





'''simple docstring'''




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : List[str]           =							StableDiffusionDiffEditPipeline
 _lowerCamelCase  : Any           =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 _lowerCamelCase  : str           =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 _lowerCamelCase  : List[Any]           =							frozenset(
     []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 _lowerCamelCase  : Optional[Any]           =							frozenset([]			)




 def      lowercase	(					self       :    Optional[int]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)	,			layers_per_block=2	,			sample_size=3_2	,			in_channels=4	,			out_channels=4	,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,			cross_attention_dim=3_2	,			attention_head_dim=(2, 4)	,			use_linear_projection=snake_case_	,			)
      _UpperCAmelCase     							=   DDIMScheduler(
          beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule="scaled_linear"	,			clip_sample=snake_case_	,			set_alpha_to_one=snake_case_	,			)
      _UpperCAmelCase     							=   DDIMInverseScheduler(
          beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule="scaled_linear"	,			clip_sample=snake_case_	,			set_alpha_to_zero=snake_case_	,			)
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   AutoencoderKL(
          block_out_channels=[3_2, 6_4]	,			in_channels=3	,			out_channels=3	,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,			latent_channels=4	,			sample_size=1_2_8	,			)
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   CLIPTextConfig(
          bos_token_id=0	,			eos_token_id=2	,			hidden_size=3_2	,			intermediate_size=3_7	,			layer_norm_eps=1e-05	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1_0_0_0	,			hidden_act="gelu"	,			projection_dim=5_1_2	,			)
      _UpperCAmelCase     							=   CLIPTextModel(snake_case_    )
      _UpperCAmelCase     							=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

      _UpperCAmelCase     							=   {
          "unet": unet,
          "scheduler": scheduler,
          "inverse_scheduler": inverse_scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }

      return components




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    str	,			snake_case_       :    List[Any]=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 1_6, 1_6)	,			rng=random.Random(snake_case_    )    ).to(snake_case_    )
      _UpperCAmelCase     							=   floats_tensor((1, 2, 4, 1_6, 1_6)	,			rng=random.Random(snake_case_    )    ).to(snake_case_    )
      if str(snake_case_    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
      _UpperCAmelCase     							=   {
          "prompt": "a dog and a newt",
          "mask_image": mask,
          "image_latents": latents,
          "generator": generator,
          "num_inference_steps": 2,
          "inpaint_strength": 1.0,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }

      return inputs




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 3, 3_2, 3_2)	,			rng=random.Random(snake_case_    )    ).to(snake_case_    )
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    )[0]
      _UpperCAmelCase     							=   Image.fromarray(np.uinta(snake_case_    )    ).convert("RGB"    )
      if str(snake_case_    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
      _UpperCAmelCase     							=   {
          "image": image,
          "source_prompt": "a cat and a frog",
          "target_prompt": "a dog and a newt",
          "generator": generator,
          "num_inference_steps": 2,
          "num_maps_per_mask": 2,
          "mask_encode_strength": 1.0,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }

      return inputs




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    List[str]	,			snake_case_       :    Any=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 3, 3_2, 3_2)	,			rng=random.Random(snake_case_    )    ).to(snake_case_    )
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    )[0]
      _UpperCAmelCase     							=   Image.fromarray(np.uinta(snake_case_    )    ).convert("RGB"    )
      if str(snake_case_    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
      _UpperCAmelCase     							=   {
          "image": image,
          "prompt": "a cat and a frog",
          "generator": generator,
          "num_inference_steps": 2,
          "inpaint_strength": 1.0,
          "guidance_scale": 6.0,
          "decode_latents": True,
          "output_type": "numpy",
      }
      return inputs




 def      lowercase	(					self       :    Tuple    ):
      if not hasattr(self.pipeline_class	,			"_optional_components"    ):
           return

      _UpperCAmelCase     							=   self.get_dummy_components()
      _UpperCAmelCase     							=   self.pipeline_class(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      # set all optional components to None and update pipeline config accordingly
      for optional_component in pipe._optional_components:
           setattr(snake_case_	,			snake_case_	,			snake_case_    )
      pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

      _UpperCAmelCase     							=   self.get_dummy_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe(**snake_case_    )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(snake_case_    )
           _UpperCAmelCase     							=   self.pipeline_class.from_pretrained(snake_case_    )
           pipe_loaded.to(snake_case_    )
           pipe_loaded.set_progress_bar_config(disable=snake_case_    )

      for optional_component in pipe._optional_components:
           self.assertTrue(
               getattr(snake_case_	,			snake_case_    ) is None	,			f'`{optional_component}` did not stay set to None after loading.'	,			)

      _UpperCAmelCase     							=   self.get_dummy_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe_loaded(**snake_case_    )[0]

      _UpperCAmelCase     							=   np.abs(output - output_loaded    ).max()
      self.assertLess(snake_case_	,			1e-4    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   "cpu"

      _UpperCAmelCase     							=   self.get_dummy_components()
      _UpperCAmelCase     							=   self.pipeline_class(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_dummy_mask_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe.generate_mask(**snake_case_    )
      _UpperCAmelCase     							=   mask[0, -3:, -3:]

      self.assertEqual(mask.shape	,			(1, 1_6, 1_6)    )
      _UpperCAmelCase     							=   np.array([0] * 9    )
      _UpperCAmelCase     							=   np.abs(mask_slice.flatten() - expected_slice    ).max()
      self.assertLessEqual(snake_case_	,			1e-3    )
      self.assertEqual(mask[0, -3, -4]	,			0    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   "cpu"

      _UpperCAmelCase     							=   self.get_dummy_components()
      _UpperCAmelCase     							=   self.pipeline_class(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_dummy_inversion_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe.invert(**snake_case_    ).images
      _UpperCAmelCase     							=   image[0, -1, -3:, -3:]

      self.assertEqual(image.shape	,			(2, 3_2, 3_2, 3)    )
      _UpperCAmelCase     							=   np.array(
          [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,			)
      _UpperCAmelCase     							=   np.abs(image_slice.flatten() - expected_slice    ).max()
      self.assertLessEqual(snake_case_	,			1e-3    )




 def      lowercase	(					self       :    List[Any]    ):
      super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   "cpu"

      _UpperCAmelCase     							=   self.get_dummy_components()

      _UpperCAmelCase     							=   {"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
      _UpperCAmelCase     							=   DPMSolverMultistepScheduler(**snake_case_    )
      _UpperCAmelCase     							=   DPMSolverMultistepInverseScheduler(**snake_case_    )

      _UpperCAmelCase     							=   self.pipeline_class(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_dummy_inversion_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe.invert(**snake_case_    ).images
      _UpperCAmelCase     							=   image[0, -1, -3:, -3:]

      self.assertEqual(image.shape	,			(2, 3_2, 3_2, 3)    )
      _UpperCAmelCase     							=   np.array(
          [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]	,			)
      _UpperCAmelCase     							=   np.abs(image_slice.flatten() - expected_slice    ).max()
      self.assertLessEqual(snake_case_	,			1e-3    )







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




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




 @classmethod
 def      lowercase	(					cls       :    Dict    ):
      _UpperCAmelCase     							=   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"    )

      _UpperCAmelCase     							=   raw_image.convert("RGB"    ).resize((7_6_8, 7_6_8)    )

      _UpperCAmelCase     							=   raw_image




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   torch.manual_seed(0    )

      _UpperCAmelCase     							=   StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"	,			safety_checker=snake_case_	,			torch_dtype=torch.floataa    )
      _UpperCAmelCase     							=   DDIMScheduler.from_config(pipe.scheduler.config    )
      _UpperCAmelCase     							=   DDIMInverseScheduler.from_config(pipe.scheduler.config    )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   "a bowl of fruit"
      _UpperCAmelCase     							=   "a bowl of pears"

      _UpperCAmelCase     							=   pipe.generate_mask(
          image=self.raw_image	,			source_prompt=snake_case_	,			target_prompt=snake_case_	,			generator=snake_case_	,			)

      _UpperCAmelCase     							=   pipe.invert(
          prompt=snake_case_	,			image=self.raw_image	,			inpaint_strength=0.7	,			generator=snake_case_    ).latents

      _UpperCAmelCase     							=   pipe(
          prompt=snake_case_	,			mask_image=snake_case_	,			image_latents=snake_case_	,			generator=snake_case_	,			negative_prompt=snake_case_	,			inpaint_strength=0.7	,			output_type="numpy"	,			).images[0]

      _UpperCAmelCase     							=   (
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"    ).resize((7_6_8, 7_6_8)    )    )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()    ) < 5e-1




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   torch.manual_seed(0    )

      _UpperCAmelCase     							=   StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"	,			safety_checker=snake_case_	,			torch_dtype=torch.floataa    )
      _UpperCAmelCase     							=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
      _UpperCAmelCase     							=   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   "a bowl of fruit"
      _UpperCAmelCase     							=   "a bowl of pears"

      _UpperCAmelCase     							=   pipe.generate_mask(
          image=self.raw_image	,			source_prompt=snake_case_	,			target_prompt=snake_case_	,			generator=snake_case_	,			)

      _UpperCAmelCase     							=   pipe.invert(
          prompt=snake_case_	,			image=self.raw_image	,			inpaint_strength=0.7	,			generator=snake_case_	,			num_inference_steps=2_5	,			).latents

      _UpperCAmelCase     							=   pipe(
          prompt=snake_case_	,			mask_image=snake_case_	,			image_latents=snake_case_	,			generator=snake_case_	,			negative_prompt=snake_case_	,			inpaint_strength=0.7	,			num_inference_steps=2_5	,			output_type="numpy"	,			).images[0]

      _UpperCAmelCase     							=   (
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"    ).resize((7_6_8, 7_6_8)    )    )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()    ) < 5e-1











'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     return " ".join(
         "".join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words('''Hey wollef sroirraw'''))




