
'''simple docstring'''





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE     =       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE     =       {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Optional[Any]				=							VOCAB_FILES_NAMES
 lowercase_					:    str				=							PRETRAINED_VOCAB_FILES_MAP
 lowercase_					:    Union[str, Any]				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_					:    Union[str, Any]				=							["input_ids", "attention_mask"]
 lowercase_					:    List[int]				=							[]







 def __init__(					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]			,				snake_case__		:  Tuple="<unk>"			,				snake_case__		:  Any="<s>"			,				snake_case__		:  Any="</s>"			,				snake_case__		:  Union[str, Any]="<pad>"			,				snake_case__		:  Optional[Any]="[SEP]"			,				snake_case__		:  Union[str, Any]="[MASK]"			,				snake_case__		:  List[str]="[CLS]"			,				snake_case__		:  Optional[Dict[str, Any]] = None			,				**snake_case__		:  Union[str, Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else bos_token
   UpperCAmelCase__      :					List[Any]				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else eos_token
   UpperCAmelCase__      :					Union[str, Any]				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else unk_token
   UpperCAmelCase__      :					Optional[Any]				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else pad_token
   UpperCAmelCase__      :					str				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else cls_token
   UpperCAmelCase__      :					List[str]				       =							AddedToken(snake_case__			,				lstrip=snake_case__			,				rstrip=snake_case__ ) if isinstance(snake_case__			,				snake_case__ ) else sep_token

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

   UpperCAmelCase__      :					Optional[int]				       =							{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=snake_case__			,				eos_token=snake_case__			,				unk_token=snake_case__			,				pad_token=snake_case__			,				sep_token=snake_case__			,				mask_token=snake_case__			,				cls_token=snake_case__			,				sp_model_kwargs=self.sp_model_kwargs			,				**snake_case__			,				)

   UpperCAmelCase__      :					Any				       =							vocab_file

   UpperCAmelCase__      :					Any				       =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
   self.sp_model.Load(snake_case__ )







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return self.sp_model.get_piece_size()







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							{self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
   vocab.update(self.added_tokens_encoder )
   return vocab







 def __getstate__(					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.__dict__.copy()
   UpperCAmelCase__      :					List[str]				       =							None
   return state







 def __setstate__(					self		:  str			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							d

   # for backward compatibility
   if not hasattr(self			,				"sp_model_kwargs" ):
     UpperCAmelCase__      :					Optional[Any]				       =							{}

   UpperCAmelCase__      :					str				       =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
   self.sp_model.Load(self.vocab_file )







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  str ):



   '''simple docstring'''




   return self.sp_model.encode(snake_case__			,				out_type=snake_case__ )







 def 							UpperCamelCase  (					self		:  str			,				snake_case__		:  Union[str, Any] ):



   '''simple docstring'''




   return self.sp_model.piece_to_id(snake_case__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							self.sp_model.IdToPiece(snake_case__ )
   return token







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							[]
   UpperCAmelCase__      :					str				       =							""
   UpperCAmelCase__      :					Union[str, Any]				       =							False
   for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       if not prev_is_special:
         out_string += " "
       out_string += self.sp_model.decode(snake_case__ ) + token
       UpperCAmelCase__      :					Optional[int]				       =							True
       UpperCAmelCase__      :					Union[str, Any]				       =							[]
     else:
       current_sub_tokens.append(snake_case__ )
       UpperCAmelCase__      :					Union[str, Any]				       =							False
   out_string += self.sp_model.decode(snake_case__ )
   return out_string.strip()







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  List[int]			,				snake_case__		:  bool = False			,				snake_case__		:  bool = None			,				snake_case__		:  bool = True			,				**snake_case__		:  Dict			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							kwargs.pop("use_source_tokenizer"			,				snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							self.convert_ids_to_tokens(snake_case__			,				skip_special_tokens=snake_case__ )

   # To avoid mixing byte-level and unicode for byte-level BPT
   # we need to build string separately for added tokens and byte-level tokens
   # cf. https://github.com/huggingface/transformers/issues/1133
   UpperCAmelCase__      :					Optional[Any]				       =							[]
   UpperCAmelCase__      :					str				       =							[]
   for token in filtered_tokens:
     if skip_special_tokens and token in self.all_special_ids:
       continue
     if token in self.added_tokens_encoder:
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(snake_case__ ) )
         UpperCAmelCase__      :					Any				       =							[]
       sub_texts.append(snake_case__ )
     else:
       current_sub_text.append(snake_case__ )
   if current_sub_text:
     sub_texts.append(self.convert_tokens_to_string(snake_case__ ) )

   # Mimic the behavior of the Rust tokenizer:
   # No space before [MASK] and [SEP]
   if spaces_between_special_tokens:
     UpperCAmelCase__      :					List[Any]				       =							re.sub(R" (\[(MASK|SEP)\])"			,				R"\1"			,				" ".join(snake_case__ ) )
   else:
     UpperCAmelCase__      :					Dict				       =							"".join(snake_case__ )

   UpperCAmelCase__      :					str				       =							(
       clean_up_tokenization_spaces
       if clean_up_tokenization_spaces is not None
       else self.clean_up_tokenization_spaces
   )
   if clean_up_tokenization_spaces:
     UpperCAmelCase__      :					Union[str, Any]				       =							self.clean_up_tokenization(snake_case__ )
     return clean_text
   else:
     return text







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  str			,				snake_case__		:  Optional[str] = None ):



   '''simple docstring'''




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

   if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
     copyfile(self.vocab_file			,				snake_case__ )
   elif not os.path.isfile(self.vocab_file ):
     with open(snake_case__			,				"wb" ) as fi:
       UpperCAmelCase__      :					int				       =							self.sp_model.serialized_model_proto()
       fi.write(snake_case__ )

   return (out_vocab_file,)







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  List[int]			,				snake_case__		:  Optional[List[int]] = None ):



   '''simple docstring'''




   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__      :					int				       =							[self.cls_token_id]
   UpperCAmelCase__      :					List[Any]				       =							[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  List[int]			,				snake_case__		:  Optional[List[int]] = None			,				snake_case__		:  bool = False ):



   '''simple docstring'''




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

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







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  List[int]			,				snake_case__		:  Optional[List[int]] = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							[self.sep_token_id]
   UpperCAmelCase__      :					int				       =							[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 ) * [0] + len(token_ids_a + sep ) * [1]





'''simple docstring'''





import math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    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(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


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



