'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   List[Any]						= params

        lowercase__    :   List[Any]						= np.array(__lowerCAmelCase			)
        lowercase__    :   Dict						= np.array([len(__lowerCAmelCase			) for t in data]			)

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()

       def __getitem__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        return (self.token_ids[index], self.lengths[index])

       def __len__(						self			)  ->  List[Any]:
        return len(self.lengths			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        assert len(self.token_ids			) == len(self.lengths			)
        assert all(self.lengths[i] == len(self.token_ids[i]			) for i in range(len(self.lengths			)			)			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Optional[int]						= self.params.max_model_input_size
        lowercase__    :   Dict						= self.lengths > max_len
        logger.info(F"""Splitting {sum(__lowerCAmelCase			)} too long sequences."""			)

        def divide_chunks(__lowerCAmelCase     ,       __lowerCAmelCase			):
         return [l[i : i + n] for i in range(0     ,       len(__lowerCAmelCase			)     ,       __lowerCAmelCase			)]

        lowercase__    :   List[str]						= []
        lowercase__    :   Tuple						= []
        if self.params.mlm:
         lowercase__    :   Dict						= self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
        else:
         lowercase__    :   Optional[Any]						= self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

        for seq_, len_ in zip(self.token_ids     ,       self.lengths			):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
          new_tok_ids.append(seq_			)
          new_lengths.append(len_			)
         else:
          lowercase__    :   List[str]						= []
          for sub_s in divide_chunks(seq_     ,       max_len - 2			):
           if sub_s[0] != cls_id:
            lowercase__    :   List[Any]						= np.insert(__lowerCAmelCase     ,       0     ,       __lowerCAmelCase			)
           if sub_s[-1] != sep_id:
            lowercase__    :   List[Any]						= np.insert(__lowerCAmelCase     ,       len(__lowerCAmelCase			)     ,       __lowerCAmelCase			)
           assert len(__lowerCAmelCase			) <= max_len
           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
           sub_seqs.append(__lowerCAmelCase			)

          new_tok_ids.extend(__lowerCAmelCase			)
          new_lengths.extend([len(__lowerCAmelCase			) for l in sub_seqs]			)

        lowercase__    :   Tuple						= np.array(__lowerCAmelCase			)
        lowercase__    :   Tuple						= np.array(__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   str						= len(self			)
        lowercase__    :   int						= self.lengths > 11
        lowercase__    :   Optional[int]						= self.token_ids[indices]
        lowercase__    :   int						= self.lengths[indices]
        lowercase__    :   Optional[Any]						= len(self			)
        logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        if "unk_token" not in self.params.special_tok_ids:
         return
        else:
         lowercase__    :   Any						= self.params.special_tok_ids['''unk_token''']
        lowercase__    :   List[str]						= len(self			)
        lowercase__    :   int						= np.array([np.count_nonzero(a == unk_token_id			) for a in self.token_ids]			)
        lowercase__    :   Dict						= (unk_occs / self.lengths) < 0.5
        lowercase__    :   Tuple						= self.token_ids[indices]
        lowercase__    :   str						= self.lengths[indices]
        lowercase__    :   Optional[Any]						= len(self			)
        logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        if not self.params.is_master:
         return
        logger.info(F"""{len(self			)} sequences"""			)
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        lowercase__    :   Any						= [t[0] for t in batch]
        lowercase__    :   Optional[int]						= [t[1] for t in batch]
        assert len(__lowerCAmelCase			) == len(__lowerCAmelCase			)

        # Max for paddings
        lowercase__    :   List[str]						= max(__lowerCAmelCase			)

        # Pad token ids
        if self.params.mlm:
         lowercase__    :   List[str]						= self.params.special_tok_ids['''pad_token''']
        else:
         lowercase__    :   Tuple						= self.params.special_tok_ids['''unk_token''']
        lowercase__    :   Any						= [list(t.astype(__lowerCAmelCase			)			) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase			)) for t in token_ids]
        assert len(tk_			) == len(__lowerCAmelCase			)
        assert all(len(__lowerCAmelCase			) == max_seq_len_ for t in tk_			)

        lowercase__    :   Optional[int]						= torch.tensor(tk_			)  # (bs, max_seq_len_)
        lowercase__    :   Optional[int]						= torch.tensor(__lowerCAmelCase			)  # (bs)
        return tk_t, lg_t


'''simple docstring'''

import qiskit
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= qiskit.Aer.get_backend('''aer_simulator'''					)

 # Create a Quantum Circuit acting on the q register
 lowercase__    :   Any						= qiskit.QuantumCircuit(UpperCAmelCase		,					UpperCAmelCase					)

 # Map the quantum measurement to the classical bits
 circuit.measure([0]		,					[0]					)

 # Execute the circuit on the simulator
 lowercase__    :   Any						= qiskit.execute(UpperCAmelCase		,					UpperCAmelCase		,					shots=1000					)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(UpperCAmelCase					)


if __name__ == "__main__":
  print(F'Total count for various states are: {single_qubit_measure(1, 1)}')

