"""simple docstring"""



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case=True,      snake_case="pt"):
     __snake_case				=							{'''add_prefix_space''': True} if isinstance(snake_case,      snake_case) and not line.startswith(''' ''') else {}
     __snake_case				=							padding_side
     return tokenizer(
         [line],      max_length=snake_case,      padding='''max_length''' if pad_to_max_length else None,      truncation=snake_case,      return_tensors=snake_case,      add_special_tokens=snake_case,      **snake_case,      )





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case=None,      ):
     __snake_case				=							input_ids.ne(snake_case).any(dim=0)
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     int	,      A_							:     int	,      A_							:     Any	,      A_							:     str="train"	,      A_							:     Tuple=None	,      A_							:     int=None	,      A_							:     Union[str, Any]=None	,      A_							:     Union[str, Any]=""	,      )    ->       Optional[int]:
         super().__init__()
         __snake_case				=							Path(A_    ).joinpath(type_path + '''.source'''    )
         __snake_case				=							Path(A_    ).joinpath(type_path + '''.target'''    )
         __snake_case				=							self.get_char_lens(self.src_file    )
         __snake_case				=							max_source_length
         __snake_case				=							max_target_length
         assert min(self.src_lens    ) > 0, f"found empty line in {self.src_file}"
         __snake_case				=							tokenizer
         __snake_case				=							prefix
         if n_obs is not None:
              __snake_case				=							self.src_lens[:n_obs]
         __snake_case				=							src_lang
         __snake_case				=							tgt_lang
    def __len__(		self							:     Any    )    ->       Dict:
         return len(self.src_lens    )
    def __getitem__(		self							:     List[str]	,      A_							:     Tuple    )    ->       Dict[str, torch.Tensor]:
         __snake_case				=							index + 1  # linecache starts at 1
         __snake_case				=							self.prefix + linecache.getline(str(self.src_file    )	,      A_    ).rstrip('''\n'''    )
         __snake_case				=							linecache.getline(str(self.tgt_file    )	,      A_    ).rstrip('''\n'''    )
         assert source_line, f"empty source line for index {index}"
         assert tgt_line, f"empty tgt line for index {index}"

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer	,      A_    ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         __snake_case				=							(
             self.tokenizer.question_encoder if isinstance(self.tokenizer	,      A_    ) else self.tokenizer
         )
         __snake_case				=							self.tokenizer.generator if isinstance(self.tokenizer	,      A_    ) else self.tokenizer

         __snake_case				=							encode_line(A_	,      A_	,      self.max_source_length	,      '''right'''    )
         __snake_case				=							encode_line(A_	,      A_	,      self.max_target_length	,      '''right'''    )

         __snake_case				=							source_inputs['''input_ids'''].squeeze()
         __snake_case				=							target_inputs['''input_ids'''].squeeze()
         __snake_case				=							source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }
    @staticmethod
    def  lowercase					(		A_							:     str    )    ->       Dict:
         return [len(A_    ) for x in Path(A_    ).open().readlines()]







    def  lowercase					(		self							:     int	,      A_							:     int    )    ->       Dict[str, torch.Tensor]:
         __snake_case				=							torch.stack([x['''input_ids'''] for x in batch]    )
         __snake_case				=							torch.stack([x['''attention_mask'''] for x in batch]    )
         __snake_case				=							torch.stack([x['''decoder_input_ids'''] for x in batch]    )
         __snake_case				=							(
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer	,      A_    )
             else self.tokenizer.pad_token_id
         )
         __snake_case				=							(
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer	,      A_    )
             else self.tokenizer.pad_token_id
         )
         __snake_case				=							trim_batch(A_	,      A_    )
         __snake_case		,	__snake_case				=							trim_batch(A_	,      A_	,      attention_mask=A_    )
         __snake_case				=							{
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch






__lowercase		:		Optional[Any]														=							getLogger(__name__)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return list(itertools.chain.from_iterable(snake_case))





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							get_git_info()
     save_json(snake_case,      os.path.join(snake_case,      '''git_log.json'''))





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case=4,      **snake_case):
     with open(snake_case,      '''w''') as f:
          json.dump(snake_case,      snake_case,      indent=snake_case,      **snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     with open(snake_case) as f:
          return json.load(snake_case)





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							git.Repo(search_parent_directories=snake_case)
     __snake_case				=							{
         '''repo_id''': str(snake_case),
         '''repo_sha''': str(repo.head.object.hexsha),
         '''repo_branch''': str(repo.active_branch),
         '''hostname''': str(socket.gethostname()),
     }
     return repo_infos





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return list(map(snake_case,      snake_case))





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     with open(snake_case,      '''wb''') as f:
          return pickle.dump(snake_case,      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):

     def remove_articles(snake_case):
          return re.sub(R'''\b(a|an|the)\b''',      ''' ''',      snake_case)

     def white_space_fix(snake_case):
          return " ".join(text.split())

     def remove_punc(snake_case):
          __snake_case				=							set(string.punctuation)
          return "".join(ch for ch in text if ch not in exclude)

     def lower(snake_case):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(snake_case))))





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							normalize_answer(snake_case).split()
     __snake_case				=							normalize_answer(snake_case).split()
     __snake_case				=							Counter(snake_case) & Counter(snake_case)
     __snake_case				=							sum(common.values())
     if num_same == 0:
          return 0
     __snake_case				=							1.0 * num_same / len(snake_case)
     __snake_case				=							1.0 * num_same / len(snake_case)
     __snake_case				=							(2 * precision * recall) / (precision + recall)
     return fa





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return normalize_answer(snake_case) == normalize_answer(snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     assert len(snake_case) == len(snake_case)
     __snake_case				=							0
     for hypo, pred in zip(snake_case,      snake_case):
          em += exact_match_score(snake_case,      snake_case)
     if len(snake_case) > 0:
          em /= len(snake_case)
     return {"em": em}





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return model_prefix.startswith('''rag''')





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     __snake_case				=							'''dropout_rate'''
     for p in extra_params:
          if getattr(snake_case,      snake_case,      snake_case):
               if not hasattr(snake_case,      snake_case) and not hasattr(snake_case,      equivalent_param[p]):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(snake_case))
                    delattr(snake_case,      snake_case)
                    continue
               __snake_case				=							p if hasattr(snake_case,      snake_case) else equivalent_param[p]
               setattr(snake_case,      snake_case,      getattr(snake_case,      snake_case))
               delattr(snake_case,      snake_case)
     return hparams, config
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

     # Create a Quantum Circuit acting on the q register
     __snake_case				=							qiskit.QuantumCircuit(snake_case,      snake_case)

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

     # Execute the circuit on the simulator
     __snake_case				=							qiskit.execute(snake_case,      snake_case,      shots=10_00)

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


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