

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=data
        _lowercase      			=[0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]




  @staticmethod
  def     A__	(							lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=B'\x80' + B'\x00' * (63 - (len(self.data   ) + 8) % 64)
        _lowercase      			=self.data + padding + struct.pack('>Q'				,  8 * len(self.data   )   )
        return padded_data




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        return [
            self.padded_data[i : i + 64] for i in range(0				,  len(self.padded_data   )				,  64   )
        ]




  def     A__	(							self				,  lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=list(struct.unpack('>16L'				,  lowerCAmelCase   )   ) + [0] * 64
        for i in range(16				,  80   ):
              _lowercase      			=self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])				,  1   )
        return w




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=self.padding()
        _lowercase      			=self.split_blocks()
        for block in self.blocks:
              _lowercase      			=self.expand_block(lowerCAmelCase   )
              _lowercase			,			_lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self.h
              for i in range(0				,  80   ):
                    if 0 <= i < 20:
                          _lowercase      			=(b & c) | ((~b) & d)
                          _lowercase      			=0x5_a_8_2_7_9_9_9
                    elif 20 <= i < 40:
                          _lowercase      			=b ^ c ^ d
                          _lowercase      			=0x6_e_d_9_e_b_a_1
                    elif 40 <= i < 60:
                          _lowercase      			=(b & c) | (b & d) | (c & d)
                          _lowercase      			=0x8_f_1_b_b_c_d_c
                    elif 60 <= i < 80:
                          _lowercase      			=b ^ c ^ d
                          _lowercase      			=0xc_a_6_2_c_1_d_6
                    _lowercase			,			_lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=(
                        self.rotate(lowerCAmelCase				,  5   ) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
                        a,
                        self.rotate(lowerCAmelCase				,  30   ),
                        c,
                        d,
                    )
              _lowercase      			=(
                  self.h[0] + a & 0xf_f_f_f_f_f_f_f,
                  self.h[1] + b & 0xf_f_f_f_f_f_f_f,
                  self.h[2] + c & 0xf_f_f_f_f_f_f_f,
                  self.h[3] + d & 0xf_f_f_f_f_f_f_f,
                  self.h[4] + e & 0xf_f_f_f_f_f_f_f,
              )
        return ("{:08x}" * 5).format(*self.h   )
def 	a (     )       -> Tuple:





      """simple docstring"""



      _lowercase      			=B'Test String'
      assert SHAaHash(A__	).final_hash() == hashlib.shaa(A__	).hexdigest()  # noqa: S324
def 	a (     )       -> str:





      """simple docstring"""



      _lowercase      			=argparse.ArgumentParser(description='Process some strings or files'	)
      parser.add_argument(
          '--string' ,  dest='input_string' ,  default='Hello World!! Welcome to Cryptography' ,  help='Hash the string' ,  )
      parser.add_argument('--file' ,  dest='input_file' ,  help='Hash contents of a file'	)
      _lowercase      			=parser.parse_args()
      _lowercase      			=args.input_string
      # In any case hash input should be a bytestring
      if args.input_file:
            with open(args.input_file ,  'rb'	) as f:
                  _lowercase      			=f.read()
      else:
            _lowercase      			=bytes(A__ ,  'utf-8'	)
      print(SHAaHash(A__	).final_hash()	)


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod()





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




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=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				,  )
        _lowercase      			=DDIMScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_one=lowerCAmelCase				,  )
        _lowercase      			=DDIMInverseScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_zero=lowerCAmelCase				,  )
        torch.manual_seed(0   )
        _lowercase      			=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   )
        _lowercase      			=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=1_000				,  hidden_act='gelu'				,  projection_dim=512				,  )
        _lowercase      			=CLIPTextModel(lowerCAmelCase   )
        _lowercase      			=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=floats_tensor((1, 2, 4, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self   )    ->					Any:
        '''simple docstring'''






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

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=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}   )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe(**lowerCAmelCase   )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(lowerCAmelCase   )
              _lowercase      			=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.'''				,  )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe_loaded(**lowerCAmelCase   )[0]

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




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

        self.assertEqual(image.shape				,  (2, 32, 32, 3)   )
        _lowercase      			=np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]				,  )
        _lowercase      			=np.abs(image_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()

        _lowercase      			={'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
        _lowercase      			=DPMSolverMultistepScheduler(**lowerCAmelCase   )
        _lowercase      			=DPMSolverMultistepInverseScheduler(**lowerCAmelCase   )

        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

        self.assertEqual(image.shape				,  (2, 32, 32, 3)   )
        _lowercase      			=np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]				,  )
        _lowercase      			=np.abs(image_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )





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




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






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




  @classmethod
  def     A__	(							cls   )    ->					int:
        '''simple docstring'''






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

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

        _lowercase      			=raw_image




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

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

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

        _lowercase      			=pipe.invert(
            prompt=lowerCAmelCase				,  image=self.raw_image				,  inpaint_strength=0.7				,  generator=lowerCAmelCase   ).latents

        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  mask_image=lowerCAmelCase				,  image_latents=lowerCAmelCase				,  generator=lowerCAmelCase				,  negative_prompt=lowerCAmelCase				,  inpaint_strength=0.7				,  output_type='numpy'				,  ).images[0]

        _lowercase      			=(
            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     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

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

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

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

        _lowercase      			=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]

        _lowercase      			=(
            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


