code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      str      ):
  			# numbermap keys are the number and its values are lists of indexes sorted
  			# in ascending order
  			A							  =      {}
  			# indexmap keys are an index and it's values are the number at that index
  			A							  =      {}
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      Optional[Any]      ,      _lowerCAmelCase						:      int      ):
  			if isinstance(__A      ,      (range, str)      ):
  				A							  =      list(__A      )
  			elif not isinstance(__A      ,      __A      ):
  				raise TypeError(
  				    """binary_search_delete() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(__A      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == item:
  					array.pop(__A      )
  					return array
  				elif array[mid] < item:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  			raise ValueError(
  			    """Either the item is not in the array or the array was unsorted"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Optional[Any]      ):
  			if isinstance(__A      ,      (range, str)      ):
  				A							  =      list(__A      )
  			elif not isinstance(__A      ,      __A      ):
  				raise TypeError(
  				    """binary_search_insert() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(__A      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == index:
  					# If the item already exists in the array,
  					# insert it after the existing item
  					array.insert(mid + 1      ,      __A      )
  					return array
  				elif array[mid] < index:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  
          # If the item doesn't exist in the array, insert it at the appropriate position
  			array.insert(__A      ,      __A      )
  			return array
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Optional[int]      ):
  			# Remove previous index
  			if index in self.indexmap:
  				A							  =      self.indexmap[index]
  				if len(self.numbermap[n]      ) == 1:
  					del self.numbermap[n]
  				else:
  					A							  =      self.binary_search_delete(self.numbermap[n]      ,      __A      )
  
          # Set new index
  			A							  =      number
  
  			# Number not seen before or empty so insert number value
  			if number not in self.numbermap:
  				A							  =      [index]
  
  			# Here we need to perform a binary search insertion in order to insert
  			# The item in the correct place
  			else:
  				A							  =      self.binary_search_insert(
  				    self.numbermap[number]      ,      __A      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[Any]      ):
  			return self.numbermap.get(__A      ,      [-1]      )[0]
  
  
  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      )      )
  
  