




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__,UpperCamelCase__,unittest.TestCase	):
     _a											=		StableDiffusionInstructPixaPixPipeline
     _a											=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     _a											=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _a											=		IMAGE_TO_IMAGE_IMAGE_PARAMS
     _a											=		IMAGE_TO_IMAGE_IMAGE_PARAMS

     def       a__			(						self       )		->							List[Any]:
           torch.manual_seed(0       )
           _A							:  Optional[int]    =		UNetaDConditionModel(
               block_out_channels=(32, 64)	,  layers_per_block=2	,  sample_size=32	,  in_channels=8	,  out_channels=4	,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,  cross_attention_dim=32	,  )
           _A							:  Union[str, Any]    =		PNDMScheduler(skip_prk_steps=_a       )
           torch.manual_seed(0       )
           _A							:  Optional[int]    =		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	,  )
           torch.manual_seed(0       )
           _A							:  Any    =		CLIPTextConfig(
               bos_token_id=0	,  eos_token_id=2	,  hidden_size=32	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1000	,  )
           _A							:  Union[str, Any]    =		CLIPTextModel(_a       )
           _A							:  Dict    =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           _A							:  List[str]    =		{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }
           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							List[str]:
           _A							:  Optional[int]    =		floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Union[str, Any]    =		image.cpu().permute(0	,  2	,  3	,  1       )[0]
           _A							:  Tuple    =		Image.fromarray(np.uinta(_a       )       ).convert("""RGB"""       )
           if str(_a       ).startswith("""mps"""       ):
                 _A							:  str    =		torch.manual_seed(_a       )
           else:
                 _A							:  Optional[Any]    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Union[str, Any]    =		{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
               """image_guidance_scale""": 1,
               """output_type""": """numpy""",
           }
           return inputs

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:  Tuple    =		self.get_dummy_components()
           _A							:  Dict    =		StableDiffusionInstructPixaPixPipeline(**_a       )
           _A							:  Optional[Any]    =		sd_pipe.to(_a       )
           sd_pipe.set_progress_bar_config(disable=_a       )

           _A							:  Tuple    =		self.get_dummy_inputs(_a       )
           _A							:  Tuple    =		sd_pipe(**_a       ).images
           _A							:  Any    =		image[0, -3:, -3:, -1]
           assert image.shape == (1, 32, 32, 3)
           _A							:  List[str]    =		np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3

     def       a__			(						self       )		->							List[str]:
           _A							:  Tuple    =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:  Optional[Any]    =		self.get_dummy_components()
           _A							:  Optional[Any]    =		StableDiffusionInstructPixaPixPipeline(**_a       )
           _A							:  Union[str, Any]    =		sd_pipe.to(_a       )
           sd_pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[str]    =		self.get_dummy_inputs(_a       )
           _A							:  Optional[int]    =		"""french fries"""
           _A							:  Optional[int]    =		sd_pipe(**_a	,  negative_prompt=_a       )
           _A							:  Optional[Any]    =		output.images
           _A							:  str    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _A							:  str    =		np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3

     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:  int    =		self.get_dummy_components()
           _A							:  Any    =		StableDiffusionInstructPixaPixPipeline(**_a       )
           _A							:  Optional[Any]    =		sd_pipe.to(_a       )
           sd_pipe.set_progress_bar_config(disable=_a       )

           _A							:  int    =		self.get_dummy_inputs(_a       )
           _A							:  Dict    =		[inputs["""prompt"""]] * 2

           _A							:  Optional[int]    =		np.array(inputs["""image"""]       ).astype(np.floataa       ) / 255.0
           _A							:  Any    =		torch.from_numpy(_a       ).unsqueeze(0       ).to(_a       )
           _A							:  Tuple    =		image / 2 + 0.5
           _A							:  int    =		image.permute(0	,  3	,  1	,  2       )
           _A							:  Optional[Any]    =		image.repeat(2	,  1	,  1	,  1       )

           _A							:  Optional[Any]    =		sd_pipe(**_a       ).images
           _A							:  Tuple    =		image[-1, -3:, -3:, -1]

           assert image.shape == (2, 32, 32, 3)
           _A							:  Optional[int]    =		np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:  Optional[Any]    =		self.get_dummy_components()
           _A							:  Union[str, Any]    =		EulerAncestralDiscreteScheduler(
               beta_start=0.00085	,  beta_end=0.012	,  beta_schedule="""scaled_linear"""       )
           _A							:  Optional[int]    =		StableDiffusionInstructPixaPixPipeline(**_a       )
           _A							:  Tuple    =		sd_pipe.to(_a       )
           sd_pipe.set_progress_bar_config(disable=_a       )

           _A							:  Tuple    =		self.get_dummy_inputs(_a       )
           _A							:  List[str]    =		sd_pipe(**_a       ).images
           _A							:  Dict    =		image[0, -3:, -3:, -1]

           _A							:  Any    =		[round(_a	,  4       ) for x in image_slice.flatten().tolist()]
           print(""",""".join([str(_a       ) for x in slice]       )       )

           assert image.shape == (1, 32, 32, 3)
           _A							:  Dict    =		np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3

     def       a__			(						self       )		->							Optional[int]:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



     def       a__			(						self       )		->							int:
           _A							:  int    =		self.get_dummy_components()
           _A							:  List[str]    =		StableDiffusionInstructPixaPixPipeline(**_a       )
           _A							:  Optional[int]    =		VaeImageProcessor(do_resize=_a	,  do_normalize=_a       )
           _A							:  Optional[Any]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  str    =		pipe(**self.get_dummy_inputs_by_type(_a	,  input_image_type="""pt"""       )       )[0]

           _A							:  Any    =		components["""vae"""]
           _A							:  Any    =		self.get_dummy_inputs_by_type(_a	,  input_image_type="""pt"""       )

           for image_param in self.image_latents_params:
                 if image_param in inputs.keys():
                       _A							:  List[str]    =		vae.encode(inputs[image_param]       ).latent_dist.mode()

           _A							:  Dict    =		pipe(**_a       )[0]

           _A							:  Any    =		np.abs(out - out_latents_inputs       ).max()
           self.assertLess(_a	,  1e-4	,  """passing latents as image input generate different result from passing image"""       )







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

     def       a__			(						self       )		->							str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def       a__			(						self	,  _a=0       )		->							List[str]:
           _A							:  Optional[int]    =		torch.manual_seed(_a       )
           _A							:  Dict    =		load_image(
               """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""       )
           _A							:  int    =		{
               """prompt""": """turn him into a cyborg""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """image_guidance_scale""": 1.0,
               """output_type""": """numpy""",
           }
           return inputs

     def       a__			(						self       )		->							Tuple:
           _A							:  List[Any]    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               """timbrooks/instruct-pix2pix"""	,  safety_checker=_a       )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing()

           _A							:  int    =		self.get_inputs()
           _A							:  Any    =		pipe(**_a       ).images
           _A							:  List[str]    =		image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           _A							:  str    =		np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]       )

           assert np.abs(expected_slice - image_slice       ).max() < 1e-3

     def       a__			(						self       )		->							List[Any]:
           _A							:  Optional[Any]    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               """timbrooks/instruct-pix2pix"""	,  safety_checker=_a       )
           _A							:  List[Any]    =		LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing()

           _A							:  Union[str, Any]    =		self.get_inputs()
           _A							:  int    =		pipe(**_a       ).images
           _A							:  str    =		image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           _A							:  int    =		np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

           assert np.abs(expected_slice - image_slice       ).max() < 1e-3

     def       a__			(						self       )		->							Dict:
           _A							:  Any    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               """timbrooks/instruct-pix2pix"""	,  safety_checker=_a       )
           _A							:  Tuple    =		DDIMScheduler.from_config(pipe.scheduler.config       )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing()

           _A							:  Optional[Any]    =		self.get_inputs()
           _A							:  Tuple    =		pipe(**_a       ).images
           _A							:  Any    =		image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           _A							:  Any    =		np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

           assert np.abs(expected_slice - image_slice       ).max() < 1e-3

     def       a__			(						self       )		->							str:
           _A							:  str    =		0

           def callback_fn(_a	,  _a	,  _a       ) -> None:
                 _A							:  List[Any]    =		True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 1:
                       _A							:  List[Any]    =		latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 64)
                       _A							:  Dict    =		latents[0, -3:, -3:, -1]
                       _A							:  Union[str, Any]    =		np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]       )

                       assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2
                 elif step == 2:
                       _A							:  List[Any]    =		latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 64)
                       _A							:  Any    =		latents[0, -3:, -3:, -1]
                       _A							:  Optional[int]    =		np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]       )

                       assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2

           _A							:  int    =		False

           _A							:  Any    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               """timbrooks/instruct-pix2pix"""	,  safety_checker=_a	,  torch_dtype=torch.floataa       )
           _A							:  Optional[Any]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing()

           _A							:  List[str]    =		self.get_inputs()
           pipe(**_a	,  callback=_a	,  callback_steps=1       )
           assert callback_fn.has_been_called
           assert number_of_steps == 3

     def       a__			(						self       )		->							int:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           _A							:  List[str]    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               """timbrooks/instruct-pix2pix"""	,  safety_checker=_a	,  torch_dtype=torch.floataa       )
           _A							:  str    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing(1       )
           pipe.enable_sequential_cpu_offload()

           _A							:  Any    =		self.get_inputs()
           _A							:  Tuple    =		pipe(**_a       )

           _A							:  Any    =		torch.cuda.max_memory_allocated()
           # make sure that less than 2.2 GB is allocated
           assert mem_bytes < 2.2 * 10**9



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Tuple    =		self.get_inputs()
           # resize to resolution that is divisible by 8 but not 16 or 32
           _A							:  str    =		inputs["""image"""].resize((504, 504)       )

           _A							:  List[Any]    =		"""timbrooks/instruct-pix2pix"""
           _A							:  List[str]    =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
               _a	,  safety_checker=_a	,  )
           pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )
           pipe.enable_attention_slicing()

           _A							:  str    =		pipe(**_a       )
           _A							:  str    =		output.images[0]

           _A							:  Any    =		image[255:258, 383:386, -1]

           assert image.shape == (504, 504, 3)
           _A							:  List[str]    =		np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-3







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [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(snake_case_  )] += new_combinations

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

      return table[len(snake_case_  )]


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"],
      )
  )

