
'''simple docstring'''
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_			(		snake_case__      ,  snake_case__      ,  unittest.TestCase   ):
 UpperCAmelCase_						      =  StableDiffusionXLPipeline
 UpperCAmelCase_						      =  TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase_						      =  TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase_						      =  TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_						      =  TEXT_TO_IMAGE_IMAGE_PARAMS





 def     snake_case__						(      self):
     torch.manual_seed(0)
     snake_case_	:      List[Any]            =  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=lowercase_	,    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	,    )
     snake_case_	:      int            =  EulerDiscreteScheduler(
         beta_start=0.00_085	,    beta_end=0.012	,    steps_offset=1	,    beta_schedule="scaled_linear"	,    timestep_spacing="leading"	,    )
     torch.manual_seed(0)
     snake_case_	:      Union[str, Any]            =  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=1_28	,    )
     torch.manual_seed(0)
     snake_case_	:      int            =  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=10_00	,    hidden_act="gelu"	,    projection_dim=32	,    )
     snake_case_	:      Any            =  CLIPTextModel(lowercase_)
     snake_case_	:      str            =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	,    local_files_only=lowercase_)

     snake_case_	:      str            =  CLIPTextModelWithProjection(lowercase_)
     snake_case_	:      str            =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	,    local_files_only=lowercase_)

     snake_case_	:      List[str]            =  {
         "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     snake_case__						(      self	,    lowercase_	,    lowercase_=0):
     if str(lowercase_).startswith("mps"):
         snake_case_	:      List[Any]            =  torch.manual_seed(lowercase_)
     else:
         snake_case_	:      Any            =  torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_	:      List[str]            =  {
         "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     snake_case__						(      self):
     snake_case_	:      Tuple            =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_	:      Dict            =  self.get_dummy_components()
     snake_case_	:      Tuple            =  StableDiffusionXLPipeline(**lowercase_)
     snake_case_	:      Tuple            =  sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_	:      str            =  self.get_dummy_inputs(lowercase_)
     snake_case_	:      Any            =  sd_pipe(**lowercase_).images
     snake_case_	:      Union[str, Any]            =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     snake_case_	:      Tuple            =  np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747])

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





 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  self.get_dummy_components()
     snake_case_	:      str            =  StableDiffusionXLPipeline(**lowercase_)
     snake_case_	:      List[str]            =  sd_pipe.to(lowercase_)
     snake_case_	:      Optional[Any]            =  sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     # forward without prompt embeds
     snake_case_	:      int            =  self.get_dummy_inputs(lowercase_)
     snake_case_	:      List[Any]            =  3 * ["this is a negative prompt"]
     snake_case_	:      Tuple            =  negative_prompt
     snake_case_	:      List[Any]            =  3 * [inputs["prompt"]]

     snake_case_	:      Dict            =  sd_pipe(**lowercase_)
     snake_case_	:      Optional[Any]            =  output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     snake_case_	:      str            =  self.get_dummy_inputs(lowercase_)
     snake_case_	:      Optional[int]            =  3 * ["this is a negative prompt"]
     snake_case_	:      int            =  3 * [inputs.pop("prompt")]

     (
         (
         snake_case_
     )     ,	(
         snake_case_
     )     ,	(
         snake_case_
     )     ,	(
         snake_case_
     )     ,	
     )	:      Tuple            =  sd_pipe.encode_prompt(lowercase_	,    negative_prompt=lowercase_)

     snake_case_	:      Any            =  sd_pipe(
         **lowercase_	,    prompt_embeds=lowercase_	,    negative_prompt_embeds=lowercase_	,    pooled_prompt_embeds=lowercase_	,    negative_pooled_prompt_embeds=lowercase_	,    )
     snake_case_	:      Union[str, Any]            =  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     snake_case__						(      self):
     super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)





 def     snake_case__						(      self):
     super().test_inference_batch_single_identical(expected_max_diff=3E-3)






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





 def     snake_case__						(      self):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





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

     snake_case_	:      Optional[Any]            =  self.get_inputs(lowercase_)
     snake_case_	:      List[str]            =  pipe(**lowercase_).images
     snake_case_	:      Any            =  image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_	:      str            =  np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506])
     assert np.abs(image_slice - expected_slice).max() < 7E-3






'''simple docstring'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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




