




"""simple docstring"""






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		"""▁"""

lowercase__			=		{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

lowercase__			=		{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

lowercase__			=		{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

lowercase__			=		["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

lowercase__			=		{"""mustc""": MUSTC_LANGS}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     VOCAB_FILES_NAMES
			lowerCamelCase__ =     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__ =     MAX_MODEL_INPUT_SIZES
			lowerCamelCase__ =     ["""input_ids""", """attention_mask"""]

			lowerCamelCase__ =     []
			def __init__(    self    , lowercase    , lowercase    , lowercase="<s>"    , lowercase="</s>"    , lowercase="<pad>"    , lowercase="<unk>"    , lowercase=False    , lowercase=False    , lowercase=None    , lowercase=None    , lowercase = None    , **lowercase    , ):
							_lowerCamelCase	:     List[Any]       =       {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase    , eos_token=lowercase    , unk_token=lowercase    , pad_token=lowercase    , do_upper_case=lowercase    , do_lower_case=lowercase    , tgt_lang=lowercase    , lang_codes=lowercase    , sp_model_kwargs=self.sp_model_kwargs    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       do_upper_case
							_lowerCamelCase	:     int       =       do_lower_case

							_lowerCamelCase	:     Optional[int]       =       load_json(lowercase    )
							_lowerCamelCase	:     int       =       {v: k for k, v in self.encoder.items()}
							_lowerCamelCase	:     Tuple       =       spm_file
							_lowerCamelCase	:     Optional[int]       =       load_spm(lowercase    , self.sp_model_kwargs    )

							if lang_codes is not None:
											_lowerCamelCase	:     Optional[Any]       =       lang_codes
											_lowerCamelCase	:     Tuple       =       LANGUAGES[lang_codes]
											_lowerCamelCase	:     List[Any]       =       [F'''<lang:{lang}>''' for lang in self.langs]
											_lowerCamelCase	:     Optional[int]       =       {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''    ) for lang in self.langs}

											_lowerCamelCase	:     Union[str, Any]       =       self.lang_tokens
											_lowerCamelCase	:     List[Any]       =       tgt_lang if tgt_lang is not None else self.langs[0]

											self.set_tgt_lang_special_tokens(self._tgt_lang    )
							else:
											_lowerCamelCase	:     Optional[Any]       =       {}
			@property
			def    A_       (    self    ):
							return len(self.encoder    )
			@property
			def    A_       (    self    ):
							return self._tgt_lang
			@tgt_lang.setter
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     str       =       new_tgt_lang
							self.set_tgt_lang_special_tokens(lowercase    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       self.lang_code_to_id[tgt_lang]
							_lowerCamelCase	:     Dict       =       [lang_code_id]
			def    A_       (    self    , lowercase    ):
							return self.sp_model.encode(lowercase    , out_type=lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.encoder.get(lowercase    , self.encoder[self.unk_token]    )
			def    A_       (    self    , lowercase    ):
							return self.decoder.get(lowercase    , self.unk_token    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Dict       =       []
							_lowerCamelCase	:     Dict       =       ''
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															_lowerCamelCase	:     List[Any]       =       self.sp_model.decode(lowercase    )
															out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
															_lowerCamelCase	:     Any       =       []
											else:
															current_sub_tokens.append(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       self.sp_model.decode(lowercase    )
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()
			def    A_       (    self    , lowercase    , lowercase=None    ):
							if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
			def    A_       (    self    , lowercase    , lowercase = None    , lowercase = False    ):

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

							_lowerCamelCase	:     Union[str, Any]       =       [1] * len(self.prefix_tokens    )
							_lowerCamelCase	:     Dict       =       [1]
							if token_ids_a is None:
											return prefix_ones + ([0] * len(lowercase    )) + suffix_ones
							return prefix_ones + ([0] * len(lowercase    )) + ([0] * len(lowercase    )) + suffix_ones
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       self.encoder.copy()
							vocab.update(self.added_tokens_encoder    )
							return vocab
			def __getstate__(    self    ):
							_lowerCamelCase	:     List[str]       =       self.__dict__.copy()
							_lowerCamelCase	:     int       =       None
							return state
			def __setstate__(    self    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       d

							# for backward compatibility
							if not hasattr(self    , 'sp_model_kwargs'    ):
											_lowerCamelCase	:     Union[str, Any]       =       {}

							_lowerCamelCase	:     str       =       load_spm(self.spm_file    , self.sp_model_kwargs    )






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     List[str]       =       Path(lowercase    )
							assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
							_lowerCamelCase	:     int       =       save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
							)
							_lowerCamelCase	:     Tuple       =       save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
							)

							save_json(self.encoder    , lowercase    )

							if os.path.abspath(self.spm_file    ) != os.path.abspath(lowercase    ) and os.path.isfile(self.spm_file    ):
											copyfile(self.spm_file    , lowercase    )
							elif not os.path.isfile(self.spm_file    ):
											with open(lowercase    , 'wb'    ) as fi:
															_lowerCamelCase	:     int       =       self.sp_model.serialized_model_proto()
															fi.write(lowercase    )

							return (str(lowercase    ), str(lowercase    ))







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       sentencepiece.SentencePieceProcessor(**lowercase__						)
				spm.Load(str(lowercase__						)						)
				return spm







def 	_snake_case     (							lowercase__						):
				with open(lowercase__				,       'r'						) as f:
								return json.load(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				with open(lowercase__				,       'w'						) as f:
								json.dump(lowercase__				,       lowercase__				,       indent=2						)





"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

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

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

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

				# 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
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


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