




"""simple docstring"""



import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				IFPipeline
    lowercase__						 =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
    lowercase__						 =				TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase__						 =				PipelineTesterMixin.required_optional_params - {'''latents'''}




    def 							__magic_name__							(   self						:	Any				)				->							Optional[Any]:




     '''simple docstring'''

     return self._get_dummy_components()




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict=0				)				->							List[str]:




     '''simple docstring'''

     if str(snake_case_				).startswith("mps"				):
      A__	=    torch.manual_seed(snake_case_				)
     else:
      A__	=    torch.Generator(device=snake_case_				).manual_seed(snake_case_				)

     A__	=    {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "numpy",
     }

     return inputs




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     self._test_save_load_optional_components()




    @unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

     super().test_save_load_floataa(expected_max_diff=1e-1				)




    def 							__magic_name__							(   self						:	Any				)				->							List[Any]:




     '''simple docstring'''

     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)




    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     self._test_save_load_local()




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2     ,			)




    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
    def 							__magic_name__							(   self						:	Tuple				)				->							Union[str, Any]:




     '''simple docstring'''

     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)






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




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




     '''simple docstring'''

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




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




     '''simple docstring'''

     A__	=    IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"     ,			variant="fp16"     ,			torch_dtype=torch.floataa				)

     A__	=    IFSuperResolutionPipeline.from_pretrained(
         "DeepFloyd/IF-II-L-v1.0"     ,			variant="fp16"     ,			torch_dtype=torch.floataa     ,			text_encoder=snake_case_     ,			tokenizer=snake_case_				)

     # pre compute text embeddings and remove T5 to save memory

     pipe_a.text_encoder.to("cuda"				)

     A__,				A__	=    pipe_a.encode_prompt("anime turtle"     ,			device="cuda"				)

     del pipe_a.tokenizer
     del pipe_a.text_encoder
     gc.collect()

     A__	=    None
     A__	=    None

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

     self._test_if(snake_case_     ,			snake_case_     ,			snake_case_     ,			snake_case_				)

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # img2img

     A__	=    IFImgaImgPipeline(**pipe_a.components				)
     A__	=    IFImgaImgSuperResolutionPipeline(**pipe_a.components				)

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

     self._test_if_imgaimg(snake_case_     ,			snake_case_     ,			snake_case_     ,			snake_case_				)

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # inpainting

     A__	=    IFInpaintingPipeline(**pipe_a.components				)
     A__	=    IFInpaintingSuperResolutionPipeline(**pipe_a.components				)

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

     self._test_if_inpainting(snake_case_     ,			snake_case_     ,			snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	int     ,			snake_case_						:	Any     ,			snake_case_						:	str     ,			snake_case_						:	Union[str, Any]				)				->							int:




     '''simple docstring'''

     _start_torch_memory_measurement()

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)
     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			num_inference_steps=2     ,			generator=snake_case_     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (64, 64, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 13 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)

     # pipeline 2

     _start_torch_memory_measurement()

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)

     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(0				)				).to(snake_case_				)

     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			image=snake_case_     ,			generator=snake_case_     ,			num_inference_steps=2     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (256, 256, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     _start_torch_memory_measurement()

     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(0				)				).to(snake_case_				)

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)

     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			image=snake_case_     ,			num_inference_steps=2     ,			generator=snake_case_     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (64, 64, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 10 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)

     # pipeline 2

     _start_torch_memory_measurement()

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)

     A__	=    floats_tensor((1, 3, 256, 256)     ,			rng=random.Random(0				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(0				)				).to(snake_case_				)

     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			image=snake_case_     ,			original_image=snake_case_     ,			generator=snake_case_     ,			num_inference_steps=2     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (256, 256, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[str]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     _start_torch_memory_measurement()

     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(0				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(1				)				).to(snake_case_				)

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)
     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			image=snake_case_     ,			mask_image=snake_case_     ,			num_inference_steps=2     ,			generator=snake_case_     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (64, 64, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 10 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)

     # pipeline 2

     _start_torch_memory_measurement()

     A__	=    torch.Generator(device="cpu"				).manual_seed(0				)

     A__	=    floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(0				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 256, 256)     ,			rng=random.Random(0				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 256, 256)     ,			rng=random.Random(1				)				).to(snake_case_				)

     A__	=    pipe_a(
         prompt_embeds=snake_case_     ,			negative_prompt_embeds=snake_case_     ,			image=snake_case_     ,			mask_image=snake_case_     ,			original_image=snake_case_     ,			generator=snake_case_     ,			num_inference_steps=2     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (256, 256, 3)

     A__	=    torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"				)
     assert_mean_pixel_difference(snake_case_     ,			snake_case_				)







def   _SCREAMING_SNAKE_CASE       (							)						->		str:
 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()











"""simple docstring"""



from __future__ import annotations

SCREAMING_SNAKE_CASE    =	"#"




class      UpperCAmelCase_  :




    def __init__(   self						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    {}




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

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




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




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	dict				)				->							tuple:




     '''simple docstring'''

     A__	=    []
     for c, v in d.items():
      A__	=    [" "] if c == END else [(c + s) for s in self._elements(snake_case_				)]
      result.extend(snake_case_				)
     return tuple(snake_case_				)






SCREAMING_SNAKE_CASE    =	Trie()
SCREAMING_SNAKE_CASE    =	("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
      trie.insert_word(word)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		tuple:
 A__	=    trie.find_word(lowercase_						)
 return tuple(string + word for word in suffixes						)







def   _SCREAMING_SNAKE_CASE       (							)						->		None:
 print(autocomplete_using_trie("de"						)						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





