





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, 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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		int			 =	StableDiffusionXLImgaImgPipeline
   _UpperCamelCase   :		List[Any]			 =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
   _UpperCamelCase   :		List[Any]			 =	PipelineTesterMixin.required_optional_params - {'latents'}
   _UpperCamelCase   :		Any			 =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _UpperCamelCase   :		str			 =	IMAGE_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase   :		Any			 =	IMAGE_TO_IMAGE_IMAGE_PARAMS


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Union[str, 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')      ,					attention_head_dim=(2, 4)      ,					use_linear_projection=a      ,					addition_embed_type='text_time'      ,					addition_time_embed_dim=8      ,					transformer_layers_per_block=(1, 2)      ,					projection_class_embeddings_input_dim=80      ,					cross_attention_dim=64      ,					)
     lowercase__           =  EulerDiscreteScheduler(
         beta_start=0.00085      ,					beta_end=0.012      ,					steps_offset=1      ,					beta_schedule='scaled_linear'      ,					timestep_spacing='leading'      ,					)
     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-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1_000      ,					hidden_act='gelu'      ,					projection_dim=32      ,					)
     lowercase__           =  CLIPTextModel(a	)
     lowercase__           =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,					local_files_only=a	)

     lowercase__           =  CLIPTextModelWithProjection(a	)
     lowercase__           =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,					local_files_only=a	)

     lowercase__           =  {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'text_encoder_2': text_encoder_a,
         'tokenizer_2': tokenizer_a,
         # "safety_checker": None,
         # "feature_extractor": None,
     }
     return components


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Optional[int]      ,					a      :    Optional[int]=0	)->       List[str]:





     """simple docstring"""







     lowercase__           =  floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(a	)	).to(a	)
     lowercase__           =  image / 2 + 0.5
     if str(a	).startswith('mps'	):
       lowercase__           =  torch.manual_seed(a	)
     else:
       lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
     lowercase__           =  {
         'prompt': 'A painting of a squirrel eating a burger',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 5.0,
         'output_type': 'numpy',
         'strength': 0.75,
     }
     return inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
     lowercase__           =  self.get_dummy_components()
     lowercase__           =  StableDiffusionXLImgaImgPipeline(**a	)
     lowercase__           =  sd_pipe.to(a	)
     sd_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  sd_pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)

     lowercase__           =  np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Union[str, Any]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Optional[Any]:





     """simple docstring"""







     pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  self.get_dummy_components()
     lowercase__           =  StableDiffusionXLImgaImgPipeline(**a	)
     lowercase__           =  sd_pipe.to(a	)
     lowercase__           =  sd_pipe.to(a	)
     sd_pipe.set_progress_bar_config(disable=a	)

     # forward without prompt embeds
     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * ['this is a negative prompt']
     lowercase__           =  negative_prompt
     lowercase__           =  3 * [inputs['prompt']]

     lowercase__           =  sd_pipe(**a	)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     lowercase__           =  self.get_dummy_inputs(a	)
     lowercase__           =  3 * ['this is a negative prompt']
     lowercase__           =  3 * [inputs.pop('prompt'	)]

     (
         (
         lowercase__   
     )      ,   (
         lowercase__   
     )      ,   (
         lowercase__   
     )      ,   (
         lowercase__   
     )      ,      
     )           =  sd_pipe.encode_prompt(a      ,					negative_prompt=a	)

     lowercase__           =  sd_pipe(
         **a      ,					prompt_embeds=a      ,					negative_prompt_embeds=a      ,					pooled_prompt_embeds=a      ,					negative_pooled_prompt_embeds=a      ,					)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     # make sure that it's equal
     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4






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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    List[str]      ,					a      :    List[str]="cpu"      ,					a      :    Optional[int]=torch.floataa      ,					a      :    List[str]=0	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
     lowercase__           =  np.random.RandomState(a	).standard_normal((1, 4, 64, 64)	)
     lowercase__           =  torch.from_numpy(a	).to(device=a      ,					dtype=a	)
     lowercase__           =  {
         'prompt': 'a photograph of an astronaut riding a horse',
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 7.5,
         'output_type': 'numpy',
     }
     return inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'	)
     pipe.to(a	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_inputs(a	)
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     lowercase__           =  np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]	)
     assert np.abs(image_slice - expected_slice	).max() < 7E-3









def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:

  if exponent == 1:
    return base
  if exponent % 2 == 0:
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						exponent // 2		,						_SCREAMING_SNAKE_CASE		) % modulo_value
    return (x * x) % modulo_value
  else:
    return (base * _modexpt(_SCREAMING_SNAKE_CASE		,						exponent - 1		,						_SCREAMING_SNAKE_CASE		)) % modulo_value




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = 1777		,						_SCREAMING_SNAKE_CASE = 1855		,						_SCREAMING_SNAKE_CASE = 8		)    ->       int:
  lowercase__           =  base
  for _ in range(1		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						10**digits		)

  return result


if __name__ == "__main__":
      print(f'''{solution() = }''')


