




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Dict		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       {"vocab_file": "spm_char.model"}

SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "microsoft/speecht5_asr": 10_24,
    "microsoft/speecht5_tts": 10_24,
    "microsoft/speecht5_vc": 10_24,
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= VOCAB_FILES_NAMES
	A    							= PRETRAINED_VOCAB_FILES_MAP
	A    							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A    							= ['''input_ids''', '''attention_mask''']





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE="<s>"      ,     SCREAMING_SNAKE_CASE="</s>"      ,     SCREAMING_SNAKE_CASE="<unk>"      ,     SCREAMING_SNAKE_CASE="<pad>"      ,     SCREAMING_SNAKE_CASE = None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=SCREAMING_SNAKE_CASE      ,     eos_token=SCREAMING_SNAKE_CASE      ,     unk_token=SCREAMING_SNAKE_CASE      ,     pad_token=SCREAMING_SNAKE_CASE      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **SCREAMING_SNAKE_CASE      ,     )

				SCREAMING_SNAKE_CASE_								=						vocab_file

				SCREAMING_SNAKE_CASE_								=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(SCREAMING_SNAKE_CASE      )





	@property
	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				return self.sp_model.get_piece_size()





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
				vocab.update(self.added_tokens_encoder      )
				return vocab





	def __getstate__(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.__dict__.copy()
				SCREAMING_SNAKE_CASE_								=						None
				return state





	def __setstate__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						d

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

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE      )
				return token





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				SCREAMING_SNAKE_CASE_								=						''
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      ) + token
										SCREAMING_SNAKE_CASE_								=						[]
							else:
										current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
				out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
				return out_string.strip()





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					List[int]:




				"""simple docstring"""



				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      )  ->					List[int]:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						[1]
				if token_ids_a is None:
							return ([0] * len(SCREAMING_SNAKE_CASE      )) + suffix_ones
				return ([0] * len(SCREAMING_SNAKE_CASE      )) + ([0] * len(SCREAMING_SNAKE_CASE      )) + suffix_ones





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					Tuple[str]:




				"""simple docstring"""



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

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

				return (out_vocab_file,)






import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

			# Using Aer's simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.Aer.get_backend('aer_simulator' )

			# Creating a Quantum Circuit acting on the q register
			SCREAMING_SNAKE_CASE_								=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0 )

			for i in range(1       ,    SCREAMING_SNAKE_CASE ):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,    SCREAMING_SNAKE_CASE )

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE ) )       ,    list(range(SCREAMING_SNAKE_CASE ) ) )

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				print(f"""Total count for various states are: {quantum_entanglement(3)}""")
