



import os
import re
import unicodedata
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


UpperCAmelCase							=       logging.get_logger(__name__)
UpperCAmelCase							=       {"""vocab_file""": """spiece.model"""}

UpperCAmelCase							=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

UpperCAmelCase							=       {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCAmelCase							=       """▁"""

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       VOCAB_FILES_NAMES
							UpperCamelCase__					:   List[str]       =       PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__					:   List[Any]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__					:   List[str]       =       ["""input_ids""", """token_type_ids"""]


							def __init__(				self	,	a_	,	a_=False	,	a_=True	,	a_=True	,	a_="<unk>"	,	a_="[SEP]"	,	a_="<pad>"	,	a_="[CLS]"	,	a_="[MASK]"	,	a_ = None	,	**a_	,	):
											# Mask token behave like a normal word, i.e. include the space before it and
											# is included in the raw text, there should be a match in a non-normalized sentence.
											a__					   =		(
											    AddedToken(a_	,	lstrip=a_	,	rstrip=a_	,	normalized=a_		)
											    if isinstance(a_	,	a_		)
											    else mask_token
											)

											a__					   =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=a_	,	remove_space=a_	,	keep_accents=a_	,	unk_token=a_	,	sep_token=a_	,	pad_token=a_	,	cls_token=a_	,	mask_token=a_	,	sp_model_kwargs=self.sp_model_kwargs	,	**a_	,	)

											a__					   =		do_lower_case
											a__					   =		remove_space
											a__					   =		keep_accents
											a__					   =		vocab_file

											a__					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a   (				self		):
											return len(self.sp_model		)


							def _a   (				self		):
											a__					   =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(				self		):
											a__					   =		self.__dict__.copy()
											a__					   =		None
											return state


							def __setstate__(				self	,	a_		):
											a__					   =		d

											# for backward compatibility
											if not hasattr(self	,	"""sp_model_kwargs"""		):
															a__					   =		{}

											a__					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a   (				self	,	a_		):
											if self.remove_space:
															a__					   =		""" """.join(inputs.strip().split()		)
											else:
															a__					   =		inputs
											a__					   =		outputs.replace("""``"""	,	"""\""""		).replace("""''"""	,	"""\""""		)

											if not self.keep_accents:
															a__					   =		unicodedata.normalize("""NFKD"""	,	a_		)
															a__					   =		"""""".join([c for c in outputs if not unicodedata.combining(a_		)]		)
											if self.do_lower_case:
															a__					   =		outputs.lower()

											return outputs


							def _a   (				self	,	a_		):
											a__					   =		self.preprocess_text(a_		)
											a__					   =		self.sp_model.encode(a_	,	out_type=a_		)
											a__					   =		[]
											for piece in pieces:
															if len(a_		) > 1 and piece[-1] == str(""","""		) and piece[-2].isdigit():
																			a__					   =		self.sp_model.EncodeAsPieces(piece[:-1].replace(a_	,	""""""		)		)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																							if len(cur_pieces[0]		) == 1:
																											a__					   =		cur_pieces[1:]
																							else:
																											a__					   =		cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]		)
																			new_pieces.extend(a_		)
															else:
																			new_pieces.append(a_		)

											return new_pieces


							def _a   (				self	,	a_		):
											return self.sp_model.PieceToId(a_		)


							def _a   (				self	,	a_		):
											return self.sp_model.IdToPiece(a_		)


							def _a   (				self	,	a_		):
											a__					   =		[]
											a__					   =		""""""
											a__					   =		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(a_		) + token
																			a__					   =		True
																			a__					   =		[]
															else:
																			current_sub_tokens.append(a_		)
																			a__					   =		False
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()


							def _a   (				self	,	a_	,	a_ = False	,	a_ = None	,	a_ = True	,	**a_	,	):
											a__					   =		kwargs.pop("""use_source_tokenizer"""	,	a_		)

											a__					   =		self.convert_ids_to_tokens(a_	,	skip_special_tokens=a_		)

											# 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
											a__					   =		[]
											a__					   =		[]
											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(a_		)		)
																							a__					   =		[]
																			sub_texts.append(a_		)
															else:
																			current_sub_text.append(a_		)
											if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(a_		)		)

											# Mimic the behavior of the Rust tokenizer:
											# No space after <unk>
											if spaces_between_special_tokens:
															a__					   =		re.sub(r"""(<unk>) """	,	r"""\1"""	,	""" """.join(a_		)		)
											else:
															a__					   =		"""""".join(a_		)

											a__					   =		(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
															a__					   =		self.clean_up_tokenization(a_		)
															return clean_text
											else:
															return text


							def _a   (				self	,	a_	,	a_ = None		):
											a__					   =		[self.sep_token_id]
											a__					   =		[self.cls_token_id]
											if token_ids_a is None:
															return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):

											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 not None:
															return [1] + ([0] * len(a_		)) + [1] + ([0] * len(a_		)) + [1]
											return [1] + ([0] * len(a_		)) + [1]


							def _a   (				self	,	a_	,	a_ = None		):
											a__					   =		[self.sep_token_id]
											a__					   =		[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]







							def _a   (				self	,	a_	,	a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__					   =		os.path.join(
											    a_	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

											return (out_vocab_file,)








'''simple docstring'''


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




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


  '''simple docstring'''




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

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

  # Build the circuit
  quantum_circuit.u(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,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
  __lowerCAmelCase		:    List[str]			     =  Aer.get_backend('aer_simulator'					)
  __lowerCAmelCase		:    Optional[Any]			     =  execute(_UpperCAmelCase						,_UpperCAmelCase						,shots=1_0_0_0					)

  return job.result().get_counts(_UpperCAmelCase					)


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





