





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

__UpperCAmelCase	:       Any    	=							logging.get_logger(__name__)


__UpperCAmelCase	:       List[str]    	=							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__UpperCAmelCase	:       List[str]    	=							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__UpperCAmelCase	:       Union[str, Any]    	=							{"facebook/blenderbot-3B": 128}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					VOCAB_FILES_NAMES
       lowerCAmelCase__						=					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						=					["""input_ids""", """attention_mask"""]
       lowerCAmelCase__						=					BlenderbotTokenizer




       def __init__(  self			:   Optional[Any]							,    A			:   int=None							,    A			:   Tuple=None							,    A			:   str=None							,    A			:   Any="replace"							,    A			:   Union[str, Any]="<s>"							,    A			:   List[Any]="</s>"							,    A			:   str="</s>"							,    A			:   Any="<s>"							,    A			:   Any="<unk>"							,    A			:   Any="<pad>"							,    A			:   Any="<mask>"							,    A			:   Optional[int]=False							,    A			:   Optional[Any]=True							,    **A			:   List[Any]							,    ):
         super().__init__(
             A							,    A							,    tokenizer_file=A							,    errors=A							,    bos_token=A							,    eos_token=A							,    sep_token=A							,    cls_token=A							,    unk_token=A							,    pad_token=A							,    mask_token=A							,    add_prefix_space=A							,    trim_offsets=A							,    **A							,    )

         __snake_case:						int			  =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
         if pre_tok_state.get("""add_prefix_space"""							,    A		) != add_prefix_space:
           __snake_case:						Union[str, Any]			  =						getattr(A							,    pre_tok_state.pop("""type"""		)		)
           __snake_case:						Tuple			  =						add_prefix_space
           __snake_case:						Dict			  =						pre_tok_class(**A		)

         __snake_case:						List[Any]			  =						add_prefix_space

         __snake_case:						Any			  =						"""post_processor"""
         __snake_case:						int			  =						getattr(self.backend_tokenizer							,    A							,    A		)
         if tokenizer_component_instance:
           __snake_case:						Union[str, Any]			  =						json.loads(tokenizer_component_instance.__getstate__()		)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             __snake_case:						Union[str, Any]			  =						tuple(state["""sep"""]		)
           if "cls" in state:
             __snake_case:						Dict			  =						tuple(state["""cls"""]		)

           __snake_case:						Dict			  =						False

           if state.get("""add_prefix_space"""							,    A		) != add_prefix_space:
             __snake_case:						str			  =						add_prefix_space
             __snake_case:						Any			  =						True

           if state.get("""trim_offsets"""							,    A		) != trim_offsets:
             __snake_case:						Optional[Any]			  =						trim_offsets
             __snake_case:						Any			  =						True

           if changes_to_apply:
             __snake_case:						Tuple			  =						getattr(A							,    state.pop("""type"""		)		)
             __snake_case:						Optional[int]			  =						component_class(**A		)
             setattr(self.backend_tokenizer							,    A							,    A		)




       @property
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         if self._mask_token is None:
           if self.verbose:
             logger.error("""Using mask_token, but it is not set yet."""		)
           return None
         return str(self._mask_token		)




       @mask_token.setter
       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   Any		):
         __snake_case:						List[str]			  =						AddedToken(A							,    lstrip=A							,    rstrip=A		) if isinstance(A							,    A		) else value
         __snake_case:						List[Any]			  =						value




       def 							UpperCAmelCase__			(  self			:   Tuple							,    *A			:   List[str]							,    **A			:   Dict		):
         __snake_case:						Dict			  =						kwargs.get("""is_split_into_words"""							,    A		)
         assert self.add_prefix_space or not is_split_into_words, (
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   Tuple							,    *A			:   List[Any]							,    **A			:   Tuple		):
         __snake_case:						Optional[int]			  =						kwargs.get("""is_split_into_words"""							,    A		)

         assert self.add_prefix_space or not is_split_into_words, (
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   str							,    A			:   Optional[str] = None		):
         __snake_case:						Optional[Any]			  =						self._tokenizer.model.save(A							,    name=A		)
         return tuple(A		)




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   List[int]							,    A			:   Optional[List[int]] = None		):
         __snake_case:						Union[str, Any]			  =						[self.sep_token_id]
         __snake_case:						List[Any]			  =						[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 + sep + token_ids_a + sep		) * [0]




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   List[int]							,    A			:   Optional[List[int]] = None		):
         return token_ids_a + [self.eos_token_id]






       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   "Conversation"		):
         __snake_case:						Tuple			  =						[]
         for is_user, text in conversation.iter_texts():
           if is_user:
             # We need to space prefix as it's being done within blenderbot
             inputs.append(""" """ + text		)
           else:
             # Generated responses should contain them already.
             inputs.append(A		)

         __snake_case:						List[Any]			  =						"""  """.join(A		)
         __snake_case:						int			  =						self.encode(A		)
         if len(A		) > self.model_max_length:
           __snake_case:						str			  =						input_ids[-self.model_max_length :]
           logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''		)
         return input_ids








import math

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







def       A__							(   SCREAMING_SNAKE_CASE__ = 3)       ->   qiskit.result.counts.Counts:
  if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    raise TypeError("""number of qubits must be a integer.""")
  if number_of_qubits <= 0:
    raise ValueError("""number of qubits must be > 0.""")
  if math.floor(SCREAMING_SNAKE_CASE__) != number_of_qubits:
    raise ValueError("""number of qubits must be exact integer.""")
  if number_of_qubits > 10:
    raise ValueError("""number of qubits too large to simulate(>10).""")

  __snake_case:						int			  =						QuantumRegister(SCREAMING_SNAKE_CASE__ , """qr""")
  __snake_case:						List[str]			  =						ClassicalRegister(SCREAMING_SNAKE_CASE__ , """cr""")

  __snake_case:						Optional[Any]			  =						QuantumCircuit(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple			  =						number_of_qubits

  for i in range(SCREAMING_SNAKE_CASE__):
    quantum_circuit.h(number_of_qubits - i - 1)
    counter -= 1
    for j in range(SCREAMING_SNAKE_CASE__):
      quantum_circuit.cp(np.pi / 2 ** (counter - j) , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  for k in range(number_of_qubits // 2):
    quantum_circuit.swap(SCREAMING_SNAKE_CASE__ , number_of_qubits - k - 1)

  # measure all the qubits
  quantum_circuit.measure(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  # simulate with 10000 shots
  __snake_case:						Union[str, Any]			  =						Aer.get_backend("""qasm_simulator""")
  __snake_case:						Optional[Any]			  =						execute(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , shots=1_0000)

  return job.result().get_counts(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(
          f'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
      )






