



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        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     str								=     KandinskyVaaPriorPipeline
				A_   :     Optional[int]								=     ['prompt']
				A_   :     Optional[Any]								=     ['prompt', 'negative_prompt']
				A_   :     List[Any]								=     [
				    'num_images_per_prompt',
				    'generator',
				    'num_inference_steps',
				    'latents',
				    'negative_prompt',
				    'guidance_scale',
				    'output_type',
				    'return_dict',
				]
				A_   :     int								=     False


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


				@property
				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											return 32


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Union[str, Any]:
											return self.time_input_dim


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											return self.time_input_dim * 4


				@property
				def __UpperCamelCase						(							self      :  Dict       )		->     Optional[Any]:
											return 100


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
											return tokenizer


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     List[Any]:
											torch.manual_seed(0       )
											A      						=    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=1_000						, )
											return CLIPTextModelWithProjection(__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Tuple:
											torch.manual_seed(0       )

											A      						=    {
											    'num_attention_heads': 2,
											    'attention_head_dim': 12,
											    'embedding_dim': self.text_embedder_hidden_size,
											    'num_layers': 1,
											}

											A      						=    PriorTransformer(**__UpperCamelCase       )
											# 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
											A      						=    nn.Parameter(torch.ones(model.clip_std.shape       )       )
											return model


				@property
				def __UpperCamelCase						(							self      :  Tuple       )		->     int:
											torch.manual_seed(0       )
											A      						=    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						, )

											A      						=    CLIPVisionModelWithProjection(__UpperCamelCase       )
											return model


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A      						=    CLIPImageProcessor(
											    crop_size=224						, do_center_crop=__UpperCamelCase						, do_normalize=__UpperCamelCase						, do_resize=__UpperCamelCase						, 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 __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											A      						=    self.dummy_prior
											A      						=    self.dummy_image_encoder
											A      						=    self.dummy_text_encoder
											A      						=    self.dummy_tokenizer
											A      						=    self.dummy_image_processor

											A      						=    UnCLIPScheduler(
											    variance_type='fixed_small_log'						, prediction_type='sample'						, num_train_timesteps=1_000						, clip_sample=__UpperCamelCase						, clip_sample_range=1_0.0						, )

											A      						=    {
											    'prior': prior,
											    'image_encoder': image_encoder,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'scheduler': scheduler,
											    'image_processor': image_processor,
											}

											return components


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Tuple=0       )		->     Optional[int]:
											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    'prompt': 'horse',
											    'generator': generator,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    'cpu'

											A      						=    self.get_dummy_components()

											A      						=    self.pipeline_class(**__UpperCamelCase       )
											A      						=    pipe.to(__UpperCamelCase       )

											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    pipe(**self.get_dummy_inputs(__UpperCamelCase       )       )
											A      						=    output.image_embeds

											A      						=    pipe(
											    **self.get_dummy_inputs(__UpperCamelCase       )						, return_dict=__UpperCamelCase						, )[0]

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

											assert image.shape == (1, 32)

											A      						=    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 __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											A      						=    torch_device == 'cpu'
											A      						=    True
											A      						=    False

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


				@skip_mps
				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											A      						=    torch_device == 'cpu'
											A      						=    False

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



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()

