





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




 def      lowercase	(					self       :    Optional[Any]    ):
      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=__UpperCamelCase	,			)
      _UpperCAmelCase     							=   DDIMScheduler(
          beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule="scaled_linear"	,			clip_sample=__UpperCamelCase	,			set_alpha_to_one=__UpperCamelCase	,			)
      _UpperCAmelCase     							=   DDIMInverseScheduler(
          beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule="scaled_linear"	,			clip_sample=__UpperCamelCase	,			set_alpha_to_zero=__UpperCamelCase	,			)
      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(__UpperCamelCase    )
      _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       :    List[Any]	,			snake_case_       :    List[str]	,			snake_case_       :    Union[str, Any]=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 1_6, 1_6)	,			rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
      _UpperCAmelCase     							=   floats_tensor((1, 2, 4, 1_6, 1_6)	,			rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
      if str(__UpperCamelCase    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(__UpperCamelCase    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )
      _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       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    List[str]=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 3, 3_2, 3_2)	,			rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    )[0]
      _UpperCAmelCase     							=   Image.fromarray(np.uinta(__UpperCamelCase    )    ).convert("RGB"    )
      if str(__UpperCamelCase    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(__UpperCamelCase    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )
      _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       :    str	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    int=0    ):
      _UpperCAmelCase     							=   floats_tensor((1, 3, 3_2, 3_2)	,			rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    )[0]
      _UpperCAmelCase     							=   Image.fromarray(np.uinta(__UpperCamelCase    )    ).convert("RGB"    )
      if str(__UpperCamelCase    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(__UpperCamelCase    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )
      _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       :    List[Any]    ):


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

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

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

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

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

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

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

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




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   'cpu'

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

      _UpperCAmelCase     							=   self.get_dummy_mask_inputs(__UpperCamelCase    )
      _UpperCAmelCase     							=   pipe.generate_mask(**__UpperCamelCase    )
      _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(__UpperCamelCase	,			1e-3    )
      self.assertEqual(mask[0, -3, -4]	,			0    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   'cpu'

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

      _UpperCAmelCase     							=   self.get_dummy_inversion_inputs(__UpperCamelCase    )
      _UpperCAmelCase     							=   pipe.invert(**__UpperCamelCase    ).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(__UpperCamelCase	,			1e-3    )




 def      lowercase	(					self       :    int    ):
      super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




 def      lowercase	(					self       :    int    ):
      _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(**__UpperCamelCase    )
      _UpperCAmelCase     							=   DPMSolverMultistepInverseScheduler(**__UpperCamelCase    )

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

      _UpperCAmelCase     							=   self.get_dummy_inversion_inputs(__UpperCamelCase    )
      _UpperCAmelCase     							=   pipe.invert(**__UpperCamelCase    ).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(__UpperCamelCase	,			1e-3    )







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




 def      lowercase	(					self       :    Optional[int]    ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 @classmethod
 def      lowercase	(					cls       :    Tuple    ):
      _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       :    List[str]    ):
      _UpperCAmelCase     							=   torch.manual_seed(0    )

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

      _UpperCAmelCase     							=   'a bowl of fruit'
      _UpperCAmelCase     							=   'a bowl of pears'

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

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

      _UpperCAmelCase     							=   pipe(
          prompt=__UpperCamelCase	,			mask_image=__UpperCamelCase	,			image_latents=__UpperCamelCase	,			generator=__UpperCamelCase	,			negative_prompt=__UpperCamelCase	,			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       :    str    ):
      _UpperCAmelCase     							=   torch.manual_seed(0    )

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

      _UpperCAmelCase     							=   'a bowl of fruit'
      _UpperCAmelCase     							=   'a bowl of pears'

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

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

      _UpperCAmelCase     							=   pipe(
          prompt=__UpperCamelCase	,			mask_image=__UpperCamelCase	,			image_latents=__UpperCamelCase	,			generator=__UpperCamelCase	,			negative_prompt=__UpperCamelCase	,			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"""







__SCREAMING_SNAKE_CASE    	={}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     lowercase_	:				Any													=	(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     lowercase_	:				Optional[int]													=	_calculate(days - 1     ,			__SCREAMING_SNAKE_CASE     ,			late + 1      )

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     lowercase_	:				Any													=	_calculate(days - 1     ,			absent + 1     ,			0      )

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     lowercase_	:				Dict													=	_calculate(days - 1     ,			__SCREAMING_SNAKE_CASE     ,			0      )

     lowercase_	:				str													=	state_late + state_absent + state_ontime

     lowercase_	:				Tuple													=	prizestrings
     return prizestrings



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int = 30      ):
     return _calculate(__SCREAMING_SNAKE_CASE     ,			absent=0     ,			late=0      )


if __name__ == "__main__":
       print(solution())

