'''simple docstring'''

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 				_lowercase	(      lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase   ):
 '''simple docstring'''


 UpperCAmelCase_      :      int      							=   StableDiffusionXLImgaImgPipeline
 UpperCAmelCase_      :      Any      							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
 UpperCAmelCase_      :      Optional[Any]      							=   PipelineTesterMixin.required_optional_params - {'''latents'''}
 UpperCAmelCase_      :      List[str]      							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_      :      int      							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_      :      int      							=   IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 		lowerCAmelCase__					(				self							)			->			int:



    '''simple docstring'''



    torch.manual_seed(0							)
    UpperCAmelCase__							:				Union[str, 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=lowerCamelCase_					,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__							:				Optional[int]   				=  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__							:				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=128					,)
    torch.manual_seed(0							)
    UpperCAmelCase__							:				Tuple   				=  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__							:				str   				=  CLIPTextModel(lowerCamelCase_							)
    UpperCAmelCase__							:				List[str]   				=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					,local_files_only=lowerCamelCase_							)

    UpperCAmelCase__							:				List[Any]   				=  CLIPTextModelWithProjection(lowerCamelCase_							)
    UpperCAmelCase__							:				Tuple   				=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					,local_files_only=lowerCamelCase_							)

    UpperCAmelCase__							:				List[Any]   				=  {
        '''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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			Any:



    '''simple docstring'''



    UpperCAmelCase__							:				str   				=  floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
    UpperCAmelCase__							:				str   				=  image / 2 + 0.5
    if str(lowerCamelCase_							).startswith('''mps'''							):
       UpperCAmelCase__							:				Dict   				=  torch.manual_seed(lowerCamelCase_							)
    else:
       UpperCAmelCase__							:				Optional[int]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
    UpperCAmelCase__							:				Optional[int]   				=  {
        '''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 		lowerCAmelCase__					(				self							)			->			str:



    '''simple docstring'''



    UpperCAmelCase__							:				Dict   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__							:				int   				=  self.get_dummy_components()
    UpperCAmelCase__							:				Any   				=  StableDiffusionXLImgaImgPipeline(**lowerCamelCase_							)
    UpperCAmelCase__							:				List[Any]   				=  sd_pipe.to(lowerCamelCase_							)
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

    UpperCAmelCase__							:				Dict   				=  self.get_dummy_inputs(lowerCamelCase_							)
    UpperCAmelCase__							:				str   				=  sd_pipe(**lowerCamelCase_							).images
    UpperCAmelCase__							:				Dict   				=  image[0, -3:, -3:, -1]

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

    UpperCAmelCase__							:				Optional[int]   				=  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 		lowerCAmelCase__					(				self							)			->			List[str]:



    '''simple docstring'''



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




 def 		lowerCAmelCase__					(				self							)			->			Tuple:



    '''simple docstring'''



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




 def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



    '''simple docstring'''



    pass




 def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



    '''simple docstring'''



    UpperCAmelCase__							:				str   				=  self.get_dummy_components()
    UpperCAmelCase__							:				Optional[int]   				=  StableDiffusionXLImgaImgPipeline(**lowerCamelCase_							)
    UpperCAmelCase__							:				Optional[Any]   				=  sd_pipe.to(lowerCamelCase_							)
    UpperCAmelCase__							:				Any   				=  sd_pipe.to(lowerCamelCase_							)
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

    # forward without prompt embeds
    UpperCAmelCase__							:				str   				=  self.get_dummy_inputs(lowerCamelCase_							)
    UpperCAmelCase__							:				Dict   				=  3 * ['''this is a negative prompt''']
    UpperCAmelCase__							:				Dict   				=  negative_prompt
    UpperCAmelCase__							:				Tuple   				=  3 * [inputs['''prompt''']]

    UpperCAmelCase__							:				Optional[int]   				=  sd_pipe(**lowerCamelCase_							)
    UpperCAmelCase__							:				Any   				=  output.images[0, -3:, -3:, -1]

    # forward with prompt embeds
    UpperCAmelCase__							:				Union[str, Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
    UpperCAmelCase__							:				Union[str, Any]   				=  3 * ['''this is a negative prompt''']
    UpperCAmelCase__							:				Dict   				=  3 * [inputs.pop('''prompt'''							)]

    (
        UpperCAmelCase__      
    )							:				Optional[int]   				=  sd_pipe.encode_prompt(lowerCamelCase_					,negative_prompt=lowerCamelCase_							)

    UpperCAmelCase__							:				List[Any]   				=  sd_pipe(
        **lowerCamelCase_					,prompt_embeds=lowerCamelCase_					,negative_prompt_embeds=lowerCamelCase_					,pooled_prompt_embeds=lowerCamelCase_					,negative_pooled_prompt_embeds=lowerCamelCase_					,)
    UpperCAmelCase__							:				Dict   				=  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 				_lowercase	(      unittest.TestCase   ):
 '''simple docstring'''


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



    '''simple docstring'''



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




 def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_="cpu"					,lowerCamelCase_=torch.floataa					,lowerCamelCase_=0							)			->			str:



    '''simple docstring'''



    UpperCAmelCase__							:				Optional[int]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
    UpperCAmelCase__							:				str   				=  np.random.RandomState(lowerCamelCase_							).standard_normal((1, 4, 64, 64)							)
    UpperCAmelCase__							:				Tuple   				=  torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_					,dtype=lowerCamelCase_							)
    UpperCAmelCase__							:				Optional[int]   				=  {
        '''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 		lowerCAmelCase__					(				self							)			->			str:



    '''simple docstring'''



    UpperCAmelCase__							:				List[Any]   				=  DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''							)
    pipe.to(lowerCamelCase_							)
    pipe.set_progress_bar_config(disable=lowerCamelCase_							)

    UpperCAmelCase__							:				Tuple   				=  self.get_inputs(lowerCamelCase_							)
    UpperCAmelCase__							:				int   				=  pipe(**lowerCamelCase_							).images
    UpperCAmelCase__							:				Optional[int]   				=  image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    UpperCAmelCase__							:				Optional[int]   				=  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





'''simple docstring'''

import math







def __UpperCamelCase(   _A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   return math.pow(_A  ,			2    ) - a







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   return 2 * x







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  2.0

   while start <= a:
      UpperCAmelCase__							:				int   				=  math.pow(_A  ,			2    )

   return start







def __UpperCamelCase(   _A					:				float  ,			_A					:				int = 99_99  ,			_A					:				float = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1    ):
   '''simple docstring'''

   if a < 0:
      raise ValueError('''math domain error'''    )

   UpperCAmelCase__							:				Any   				=  get_initial_point(_A    )

   for _ in range(_A    ):
      UpperCAmelCase__							:				Optional[Any]   				=  value
      UpperCAmelCase__							:				Tuple   				=  value - fx(_A  ,			_A    ) / fx_derivative(_A    )
      if abs(prev_value - value    ) < tolerance:
         return value

   return value


if __name__ == "__main__":
  from doctest import testmod

  testmod()




