


from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowerCamelCase						 =	9.8_0665




def      a_				(   SCREAMING_SNAKE_CASE__ :				float		,      SCREAMING_SNAKE_CASE__ :				float					):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__		,      (int, float)					):
              raise TypeError('Invalid velocity. Should be a positive number.'					)

       if not isinstance(SCREAMING_SNAKE_CASE__		,      (int, float)					):
              raise TypeError('Invalid angle. Range is 1-90 degrees.'					)

       # Ensure valid angle
       if angle > 90 or angle < 1:
              raise ValueError('Invalid angle. Range is 1-90 degrees.'					)

       # Ensure valid velocity
       if init_velocity < 0:
              raise ValueError('Invalid velocity. Should be a positive number.'					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				float		,      SCREAMING_SNAKE_CASE__ :				float					):
       '''simple docstring'''





       check_args(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =angle_to_radians(2 * angle					)
       return round(init_velocity**2 * sin(SCREAMING_SNAKE_CASE__					) / g		,      2					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				float		,      SCREAMING_SNAKE_CASE__ :				float					):
       '''simple docstring'''





       check_args(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[str]          =angle_to_radians(SCREAMING_SNAKE_CASE__					)
       return round(init_velocity**2 * sin(SCREAMING_SNAKE_CASE__					) ** 2 / (2 * g)		,      2					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				float		,      SCREAMING_SNAKE_CASE__ :				float					):
       '''simple docstring'''





       check_args(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		str          =angle_to_radians(SCREAMING_SNAKE_CASE__					)
       return round(2 * init_velocity * sin(SCREAMING_SNAKE_CASE__					) / g		,      2					)




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase     ,					_lowerCamelCase	:		Optional[int]          =25, 20
       assert horizontal_distance(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) == 40.97
       assert max_height(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) == 3.73
       assert total_time(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) == 1.74


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

 testmod()

 # Get input from user
 lowerCamelCase						 =	float(input('Initial Velocity: ').strip())

 # Get input from user
 lowerCamelCase						 =	float(input('angle: ').strip())

 # Print results
 print()
 print('Results: ')
 print(F"""Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]""")
 print(F"""Maximum Height: {max_height(init_vel, angle)!s} [m]""")
 print(F"""Total Time: {total_time(init_vel, angle)!s} [s]""")






import bisect
import copy
from collections import defaultdict

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

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





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




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    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
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




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


