



'''simple docstring'''

import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class __snake_case( lowercase__						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Optional[int] 	=						KandinskyVaaPriorPipeline
						UpperCAmelCase				:       Optional[Any] 	=						["prompt"]
						UpperCAmelCase				:       str 	=						["prompt", "negative_prompt"]
						UpperCAmelCase				:       List[Any] 	=						[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase				:       Tuple 	=						False


						@property
						def 							__snake_case       (   self )      ->      Optional[int]:
							return 32


						@property
						def 							__snake_case       (   self )      ->      Tuple:
							return 32


						@property
						def 							__snake_case       (   self )      ->      int:
							return self.time_input_dim


						@property
						def 							__snake_case       (   self )      ->      str:
							return self.time_input_dim * 4


						@property
						def 							__snake_case       (   self )      ->      Optional[int]:
							return 100


						@property
						def 							__snake_case       (   self )      ->      Union[str, Any]:
							lowerCAmelCase       =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
							return tokenizer


						@property
						def 							__snake_case       (   self )      ->      Union[str, Any]:
							torch.manual_seed(0 )
							lowerCAmelCase       =      CLIPTextConfig(
							    bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=self.text_embedder_hidden_size      ,			projection_dim=self.text_embedder_hidden_size      ,			intermediate_size=37      ,			layer_norm_eps=1e-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1000      ,			)
							return CLIPTextModelWithProjection(lowercase_ )


						@property
						def 							__snake_case       (   self )      ->      Tuple:
							torch.manual_seed(0 )

							lowerCAmelCase       =      {
							    """num_attention_heads""": 2,
							    """attention_head_dim""": 12,
							    """embedding_dim""": self.text_embedder_hidden_size,
							    """num_layers""": 1,
							}

							lowerCAmelCase       =      PriorTransformer(**lowercase_ )
							# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
							lowerCAmelCase       =      nn.Parameter(torch.ones(model.clip_std.shape ) )
							return model


						@property
						def 							__snake_case       (   self )      ->      Any:
							torch.manual_seed(0 )
							lowerCAmelCase       =      CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size      ,			image_size=224      ,			projection_dim=self.text_embedder_hidden_size      ,			intermediate_size=37      ,			num_attention_heads=4      ,			num_channels=3      ,			num_hidden_layers=5      ,			patch_size=14      ,			)

							lowerCAmelCase       =      CLIPVisionModelWithProjection(lowercase_ )
							return model


						@property
						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      CLIPImageProcessor(
							    crop_size=224      ,			do_center_crop=lowercase_      ,			do_normalize=lowercase_      ,			do_resize=lowercase_      ,			image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,			image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,			resample=3      ,			size=224      ,			)

							return image_processor


						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      self.dummy_prior
							lowerCAmelCase       =      self.dummy_image_encoder
							lowerCAmelCase       =      self.dummy_text_encoder
							lowerCAmelCase       =      self.dummy_tokenizer
							lowerCAmelCase       =      self.dummy_image_processor

							lowerCAmelCase       =      UnCLIPScheduler(
							    variance_type="""fixed_small_log"""      ,			prediction_type="""sample"""      ,			num_train_timesteps=1000      ,			clip_sample=lowercase_      ,			clip_sample_range=1_0.0      ,			)

							lowerCAmelCase       =      {
							    """prior""": prior,
							    """image_encoder""": image_encoder,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """scheduler""": scheduler,
							    """image_processor""": image_processor,
							}

							return components


						def 							__snake_case       (   self      ,			A_      ,			A_=0 )      ->      Tuple:
							if str(lowercase_ ).startswith("""mps""" ):
								lowerCAmelCase       =      torch.manual_seed(lowercase_ )
							else:
								lowerCAmelCase       =      torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
							lowerCAmelCase       =      {
							    """prompt""": """horse""",
							    """generator""": generator,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs


						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      """cpu"""

							lowerCAmelCase       =      self.get_dummy_components()

							lowerCAmelCase       =      self.pipeline_class(**lowercase_ )
							lowerCAmelCase       =      pipe.to(lowercase_ )

							pipe.set_progress_bar_config(disable=lowercase_ )

							lowerCAmelCase       =      pipe(**self.get_dummy_inputs(lowercase_ ) )
							lowerCAmelCase       =      output.image_embeds

							lowerCAmelCase       =      pipe(
							    **self.get_dummy_inputs(lowercase_ )      ,			return_dict=lowercase_      ,			)[0]

							lowerCAmelCase       =      image[0, -10:]
							lowerCAmelCase       =      image_from_tuple[0, -10:]

							assert image.shape == (1, 32)

							lowerCAmelCase       =      np.array(
							    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2


						@skip_mps
						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      torch_device == """cpu"""
							lowerCAmelCase       =      True
							lowerCAmelCase       =      False

							self._test_inference_batch_single_identical(
							    test_max_difference=lowercase_      ,			relax_max_difference=lowercase_      ,			test_mean_pixel_difference=lowercase_      ,			)




						@skip_mps
						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      torch_device == """cpu"""
							lowerCAmelCase       =      False

							self._test_attention_slicing_forward_pass(
							    test_max_difference=lowercase_      ,			test_mean_pixel_difference=lowercase_      ,			)



from __future__ import annotations

_lowerCAmelCase:						str   				=       '#'




class       lowercase_					:





				def __init__(				self)  -> None:
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											a__      =self._trie
											for char in text:
																		if char not in trie:
																									a__      ={}
																		a__      =trie[char]
											a__      =True





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple | list:
											a__      =self._trie
											for char in prefix:
																		if char in trie:
																									a__      =trie[char]
																		else:
																									return []
											return self._elements(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple:
											a__      =[]
											for c, v in d.items():
																		a__      =[' '] if c == END else [(c + s) for s in self._elements(lowercase_)]
																		result.extend(lowercase_)
											return tuple(lowercase_)







_lowerCAmelCase:						Optional[int]   				=       Trie()
_lowerCAmelCase:						List[str]   				=       ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)






def   _lowercase(					__a				:						str     ):
							a__      =trie.find_word(__a     )
							return tuple(string + word for word in suffixes     )






def   _lowercase(					):
							print(autocomplete_using_trie('de'     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

