


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase__			(   A_  ,    A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						int    =    StableDiffusionXLPipeline
    UpperCAmelCase__		:						List[str]    =    TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase__		:						int    =    TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase__		:						Optional[int]    =    TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__		:						Union[str, Any]    =    TEXT_TO_IMAGE_IMAGE_PARAMS






    def _a   (     self       )    ->			Dict:
         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')   ,  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   ,  )
         __UpperCamelCase       				=EulerDiscreteScheduler(
             beta_start=0.0_0085   ,  beta_end=0.012   ,  steps_offset=1   ,  beta_schedule='scaled_linear'   ,  timestep_spacing='leading'   ,  )
         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=32   ,  )
         __UpperCamelCase       				=CLIPTextModel(A_       )
         __UpperCamelCase       				=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,  local_files_only=A_       )

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

         __UpperCamelCase       				={
             '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 _a   (     self   ,  A_   ,  A_=0       )    ->			List[Any]:
         if str(A_       ).startswith('mps'       ):
              __UpperCamelCase       				=torch.manual_seed(A_       )
         else:
              __UpperCamelCase       				=torch.Generator(device=A_       ).manual_seed(A_       )
         __UpperCamelCase       				={
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 5.0,
             'output_type': 'numpy',
         }
         return inputs






    def _a   (     self       )    ->			Any:
         __UpperCamelCase       				='cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=StableDiffusionXLPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

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

         assert image.shape == (1, 64, 64, 3)
         __UpperCamelCase       				=np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]       )

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






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=StableDiffusionXLPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

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

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

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

         (
             (
             __UpperCamelCase
         )				, (
             __UpperCamelCase
         )				, (
             __UpperCamelCase
         )				, (
             __UpperCamelCase
         )				, 
         )       				=sd_pipe.encode_prompt(A_   ,  negative_prompt=A_       )

         __UpperCamelCase       				=sd_pipe(
             **A_   ,  prompt_embeds=A_   ,  negative_prompt_embeds=A_   ,  pooled_prompt_embeds=A_   ,  negative_pooled_prompt_embeds=A_   ,  )
         __UpperCamelCase       				=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






    def _a   (     self       )    ->			List[Any]:
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )






    def _a   (     self       )    ->			str:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






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



    """simple docstring"""






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






    def _a   (     self   ,  A_   ,  A_="cpu"   ,  A_=torch.floataa   ,  A_=0       )    ->			Optional[int]:
         __UpperCamelCase       				=torch.Generator(device=A_       ).manual_seed(A_       )
         __UpperCamelCase       				=np.random.RandomState(A_       ).standard_normal((1, 4, 64, 64)       )
         __UpperCamelCase       				=torch.from_numpy(A_       ).to(device=A_   ,  dtype=A_       )
         __UpperCamelCase       				={
             '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 _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )

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

         assert image.shape == (1, 512, 512, 3)
         __UpperCamelCase       				=np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]       )
         assert np.abs(image_slice - expected_slice       ).max() < 7E-3









import math

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=math.log(2     ,	10							) * number
     __UpperCamelCase       				=round((log_number - int(SCREAMING_SNAKE_CASE__							))     ,	15							)

     return difference

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int = 67_89_10							):
     __UpperCamelCase       				=90
     __UpperCamelCase       				=0

     __UpperCamelCase       				=math.log(1.23     ,	10							)
     __UpperCamelCase       				=math.log(1.24     ,	10							)
     __UpperCamelCase       				=0

     while position < number:
          __UpperCamelCase       				=log_difference(SCREAMING_SNAKE_CASE__							)

          if difference >= upper_limit:
               power_iterator += 93

          elif difference < lower_limit:
               power_iterator += 1_96

          else:
               __UpperCamelCase       				=power_iterator
               power_iterator += 1_96
               position += 1

     return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")





