code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DiffusionPipeline
  from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		bool = False					):
          UpperCamelCase_     =10
  
          if only_cross_attention:
                UpperCamelCase_     =12
          else:
                # when only cross attention is not set, the cross attention dim must be the same as the query dim
                UpperCamelCase_     =query_dim
  
          return {
              "query_dim": query_dim,
              "cross_attention_dim": cross_attention_dim,
              "heads": 2,
              "dim_head": 4,
              "added_kv_proj_dim": 6,
              "norm_num_groups": 1,
              "only_cross_attention": only_cross_attention,
              "processor": AttnAddedKVProcessor(),
          }
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		Optional[Any]				,			UpperCamelCase_:		str					):
          UpperCamelCase_     =2
  
          UpperCamelCase_     =torch.rand(UpperCamelCase_				,			UpperCamelCase_				,			3				,			2					)
          UpperCamelCase_     =torch.rand(UpperCamelCase_				,			4				,			UpperCamelCase_					)
          UpperCamelCase_     =None
  
          return {
              "hidden_states": hidden_states,
              "encoder_hidden_states": encoder_hidden_states,
              "attention_mask": attention_mask,
          }
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[int]					):
          # self and cross attention
  
          torch.manual_seed(0					)
  
          UpperCamelCase_     =self.get_constructor_arguments(only_cross_attention=UpperCamelCase_					)
          UpperCamelCase_     =Attention(**UpperCamelCase_					)
  
          self.assertTrue(attn.to_k is not None					)
          self.assertTrue(attn.to_v is not None					)
  
          UpperCamelCase_     =self.get_forward_arguments(
              query_dim=constructor_args["query_dim"]				,			added_kv_proj_dim=constructor_args["added_kv_proj_dim"]					)
  
          UpperCamelCase_     =attn(**UpperCamelCase_					)
  
          # only self attention
  
          torch.manual_seed(0					)
  
          UpperCamelCase_     =self.get_constructor_arguments(only_cross_attention=UpperCamelCase_					)
          UpperCamelCase_     =Attention(**UpperCamelCase_					)
  
          self.assertTrue(attn.to_k is None					)
          self.assertTrue(attn.to_v is None					)
  
          UpperCamelCase_     =self.get_forward_arguments(
              query_dim=constructor_args["query_dim"]				,			added_kv_proj_dim=constructor_args["added_kv_proj_dim"]					)
  
          UpperCamelCase_     =attn(**UpperCamelCase_					)
  
          self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()					)
  
  
  
  
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Tuple					):
          UpperCamelCase_     =DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"				,			safety_checker=UpperCamelCase_					)
  
          UpperCamelCase_     =pipe(
              "foo"				,			num_inference_steps=2				,			generator=torch.Generator("cpu"					).manual_seed(0					)				,			output_type="np"				,			).images
  
          # the initial conversion succeeds
          UpperCamelCase_     =DiffusionPipeline.from_pretrained(
              "hf-internal-testing/tiny-stable-diffusion-pipe"				,			device_map="sequential"				,			safety_checker=UpperCamelCase_					)
  
          UpperCamelCase_     =pipe(
              "foo"				,			num_inference_steps=2				,			generator=torch.Generator("cpu"					).manual_seed(0					)				,			output_type="np"				,			).images
  
          with tempfile.TemporaryDirectory() as tmpdir:
                # save the converted model
                pipe.save_pretrained(UpperCamelCase_					)
  
                # can also load the converted weights
                UpperCamelCase_     =DiffusionPipeline.from_pretrained(UpperCamelCase_				,			device_map="sequential"				,			safety_checker=UpperCamelCase_					)
  
          UpperCamelCase_     =pipe(
              "foo"				,			num_inference_steps=2				,			generator=torch.Generator("cpu"					).manual_seed(0					)				,			output_type="np"				,			).images
  
          self.assertTrue(np.allclose(UpperCamelCase_				,			UpperCamelCase_				,			atol=1e-5					)					)
          self.assertTrue(np.allclose(UpperCamelCase_				,			UpperCamelCase_				,			atol=1e-5					)					)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from math import radians as angle_to_radians
  from math import sin
  
  # Acceleration Constant on Earth (unit m/s^2)
  A_			  =    9.80665
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
  
     # Ensure valid instance
     if not isinstance(A    ,       (int, float)			):
        raise TypeError("Invalid velocity. Should be a positive number."			)
  
     if not isinstance(A    ,       (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 						_UpperCamelCase			(  A    ,       A			):
     check_args(A    ,       A			)
     UpperCamelCase_     =angle_to_radians(2 * angle			)
     return round(init_velocity**2 * sin(A			) / g    ,       2			)
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     check_args(A    ,       A			)
     UpperCamelCase_     =angle_to_radians(A			)
     return round(init_velocity**2 * sin(A			) ** 2 / (2 * g)    ,       2			)
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     check_args(A    ,       A			)
     UpperCamelCase_     =angle_to_radians(A			)
     return round(2 * init_velocity * sin(A			) / g    ,       2			)
  
  
  
  
  def 						_UpperCamelCase			(  ):
     UpperCamelCase_    ,							UpperCamelCase_     =25, 20
     assert horizontal_distance(A    ,       A			) == 40.97
     assert max_height(A    ,       A			) == 3.73
     assert total_time(A    ,       A			) == 1.74
  
  
  if __name__ == "__main__":
      from doctest import testmod
  
      testmod()
  
      # Get input from user
      A_			  =    float(input("Initial Velocity: ").strip())
  
      # Get input from user
      A_			  =    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]''')
  