




"""simple docstring"""


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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A_      =    logging.get_logger(__name__)

A_      =    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_      =    {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

A_      =    {
    '''Salesforce/codegen-350M-mono''': 20_48,
}






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = VOCAB_FILES_NAMES
 lowercase__			 = PRETRAINED_VOCAB_FILES_MAP
 lowercase__			 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__			 = ["input_ids", "attention_mask"]
 lowercase__			 = CodeGenTokenizer


 def __init__(    self:		Union[str, Any],				a_:		Optional[int]=None,				a_:		Dict=None,				a_:		Any=None,				a_:		List[Any]="<|endoftext|>",				a_:		Dict="<|endoftext|>",				a_:		int="<|endoftext|>",				a_:		Dict=False,				**a_:		Optional[int],				):




   '''simple docstring'''



   super().__init__(
       a_,				a_,				tokenizer_file=a_,				unk_token=a_,				bos_token=a_,				eos_token=a_,				add_prefix_space=a_,				**a_,				)

   if kwargs.pop("""add_bos_token""",				a_		):
     _snake_case							:		List[Any]								=  kwargs.pop("""name_or_path""",				""""""		)
     raise ValueError(
         """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
         """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
         f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
         f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
         """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
         """ so that the fast tokenizer works correctly."""		)

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

   _snake_case							:		Union[str, Any]								=  add_prefix_space


 def 			UpperCamelCase_       (    self:		Optional[Any],				*a_:		Any,				**a_:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		str								=  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:		Union[str, Any],				*a_:		Optional[int],				**a_:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  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:		List[str],				a_:		str,				a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self._tokenizer.model.save(a_,				name=a_		)
   return tuple(a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],				a_:		bool = False,				a_:		bool = None,				a_:		Optional[List[str]] = None,				**a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		List[str]								=  super().decode(
       token_ids=a_,				skip_special_tokens=a_,				clean_up_tokenization_spaces=a_,				**a_,				)

   if truncate_before_pattern is not None and len(a_		) > 0:
     _snake_case							:		str								=  self.truncate(a_,				a_		)

   return decoded_text


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		int,				a_:		int		):




   '''simple docstring'''



   def find_re(a_:		str,				a_:		List[str],				a_:		Any		):
     _snake_case							:		List[Any]								=  pattern.search(a_,				a_		)
     return m.start() if m else -1

   _snake_case							:		List[Any]								=  [re.compile(a_,				re.MULTILINE		) for pattern in truncate_before_pattern]

   _snake_case							:		Union[str, Any]								=  list(re.finditer("""^print""",				a_,				re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		Dict								=  completion[: prints[1].start()]

   _snake_case							:		List[Any]								=  list(re.finditer("""^def""",				a_,				re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		Optional[int]								=  completion[: defs[1].start()]

   _snake_case							:		Dict								=  0

   _snake_case							:		str								=  [
       pos for pos in [find_re(a_,				a_,				a_		) for terminal in terminals] if pos != -1
   ]

   if len(a_		) > 0:
     return completion[: min(a_		)]
   else:
     return completion








"""simple docstring"""


import math

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


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  if isinstance(snake_case__		,       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(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							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(snake_case__		,       snake_case__   )
  # simulate with 10000 shots
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


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


