Optional[int]							,					_a      :	Optional[int]							,					_a      :	List[str]="replace"							,					_a      :	List[str]="<s>"							,					_a      :	int="</s>"							,					_a      :	List[Any]="</s>"							,					_a      :	Optional[Any]="<s>"							,					_a      :	Any="<unk>"							,					_a      :	Optional[int]="<pad>"							,					_a      :	int="<mask>"							,					_a      :	List[str]=False							,					**_a      :	Tuple							,					)	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else bos_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else eos_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else sep_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else cls_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else unk_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else mask_token

       super().__init__(
           errors=_a							,					bos_token=_a							,					eos_token=_a							,					unk_token=_a							,					sep_token=_a							,					cls_token=_a							,					pad_token=_a							,					mask_token=_a							,					add_prefix_space=_a							,					**_a							,					)

       with open(_a							,					encoding='utf-8' ) as vocab_handle:
            _SCREAMING_SNAKE_CASE							    =json.load(_a )
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.encoder.items()}
       _SCREAMING_SNAKE_CASE							    =errors  # how to handle errors in decoding
       _SCREAMING_SNAKE_CASE							    =bytes_to_unicode()
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.byte_encoder.items()}
       with open(_a							,					encoding='utf-8' ) as merges_handle:
            _SCREAMING_SNAKE_CASE							    =merges_handle.read().split('\n' )[1:-1]
       _SCREAMING_SNAKE_CASE							    =[tuple(merge.split() ) for merge in bpe_merges]
       _SCREAMING_SNAKE_CASE							    =dict(zip(_a							,					range(len(_a ) ) ) )
       _SCREAMING_SNAKE_CASE							    ={}
       _SCREAMING_SNAKE_CASE							    =add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       _SCREAMING_SNAKE_CASE							    =re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )





  @property
  def 						A  (		self      :	Any )	->    str:





       '''simple docstring'''






       return len(self.encoder )





  def 						A  (		self      :	Optional[Any] )	->    Union[str, Any]:





       '''simple docstring'''






       return dict(self.encoder							,					**self.added_tokens_encoder )





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Tuple )	->    str:





       '''simple docstring'''






       if token in self.cache:
            return self.cache[token]
       _SCREAMING_SNAKE_CASE							    =tuple(_a )
       _SCREAMING_SNAKE_CASE							    =get_pairs(_a )

       if not pairs:
            return token

       while True:
            _SCREAMING_SNAKE_CASE							    =min(_a							,					key=lambda _a : self.bpe_ranks.get(_a							,					float('inf' ) ) )
            if bigram not in self.bpe_ranks:
                 break
            _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =bigram
            _SCREAMING_SNAKE_CASE							    =[]
            _SCREAMING_SNAKE_CASE							    =0
            while i < len(_a ):
                 try:
                      _SCREAMING_SNAKE_CASE							    =word.index(_a							,					_a )
                 except ValueError:
                      new_word.extend(word[i:] )
                      break
                 else:
                      new_word.extend(word[i:j] )
                      _SCREAMING_SNAKE_CASE							    =j

                 if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
                      new_word.append(first + second )
                      i += 2
                 else:
                      new_word.append(word[i] )
                      i += 1
            _SCREAMING_SNAKE_CASE							    =tuple(_a )
            _SCREAMING_SNAKE_CASE							    =new_word
            if len(_a ) == 1:
                 break
            else:
                 _SCREAMING_SNAKE_CASE							    =get_pairs(_a )
       _SCREAMING_SNAKE_CASE							    =' '.join(_a )
       _SCREAMING_SNAKE_CASE							    =word
       return word





  def 						A  (		self      :	Optional[Any]							,					_a      :	int )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       for token in re.findall(self.pat							,					_a ):
            _SCREAMING_SNAKE_CASE							    =''.join(
                self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a ).split(' ' ) )
       return bpe_tokens





  def 						A  (		self      :	Dict							,					_a      :	List[str] )	->    int:





       '''simple docstring'''






       return self.encoder.get(_a							,					self.encoder.get(self.unk_token ) )





  def 						A  (		self      :	Dict							,					_a      :	List[str] )	->    Tuple:





       '''simple docstring'''






       return self.decoder.get(_a )





  def 						A  (		self      :	int							,					_a      :	Any )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =''.join(_a )
       _SCREAMING_SNAKE_CASE							    =bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'							,					errors=self.errors )
       return text





  def 						A  (		self      :	List[Any]							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






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

       with open(_a							,					'w'							,					encoding='utf-8' ) as f:
            f.write(json.dumps(self.encoder							,					indent=2							,					sort_keys=_a							,					ensure_ascii=_a ) + '\n' )

       _SCREAMING_SNAKE_CASE							    =0
       with open(_a							,					'w'							,					encoding='utf-8' ) as writer:
            writer.write('#version: 0.2\n' )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,					key=lambda _a : kv[1] ):
                 if index != token_index:
                      logger.warning(
                          f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                          ' Please check that the tokenizer is not corrupted!' )
                      _SCREAMING_SNAKE_CASE							    =token_index
                 writer.write(' '.join(_a ) + '\n' )
                 index += 1

       return vocab_file, merge_file





  def 						A  (		self      :	Tuple							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[int]							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None							,					_a      :	bool = False )	->    List[int]:





       '''simple docstring'''






       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_a							,					token_ids_a=_a							,					already_has_special_tokens=_a )

       if token_ids_a is None:
            return [1] + ([0] * len(_a )) + [1]
       return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]





  def 						A  (		self      :	Any							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_SNAKE_CASE							    =[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 						A  (		self      :	List[Any]							,					_a      :	Tuple							,					_a      :	List[Any]=False							,					**_a      :	Optional[int] )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.pop('add_prefix_space'							,					self.add_prefix_space )
       if (is_split_into_words or add_prefix_space) and (len(_a ) > 0 and not text[0].isspace()):
            _SCREAMING_SNAKE_CASE							    =' ' + text
       return (text, kwargs)


"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    raise TypeError('''number of qubits must be a integer.'''		)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''		)
  if math.floor(__lowerCamelCase		) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''		)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

  for i in range(__lowerCamelCase		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(__lowerCamelCase		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j) ,					__lowerCamelCase ,					__lowerCamelCase		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(__lowerCamelCase ,					number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(__lowerCamelCase ,					__lowerCamelCase		)
  # simulate with 10000 shots
  _snake_case					     =       Aer.get_backend('''qasm_simulator'''		)
  _snake_case					     =       execute(__lowerCamelCase ,					__lowerCamelCase ,					shots=1_00_00		)

  return job.result().get_counts(__lowerCamelCase		)


if __name__ == "__main__":
 print(
     F"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
 )



