


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_fnet import FNetTokenizer
else:
		SCREAMING_SNAKE_CASE__     					=			None

SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__     					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__     					=			{
    "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",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

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

SCREAMING_SNAKE_CASE__     					=			"▁"



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Optional[int]       			=  VOCAB_FILES_NAMES
		lowerCamelCase_	:			List[str]       			=  PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase_	:			List[str]       			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase_	:			Optional[int]       			=  ["""input_ids""", """token_type_ids"""]
		lowerCamelCase_	:			Optional[int]       			=  FNetTokenizer






		def __init__(						self				:     Optional[Any]   ,  UpperCAmelCase				:     List[str]=None   ,  UpperCAmelCase				:     str=None   ,  UpperCAmelCase				:     Any=False   ,  UpperCAmelCase				:     Dict=True   ,  UpperCAmelCase				:     Any=True   ,  UpperCAmelCase				:     Optional[int]="<unk>"   ,  UpperCAmelCase				:     int="[SEP]"   ,  UpperCAmelCase				:     str="<pad>"   ,  UpperCAmelCase				:     Dict="[CLS]"   ,  UpperCAmelCase				:     Any="[MASK]"   ,  **UpperCAmelCase				:     Union[str, Any]   ,  ):
				# 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.
				SCREAMING_SNAKE_CASE_				:Tuple				     =						(
				    AddedToken(UpperCAmelCase   ,  lstrip=UpperCAmelCase   ,  rstrip=UpperCAmelCase   ,  normalized=UpperCAmelCase)
				    if isinstance(UpperCAmelCase   ,  UpperCAmelCase)
				    else mask_token
				)

				super().__init__(
				    UpperCAmelCase   ,  tokenizer_file=UpperCAmelCase   ,  do_lower_case=UpperCAmelCase   ,  remove_space=UpperCAmelCase   ,  keep_accents=UpperCAmelCase   ,  unk_token=UpperCAmelCase   ,  sep_token=UpperCAmelCase   ,  pad_token=UpperCAmelCase   ,  cls_token=UpperCAmelCase   ,  mask_token=UpperCAmelCase   ,  **UpperCAmelCase   ,  )

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						do_lower_case
				SCREAMING_SNAKE_CASE_				:str				     =						remove_space
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						keep_accents
				SCREAMING_SNAKE_CASE_				:List[str]				     =						vocab_file
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						False if not self.vocab_file else True






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     List[int]   ,  UpperCAmelCase				:     Optional[List[int]] = None):
				SCREAMING_SNAKE_CASE_				:int				     =						[self.sep_token_id]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[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 _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[int]   ,  UpperCAmelCase				:     Optional[List[int]] = None):
				SCREAMING_SNAKE_CASE_				:Any				     =						[self.sep_token_id]
				SCREAMING_SNAKE_CASE_				:List[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) * [0] + len(token_ids_a + sep) * [1]







		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Optional[str] = None):
				if not os.path.isdir(UpperCAmelCase):
						logger.error(F"Vocabulary path ({save_directory}) should be a directory")
						return
				SCREAMING_SNAKE_CASE_				:str				     =						os.path.join(
				    UpperCAmelCase   ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

				return (out_vocab_file,)










import qiskit


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						qiskit.Aer.get_backend("aer_simulator" )

		# Create a Quantum Circuit acting on the q register
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						qiskit.QuantumCircuit(a     , a )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0 )
		circuit.x(1 )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]     , [0, 1] )

		# Execute the circuit on the qasm simulator
		SCREAMING_SNAKE_CASE_				:Any				     =						qiskit.execute(a     , a     , shots=1000 )

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			single_qubit_measure(2, 2)
		print(F'''Total count for various states are: {counts}''')






