





from __future__ import annotations

SCREAMING_SNAKE_CASE_						       =	"""#"""

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Dict	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._trie
           for char in text:
                 if char not in trie:
                       SCREAMING_SNAKE_CASE		=						{}
                 SCREAMING_SNAKE_CASE		=						trie[char]
           SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   List[Any]	)					->		tuple | list:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._trie
           for char in prefix:
                 if char in trie:
                       SCREAMING_SNAKE_CASE		=						trie[char]
                 else:
                       return []
           return self._elements(UpperCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[Any]	)					->		tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for c, v in d.items():
                 SCREAMING_SNAKE_CASE		=						[""" """] if c == END else [(c + s) for s in self._elements(UpperCamelCase__	)]
                 result.extend(UpperCamelCase__	)
           return tuple(UpperCamelCase__	)
SCREAMING_SNAKE_CASE_						       =	Trie()
SCREAMING_SNAKE_CASE_						       =	("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
      trie.insert_word(word)



def        UpperCamelCase__       (				_SCREAMING_SNAKE_CASE     )			->       tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						trie.find_word(UpperCAmelCase__     )
      return tuple(string + word for word in suffixes     )



def        UpperCamelCase__       (				)			->       None:


      '''simple docstring'''


      print(autocomplete_using_trie("""de"""     )     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()









import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     str		     =      AltDiffusionPipeline
     __snake_case   :     int		     =      TEXT_TO_IMAGE_PARAMS
     __snake_case   :     Dict		     =      TEXT_TO_IMAGE_BATCH_PARAMS
     __snake_case   :     Tuple		     =      TEXT_TO_IMAGE_IMAGE_PARAMS
     __snake_case   :     Optional[int]		     =      TEXT_TO_IMAGE_IMAGE_PARAMS






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[Any]:




           '''simple docstring'''


           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						UNetaDConditionModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=4						,out_channels=4						,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,cross_attention_dim=32						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						AutoencoderKL(
               block_out_channels=[32, 64]						,in_channels=3						,out_channels=3						,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,latent_channels=4						,)

           # TODO: address the non-deterministic text encoder (fails for save-load tests)
           # torch.manual_seed(0)
           # text_encoder_config = RobertaSeriesConfig(
           #     hidden_size=32,
           #     project_dim=32,
           #     intermediate_size=37,
           #     layer_norm_eps=1e-05,
           #     num_attention_heads=4,
           #     num_hidden_layers=5,
           #     vocab_size=5002,
           # )
           # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,projection_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=5002						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""	)
           SCREAMING_SNAKE_CASE		=						77

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }
           return components






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   int=0	)					->		Any:




           '''simple docstring'''


           if str(lowerCamelCase__	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """A painting of a squirrel eating a burger""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
           }
           return inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		List[str]:




           '''simple docstring'''


           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Tuple:




           '''simple docstring'''


           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						RobertaSeriesConfig(
               hidden_size=32						,project_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5002						,)
           # TODO: remove after fixing the non-deterministic text encoder
           SCREAMING_SNAKE_CASE		=						RobertaSeriesModelWithTransformation(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						text_encoder

           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						"""A photo of an astronaut"""
           SCREAMING_SNAKE_CASE		=						alt_pipe(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						PNDMScheduler(skip_prk_steps=lowerCamelCase__	)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						RobertaSeriesConfig(
               hidden_size=32						,project_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5002						,)
           # TODO: remove after fixing the non-deterministic text encoder
           SCREAMING_SNAKE_CASE		=						RobertaSeriesModelWithTransformation(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						text_encoder
           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]	)

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

@slow
@require_torch_gpu
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,safety_checker=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""A painting of a squirrel eating a burger"""
           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						alt_pipe([prompt]						,generator=lowerCamelCase__						,guidance_scale=6.0						,num_inference_steps=20						,output_type="""np"""	)

           SCREAMING_SNAKE_CASE		=						output.images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""						,subfolder="""scheduler"""	)

           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,scheduler=lowerCamelCase__						,safety_checker=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""A painting of a squirrel eating a burger"""
           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						alt_pipe([prompt]						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""numpy"""	)
           SCREAMING_SNAKE_CASE		=						output.images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]	)

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


