
"""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 __lowerCAmelCase		(      _snake_case			,  _snake_case			,  unittest.TestCase			):
     lowercase						   =						StableDiffusionDiffEditPipeline
     lowercase						   =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
     lowercase						   =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
     lowercase						   =						frozenset(
         []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowercase						   =						frozenset([]			)





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      torch.manual_seed(0      )
      __UpperCamelCase				  =       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	,     attention_head_dim=(2, 4)	,     use_linear_projection=_lowerCamelCase	,     )
      __UpperCamelCase				  =       DDIMScheduler(
          beta_start=0.0_0_0_8_5	,     beta_end=0.0_1_2	,     beta_schedule='scaled_linear'	,     clip_sample=_lowerCamelCase	,     set_alpha_to_one=_lowerCamelCase	,     )
      __UpperCamelCase				  =       DDIMInverseScheduler(
          beta_start=0.0_0_0_8_5	,     beta_end=0.0_1_2	,     beta_schedule='scaled_linear'	,     clip_sample=_lowerCamelCase	,     set_alpha_to_zero=_lowerCamelCase	,     )
      torch.manual_seed(0      )
      __UpperCamelCase				  =       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	,     sample_size=128	,     )
      torch.manual_seed(0      )
      __UpperCamelCase				  =       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	,     hidden_act='gelu'	,     projection_dim=512	,     )
      __UpperCamelCase				  =       CLIPTextModel(_lowerCamelCase      )
      __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 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase=0      ):

      '''simple docstring'''




      __UpperCamelCase				  =       floats_tensor((1, 16, 16)	,     rng=random.Random(_lowerCamelCase      )      ).to(_lowerCamelCase      )
      __UpperCamelCase				  =       floats_tensor((1, 2, 4, 16, 16)	,     rng=random.Random(_lowerCamelCase      )      ).to(_lowerCamelCase      )
      if str(_lowerCamelCase      ).startswith('mps'      ):
       __UpperCamelCase				  =       torch.manual_seed(_lowerCamelCase      )
      else:
       __UpperCamelCase				  =       torch.Generator(device=_lowerCamelCase      ).manual_seed(_lowerCamelCase      )
      __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 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase=0      ):

      '''simple docstring'''




      __UpperCamelCase				  =       floats_tensor((1, 3, 32, 32)	,     rng=random.Random(_lowerCamelCase      )      ).to(_lowerCamelCase      )
      __UpperCamelCase				  =       image.cpu().permute(0	,     2	,     3	,     1      )[0]
      __UpperCamelCase				  =       Image.fromarray(np.uinta(_lowerCamelCase      )      ).convert('RGB'      )
      if str(_lowerCamelCase      ).startswith('mps'      ):
       __UpperCamelCase				  =       torch.manual_seed(_lowerCamelCase      )
      else:
       __UpperCamelCase				  =       torch.Generator(device=_lowerCamelCase      ).manual_seed(_lowerCamelCase      )
      __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 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase=0      ):

      '''simple docstring'''




      __UpperCamelCase				  =       floats_tensor((1, 3, 32, 32)	,     rng=random.Random(_lowerCamelCase      )      ).to(_lowerCamelCase      )
      __UpperCamelCase				  =       image.cpu().permute(0	,     2	,     3	,     1      )[0]
      __UpperCamelCase				  =       Image.fromarray(np.uinta(_lowerCamelCase      )      ).convert('RGB'      )
      if str(_lowerCamelCase      ).startswith('mps'      ):
       __UpperCamelCase				  =       torch.manual_seed(_lowerCamelCase      )
      else:
       __UpperCamelCase				  =       torch.Generator(device=_lowerCamelCase      ).manual_seed(_lowerCamelCase      )
      __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 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      if not hasattr(self.pipeline_class	,     '_optional_components'      ):
       return

      __UpperCamelCase				  =       self.get_dummy_components()
      __UpperCamelCase				  =       self.pipeline_class(**_lowerCamelCase      )
      pipe.to(_lowerCamelCase      )
      pipe.set_progress_bar_config(disable=_lowerCamelCase      )

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

      __UpperCamelCase				  =       self.get_dummy_inputs(_lowerCamelCase      )
      __UpperCamelCase				  =       pipe(**_lowerCamelCase      )[0]

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

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

      __UpperCamelCase				  =       self.get_dummy_inputs(_lowerCamelCase      )
      __UpperCamelCase				  =       pipe_loaded(**_lowerCamelCase      )[0]

      __UpperCamelCase				  =       np.abs(output - output_loaded      ).max()
      self.assertLess(_lowerCamelCase	,     1E-4      )





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       '''cpu'''

      __UpperCamelCase				  =       self.get_dummy_components()
      __UpperCamelCase				  =       self.pipeline_class(**_lowerCamelCase      )
      pipe.to(_lowerCamelCase      )
      pipe.set_progress_bar_config(disable=_lowerCamelCase      )

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

      self.assertEqual(mask.shape	,     (1, 16, 16)      )
      __UpperCamelCase				  =       np.array([0] * 9      )
      __UpperCamelCase				  =       np.abs(mask_slice.flatten() - expected_slice      ).max()
      self.assertLessEqual(_lowerCamelCase	,     1E-3      )
      self.assertEqual(mask[0, -3, -4]	,     0      )





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       '''cpu'''

      __UpperCamelCase				  =       self.get_dummy_components()
      __UpperCamelCase				  =       self.pipeline_class(**_lowerCamelCase      )
      pipe.to(_lowerCamelCase      )
      pipe.set_progress_bar_config(disable=_lowerCamelCase      )

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

      self.assertEqual(image.shape	,     (2, 32, 32, 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(_lowerCamelCase	,     1E-3      )





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=5E-3      )







     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __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(**_lowerCamelCase      )
      __UpperCamelCase				  =       DPMSolverMultistepInverseScheduler(**_lowerCamelCase      )

      __UpperCamelCase				  =       self.pipeline_class(**_lowerCamelCase      )
      pipe.to(_lowerCamelCase      )
      pipe.set_progress_bar_config(disable=_lowerCamelCase      )

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

      self.assertEqual(image.shape	,     (2, 32, 32, 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(_lowerCamelCase	,     1E-3      )


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





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




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





     @classmethod
     def 						UpperCAmelCase					(	cls      ):

      '''simple docstring'''




      __UpperCamelCase				  =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'      )

      __UpperCamelCase				  =       raw_image.convert('RGB'      ).resize((768, 768)      )

      __UpperCamelCase				  =       raw_image





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       torch.manual_seed(0      )

      __UpperCamelCase				  =       StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'	,     safety_checker=_lowerCamelCase	,     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=_lowerCamelCase      )

      __UpperCamelCase				  =       '''a bowl of fruit'''
      __UpperCamelCase				  =       '''a bowl of pears'''

      __UpperCamelCase				  =       pipe.generate_mask(
          image=self.raw_image	,     source_prompt=_lowerCamelCase	,     target_prompt=_lowerCamelCase	,     generator=_lowerCamelCase	,     )

      __UpperCamelCase				  =       pipe.invert(
          prompt=_lowerCamelCase	,     image=self.raw_image	,     inpaint_strength=0.7	,     generator=_lowerCamelCase      ).latents

      __UpperCamelCase				  =       pipe(
          prompt=_lowerCamelCase	,     mask_image=_lowerCamelCase	,     image_latents=_lowerCamelCase	,     generator=_lowerCamelCase	,     negative_prompt=_lowerCamelCase	,     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((768, 768)      )      )
          / 255
      )
      assert np.abs((expected_image - image).max()      ) < 5E-1







     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       torch.manual_seed(0      )

      __UpperCamelCase				  =       StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'	,     safety_checker=_lowerCamelCase	,     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=_lowerCamelCase      )

      __UpperCamelCase				  =       '''a bowl of fruit'''
      __UpperCamelCase				  =       '''a bowl of pears'''

      __UpperCamelCase				  =       pipe.generate_mask(
          image=self.raw_image	,     source_prompt=_lowerCamelCase	,     target_prompt=_lowerCamelCase	,     generator=_lowerCamelCase	,     )

      __UpperCamelCase				  =       pipe.invert(
          prompt=_lowerCamelCase	,     image=self.raw_image	,     inpaint_strength=0.7	,     generator=_lowerCamelCase	,     num_inference_steps=25	,     ).latents

      __UpperCamelCase				  =       pipe(
          prompt=_lowerCamelCase	,     mask_image=_lowerCamelCase	,     image_latents=_lowerCamelCase	,     generator=_lowerCamelCase	,     negative_prompt=_lowerCamelCase	,     inpaint_strength=0.7	,     num_inference_steps=25	,     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((768, 768)      )      )
          / 255
      )
      assert np.abs((expected_image - image).max()      ) < 5E-1







from __future__ import annotations

import collections
import pprint
from pathlib import Path
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return "".join(sorted(UpperCAmelCase_						)						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return word_by_signature[signature(UpperCAmelCase_						)]


snake_case				:    str       				=     Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case				:    Optional[int]       				=     sorted({word.strip().lower() for word in data.splitlines()})

snake_case				:    str       				=     collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('''anagrams.txt''', '''w''') as file:
          file.write('''all_anagrams = \n ''')
          file.write(pprint.pformat(all_anagrams))





