




'''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     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Tuple            =      IFPipeline
       __a       :  Optional[Any]            =      TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
       __a       :  Union[str, Any]            =      TEXT_TO_IMAGE_BATCH_PARAMS
       __a       :  Any            =      PipelineTesterMixin.required_optional_params - {"""latents"""}
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return self._get_dummy_components()
       def 					A   (			self		:			int     ,       lowercase		:			Any     ,       lowercase		:			List[str]=0       ):



           '''simple docstring'''

           if str(lowercase       ).startswith('''mps'''       ):
               UpperCAmelCase     =	torch.manual_seed(lowercase       )
           else:
               UpperCAmelCase     =	torch.Generator(device=lowercase       ).manual_seed(lowercase       )

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

           return inputs
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           self._test_save_load_optional_components()
       @unittest.skipIf(torch_device != '''cuda'''     ,       reason='''float16 requires CUDA'''       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1E-1       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           self._test_save_load_local()
       def 					A   (			self		:			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 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )






@slow
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

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

           UpperCAmelCase     =	IFSuperResolutionPipeline.from_pretrained(
               '''DeepFloyd/IF-II-L-v1.0'''     ,       variant='''fp16'''     ,       torch_dtype=torch.floataa     ,       text_encoder=lowercase     ,       tokenizer=lowercase       )

           # pre compute text embeddings and remove T5 to save memory

           pipe_a.text_encoder.to('''cuda'''       )

           UpperCAmelCase ,  UpperCAmelCase     =	pipe_a.encode_prompt('''anime turtle'''     ,       device='''cuda'''       )

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

           UpperCAmelCase     =	None
           UpperCAmelCase     =	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(lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # img2img

           UpperCAmelCase     =	IFImgaImgPipeline(**pipe_a.components       )
           UpperCAmelCase     =	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(lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # inpainting

           UpperCAmelCase     =	IFInpaintingPipeline(**pipe_a.components       )
           UpperCAmelCase     =	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(lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

           _start_torch_memory_measurement()

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )
           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       num_inference_steps=2     ,       generator=lowercase     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

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

           # pipeline 2

           _start_torch_memory_measurement()

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )

           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(0       )       ).to(lowercase       )

           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       image=lowercase     ,       generator=lowercase     ,       num_inference_steps=2     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

           UpperCAmelCase     =	load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''       )
           assert_mean_pixel_difference(lowercase     ,       lowercase       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			Tuple     ,       lowercase		:			int       ):



           '''simple docstring'''

           _start_torch_memory_measurement()

           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(0       )       ).to(lowercase       )

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )

           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       image=lowercase     ,       num_inference_steps=2     ,       generator=lowercase     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

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

           # pipeline 2

           _start_torch_memory_measurement()

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )

           UpperCAmelCase     =	floats_tensor((1, 3, 256, 256)     ,       rng=random.Random(0       )       ).to(lowercase       )
           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(0       )       ).to(lowercase       )

           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       image=lowercase     ,       original_image=lowercase     ,       generator=lowercase     ,       num_inference_steps=2     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

           UpperCAmelCase     =	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(lowercase     ,       lowercase       )







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			List[str]     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           _start_torch_memory_measurement()

           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(0       )       ).to(lowercase       )
           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(1       )       ).to(lowercase       )

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )
           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       image=lowercase     ,       mask_image=lowercase     ,       num_inference_steps=2     ,       generator=lowercase     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

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

           # pipeline 2

           _start_torch_memory_measurement()

           UpperCAmelCase     =	torch.Generator(device='''cpu'''       ).manual_seed(0       )

           UpperCAmelCase     =	floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(0       )       ).to(lowercase       )
           UpperCAmelCase     =	floats_tensor((1, 3, 256, 256)     ,       rng=random.Random(0       )       ).to(lowercase       )
           UpperCAmelCase     =	floats_tensor((1, 3, 256, 256)     ,       rng=random.Random(1       )       ).to(lowercase       )

           UpperCAmelCase     =	pipe_a(
               prompt_embeds=lowercase     ,       negative_prompt_embeds=lowercase     ,       image=lowercase     ,       mask_image=lowercase     ,       original_image=lowercase     ,       generator=lowercase     ,       num_inference_steps=2     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

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

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

           UpperCAmelCase     =	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(lowercase     ,       lowercase       )






def 		snake_case_							():
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




