



'''simple docstring'''







def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # "Simpson Rule"
    # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
    UpperCamelCase__	     =      (boundary[1] - boundary[0]) / steps
    UpperCamelCase__	     =      boundary[0]
    UpperCamelCase__	     =      boundary[1]
    UpperCamelCase__	     =      make_points(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)
    UpperCamelCase__	     =      0.0
    y += (h / 3.0) * f(lowerCAmelCase_						)
    UpperCamelCase__	     =      2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(lowerCAmelCase_						)
        cnt += 1
    y += (h / 3.0) * f(lowerCAmelCase_						)
    return y





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      a + h
    while x < (b - h):
        yield x
        UpperCamelCase__	     =      x + h





def     snake_case__			(lowerCAmelCase_						):  # enter your function here
    UpperCamelCase__	     =      (x - 0) * (x - 0)
    return y





def     snake_case__			():
    UpperCamelCase__	     =      0.0  # Lower bound of integration
    UpperCamelCase__	     =      1.0  # Upper bound of integration
    UpperCamelCase__	     =      10.0  # define number of steps or resolution
    UpperCamelCase__	     =      [a, b]  # define boundary of integration
    UpperCamelCase__	     =      method_a(lowerCAmelCase_       ,	lowerCAmelCase_						)
    print(F'y = {y}'						)


if __name__ == "__main__":
      main()









'''simple docstring'''







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      copy.deepcopy(lowerCAmelCase_						)
    UpperCamelCase__	     =      sorted(lowerCAmelCase_						)
    UpperCamelCase__	     =      [bisect.bisect_right(lowerCAmelCase_       ,	lowerCAmelCase_						) for y in x]
    return quantized





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_=0						):
    UpperCamelCase__	     =      np.arange(start=3       ,	stop=lowerCAmelCase_       ,	step=4						).tolist() if k > 0 else [10]
    UpperCamelCase__	     =      _quantize(lowerCAmelCase_       ,	lowerCAmelCase_						)
    # count number of elements per group
    UpperCamelCase__	     =      np.unique(lowerCAmelCase_       ,	return_counts=lowerCAmelCase_						)[1]
    UpperCamelCase__	     =      [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(lowerCAmelCase_						)						)
    logger.info("Count of instances per bin: {}".format(lowerCAmelCase_						)						)
    return groups





class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       if not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__UpperCamelCase			)			)
       UpperCamelCase__	     =      sampler
       UpperCamelCase__	     =      group_ids
       UpperCamelCase__	     =      batch_size


   def __iter__(	self			):
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)

       UpperCamelCase__	     =      0
       for idx in self.sampler:
           UpperCamelCase__	     =      self.group_ids[idx]
           buffer_per_group[group_id].append(__UpperCamelCase			)
           samples_per_group[group_id].append(__UpperCamelCase			)
           if len(buffer_per_group[group_id]			) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]			) < self.batch_size

       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       UpperCamelCase__	     =      len(self			)
       UpperCamelCase__	     =      expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           UpperCamelCase__	     =      []
           for group_id, idxs in sorted(buffer_per_group.items()  ,    key=lambda __UpperCamelCase			: x[0]			):
               batch_idx.extend(__UpperCamelCase			)
               if len(__UpperCamelCase			) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   UpperCamelCase__	     =      batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(__UpperCamelCase			) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0







   def __len__(	self			):
       return (len(self.sampler			) + self.batch_size - 1) // self.batch_size




