


class 							lowerCamelCase   :


	def __init__(    self				,     lowercase__ = ""				,     lowercase__ = False):
							__UpperCAmelCase			:							dict[str, RadixNode]	    =	{}

							# A node will be a leaf if the tree contains its word
							__UpperCAmelCase			:							str	    =	is_leaf

							__UpperCAmelCase			:							Optional[int]	    =	prefix


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	0
							for q, w in zip(self.prefix				,     __UpperCAmelCase):
													if q != w:
																			break

													x += 1

							return self.prefix[:x], self.prefix[x:], word[x:]


	def  A(    self				,     lowercase__):

							for word in words:
													self.insert(__UpperCAmelCase)


	def  A(    self				,     lowercase__):

							if self.prefix == word:
													__UpperCAmelCase			:							str	    =	True

							# Case 2: The node has no edges that have a prefix to the word
							# Solution: We create an edge from the current node to a new one
							# containing the word
							elif word[0] not in self.nodes:
													__UpperCAmelCase			:							Optional[Any]	    =	RadixNode(prefix=__UpperCAmelCase				,     is_leaf=__UpperCAmelCase)

							else:
													__UpperCAmelCase			:							Dict	    =	self.nodes[word[0]]
													__UpperCAmelCase			:							Optional[int]	    =	incoming_node.match(
													    __UpperCAmelCase)

													# Case 3: The node prefix is equal to the matching
													# Solution: We insert remaining word on the next node
													if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(__UpperCAmelCase)

													# Case 4: The word is greater equal to the matching
													# Solution: Create a node in between both nodes, change
													# prefixes and add the new node for the remaining word
													else:
																			__UpperCAmelCase			:							List[Any]	    =	remaining_prefix

																			__UpperCAmelCase			:							int	    =	self.nodes[matching_string[0]]
																			__UpperCAmelCase			:							Tuple	    =	RadixNode(__UpperCAmelCase				,     __UpperCAmelCase)
																			__UpperCAmelCase			:							str	    =	aux_node

																			if remaining_word == "":
																									__UpperCAmelCase			:							Any	    =	True
																			else:
																									self.nodes[matching_string[0]].insert(__UpperCAmelCase)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							int	    =	self.nodes.get(word[0]				,     __UpperCAmelCase)
							if not incoming_node:
													return False
							else:
													__UpperCAmelCase			:							int	    =	incoming_node.match(
													    __UpperCAmelCase)
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# This applies when the word and the prefix are equal
													elif remaining_word == "":
																			return incoming_node.is_leaf
													# We have word remaining so we check the next node
													else:
																			return incoming_node.find(__UpperCAmelCase)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							int	    =	self.nodes.get(word[0]				,     __UpperCAmelCase)
							if not incoming_node:
													return False
							else:
													__UpperCAmelCase			:							Tuple	    =	incoming_node.match(
													    __UpperCAmelCase)
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# We have word remaining so we check the next node
													elif remaining_word != "":
																			return incoming_node.delete(__UpperCAmelCase)
													else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																									return False
																			else:
																									# We delete the nodes if no edges go from it
																									if len(incoming_node.nodes) == 0:
																															del self.nodes[word[0]]
																															# We merge the current node with its only child
																															if len(self.nodes) == 1 and not self.is_leaf:
																																					__UpperCAmelCase			:							str	    =	list(self.nodes.values())[0]
																																					__UpperCAmelCase			:							str	    =	merging_node.is_leaf
																																					self.prefix += merging_node.prefix
																																					__UpperCAmelCase			:							Optional[Any]	    =	merging_node.nodes
																																					 # If there is more than 1 edge, we just mark it as non-leaf
																									elif len(incoming_node.nodes) > 1:
																															__UpperCAmelCase			:							Any	    =	False
																									# If there is 1 edge, we merge it with its child
																									else:
																															__UpperCAmelCase			:							List[str]	    =	list(incoming_node.nodes.values())[0]
																															__UpperCAmelCase			:							List[str]	    =	merging_node.is_leaf
																															incoming_node.prefix += merging_node.prefix
																															__UpperCAmelCase			:							Dict	    =	merging_node.nodes

																									return True


	def  A(    self				,     lowercase__ = 0):

							if self.prefix != "":
													print('''-''' * height				,     self.prefix				,     '''  (leaf)''' if self.is_leaf else '''''')

							for value in self.nodes.values():
													value.print_tree(height + 1)






def     __SCREAMING_SNAKE_CASE							(       )		->     bool:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	'banana bananas bandana band apple all beast'.split()
						__UpperCAmelCase			:							List[Any]	    =	RadixNode()
						root.insert_many(_SCREAMING_SNAKE_CASE      )

						assert all(root.find(_SCREAMING_SNAKE_CASE      ) for word in words      )
						assert not root.find('''bandanas'''      )
						assert not root.find('''apps'''      )
						root.delete('''all'''      )
						assert not root.find('''all'''      )
						root.delete('''banana'''      )
						assert not root.find('''banana'''      )
						assert root.find('''bananas'''      )

						return True




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						assert test_trie()




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	RadixNode()
						__UpperCAmelCase			:							Any	    =	'banana bananas bandanas bandana band apple all beast'.split()
						root.insert_many(_SCREAMING_SNAKE_CASE      )

						print('''Words:'''      ,       _SCREAMING_SNAKE_CASE      )
						print('''Tree:'''      )
						root.print_tree()


if __name__ == "__main__":
				main()








"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __magic_name__	:List[str]						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # prior components
             'prior_tokenizer': prior_tokenizer,
             'prior_text_encoder': prior_text_encoder,
             'prior': prior,
             'prior_scheduler': prior_scheduler,
             # image noising components
             'image_normalizer': image_normalizer,
             'image_noising_scheduler': image_noising_scheduler,
             # regular denoising components
             'tokenizer': tokenizer,
             'text_encoder': text_encoder,
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'prior_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )

         lowerCAmelCase__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # stable unclip will oom when integration tests are run on a V100,
         # so turn on memory savings
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
