code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from collections import namedtuple
  
  _lowerCamelCase				:	Dict				    =       namedtuple('from_to', 'from_ to')
  
  _lowerCamelCase				:	Tuple				    =       {
      'atm': from_to(1, 1),
      'pascal': from_to(0.0_0_0_0_0_9_8, 10_1325),
      'bar': from_to(0.9_8_6_9_2_3, 1.0_1_3_2_5),
      'kilopascal': from_to(0.0_0_9_8_6_9_2_3, 101.325),
      'megapascal': from_to(9.8_6_9_2_3, 0.1_0_1_3_2_5),
      'psi': from_to(0.0_6_8_0_4_6, 14.6959),
      'inHg': from_to(0.0_3_3_4_2_1_1, 29.9213),
      'torr': from_to(0.0_0_1_3_1_5_7_9, 760),
  }
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if from_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
  		    + """, """.join(UpperCAmelCase    )    )
  	if to_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
  		    + """, """.join(UpperCAmelCase    )    )
  	return (
  	    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  	)
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
style_context: 
  
  
  
  
  
  '''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 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      bool = False      ):
  			A							  =      10
  
  			if only_cross_attention:
  				A							  =      12
  			else:
  				# when only cross attention is not set, the cross attention dim must be the same as the query dim
  				A							  =      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        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			A							  =      2
  
  			A							  =      torch.rand(_lowerCAmelCase      ,      _lowerCAmelCase      ,      3      ,      2      )
  			A							  =      torch.rand(_lowerCAmelCase      ,      4      ,      _lowerCAmelCase      )
  			A							  =      None
  
  			return {
  			    "hidden_states": hidden_states,
  			    "encoder_hidden_states": encoder_hidden_states,
  			    "attention_mask": attention_mask,
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			# self and cross attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is not None      )
  			self.assertTrue(attn.to_v is not None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			# only self attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is None      )
  			self.assertTrue(attn.to_v is None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			A							  =      DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			# the initial conversion succeeds
  			A							  =      DiffusionPipeline.from_pretrained(
  			    """hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      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(_lowerCAmelCase      )
  
  				# can also load the converted weights
  				A							  =      DiffusionPipeline.from_pretrained(_lowerCAmelCase      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  
  