
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = params

       _SCREAMING_SNAKE_CASE   :				List[str]							    = np.array(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = np.array([len(snake_case__				) 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  ,     snake_case__				):




       """simple docstring"""


       return (self.token_ids[index], self.lengths[index])
     def __len__(     self				):




       """simple docstring"""


       return len(self.lengths				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self.params.max_model_input_size
       _SCREAMING_SNAKE_CASE   :				int							    = self.lengths > max_len
       logger.info(F'''Splitting {sum(snake_case__				)} too long sequences.'''				)

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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
       if self.params.mlm:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
       else:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
           for sub_s in divide_chunks(seq_  ,     max_len - 2				):
             if sub_s[0] != cls_id:
               _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.insert(snake_case__  ,     0  ,     snake_case__				)
             if sub_s[-1] != sep_id:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = np.insert(snake_case__  ,     len(snake_case__				)  ,     snake_case__				)
             assert len(snake_case__				) <= max_len
             assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
             sub_seqs.append(snake_case__				)

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

       _SCREAMING_SNAKE_CASE   :				Any							    = np.array(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = np.array(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = len(self				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.lengths > 11
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.token_ids[indices]
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.lengths[indices]
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = len(self				)
       logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [t[0] for t in batch]
       _SCREAMING_SNAKE_CASE   :				int							    = [t[1] for t in batch]
       assert len(snake_case__				) == len(snake_case__				)

       # Max for paddings
       _SCREAMING_SNAKE_CASE   :				int							    = max(snake_case__				)

       # Pad token ids
       if self.params.mlm:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.params.special_tok_ids["pad_token"]
       else:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.params.special_tok_ids["unk_token"]
       _SCREAMING_SNAKE_CASE   :				Dict							    = [list(t.astype(snake_case__				)				) + [pad_idx] * (max_seq_len_ - len(snake_case__				)) for t in token_ids]
       assert len(tk_				) == len(snake_case__				)
       assert all(len(snake_case__				) == max_seq_len_ for t in tk_				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = torch.tensor(tk_				)  # (bs, max_seq_len_)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = torch.tensor(snake_case__				)  # (bs)
       return tk_t, lg_t





"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt





def    _lowerCAmelCase    (     lowerCamelCase__							:							Sequence[float],						lowerCamelCase__							:							int,						lowerCamelCase__							:							int				)  ->    tuple[int | None, int | None, float]:
  if not arr:
    return None, None, 0
  if low == high:
    return low, high, arr[low]

  _SCREAMING_SNAKE_CASE   :				Any							    = (low + high) // 2
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = max_subarray(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = max_subarray(lowerCamelCase__,						mid + 1,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = max_cross_sum(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  if left_sum >= right_sum and left_sum >= cross_sum:
    return left_low, left_high, left_sum
  elif right_sum >= left_sum and right_sum >= cross_sum:
    return right_low, right_high, right_sum
  return cross_left, cross_right, cross_sum





def    _lowerCAmelCase    (     lowerCamelCase__							:							Sequence[float],						lowerCamelCase__							:							int,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int				)  ->    tuple[int, int, float]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = float("-inf"				), -1
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = float("-inf"				), -1

  _SCREAMING_SNAKE_CASE   :				int | float							    = 0
  for i in range(lowerCamelCase__,						low - 1,						-1				):
    summ += arr[i]
    if summ > left_sum:
      _SCREAMING_SNAKE_CASE   :				int							    = summ
      _SCREAMING_SNAKE_CASE   :				Tuple							    = i

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 0
  for i in range(mid + 1,						high + 1				):
    summ += arr[i]
    if summ > right_sum:
      _SCREAMING_SNAKE_CASE   :				int							    = summ
      _SCREAMING_SNAKE_CASE   :				Any							    = i

  return max_left, max_right, (left_sum + right_sum)





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    float:
  _SCREAMING_SNAKE_CASE   :				Any							    = [randint(1,						lowerCamelCase__				) for _ in range(lowerCamelCase__				)]
  _SCREAMING_SNAKE_CASE   :				int							    = time.time()
  max_subarray(lowerCamelCase__,						0,						input_size - 1				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = time.time()
  return end - start





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
  _SCREAMING_SNAKE_CASE   :				int							    = [time_max_subarray(lowerCamelCase__				) for input_size in input_sizes]
  print("No of Inputs\t\tTime Taken"				)
  for input_size, runtime in zip(lowerCamelCase__,						lowerCamelCase__				):
    print(lowerCamelCase__,						"\t\t",						lowerCamelCase__				)
  plt.plot(lowerCamelCase__,						lowerCamelCase__				)
  plt.xlabel("Number of Inputs"				)
  plt.ylabel("Time taken in seconds"				)
  plt.show()


if __name__ == "__main__":
      from doctest import testmod

      testmod()



