



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


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			'''▁'''

__UpperCAmelCase			  =			{'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__UpperCAmelCase			  =			{
    '''facebook/xglm-564M''': 2_048,
}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   List[str]  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   Optional[int]  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   List[Any]  	=    ["input_ids", "attention_mask"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="<unk>",     SCREAMING_SNAKE_CASE_="<pad>",     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          UpperCamelCase       :    Optional[Any]		      =      {} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          UpperCamelCase       :    Any		      =      7
          UpperCamelCase       :    Optional[int]		      =      [F"""<madeupword{i}>""" for i in range(self.num_madeup_words			)]

          UpperCamelCase       :    Dict		      =      kwargs.get('additional_special_tokens',     []			)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          UpperCamelCase       :    List[str]		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(str(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Optional[Any]		      =      vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCamelCase       :    int		      =      1

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCamelCase       :    Dict		      =      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

          UpperCamelCase       :    Optional[int]		      =      len(self.sp_model			)
          UpperCamelCase       :    Any		      =      {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
          self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[str]		      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       def __getstate__(     self			)	->						List[Any]:
          UpperCamelCase       :    int		      =      self.__dict__.copy()
          UpperCamelCase       :    Union[str, Any]		      =      None
          UpperCamelCase       :    int		      =      self.sp_model.serialized_model_proto()
          return state
       def __setstate__(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    Any		      =      d

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

          UpperCamelCase       :    int		      =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:

          if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
          UpperCamelCase       :    Optional[int]		      =      [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False			)	->						List[int]:

          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_			)

          if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			))
          return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			))
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    str		      =      [self.sep_token_id]

          if token_ids_a is None:
             return len(sep + token_ids_a			) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]
       @property
       def    snake_case_			(     self			)	->						int:
          return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      {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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,     out_type=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
          UpperCamelCase       :    Union[str, Any]		      =      self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_			)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          UpperCamelCase       :    Dict		      =      ''.join(SCREAMING_SNAKE_CASE_			).replace(SCREAMING_SNAKE_CASE_,     ' '			).strip()
          return out_string






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
             return
          UpperCamelCase       :    Optional[int]		      =      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:
                UpperCamelCase       :    List[str]		      =      self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_			)

          return (out_vocab_file,)








import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

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

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

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

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

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

				# 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
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


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


