

'''simple docstring'''

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,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class 							_snake_case       (				a_		,     a_		,     a_		,     unittest.TestCase  ):
   _A				:   str			      =    StableDiffusionControlNetImgaImgPipeline
   _A				:   int			      =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   _A				:   Optional[Any]			      =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _A				:   int			      =    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}  )
   _A				:   Optional[Any]			      =    IMAGE_TO_IMAGE_IMAGE_PARAMS
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str  					=    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   ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str  					=    ControlNetModel(
         block_out_channels=(32, 64)   ,layers_per_block=2   ,in_channels=4   ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,cross_attention_dim=32   ,conditioning_embedding_out_channels=(16, 32)   ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    DDIMScheduler(
         beta_start=0.00_085   ,beta_end=0.012   ,beta_schedule="scaled_linear"   ,clip_sample=_a   ,set_alpha_to_one=_a   ,)
     torch.manual_seed(0							)
     SCREAMING_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   ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:List[str]  					=    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   ,)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    CLIPTextModel(_a							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     SCREAMING_SNAKE_CASE:Tuple  					=    {
         "unet": unet,
         "controlnet": controlnet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components
   def 			__UpperCamelCase  (       self	:					Optional[int]   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					int=0							):
     if str(_a							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    torch.manual_seed(_a							)
     else:
       SCREAMING_SNAKE_CASE:int  					=    torch.Generator(device=_a							).manual_seed(_a							)

     SCREAMING_SNAKE_CASE:List[str]  					=    2
     SCREAMING_SNAKE_CASE:List[str]  					=    randn_tensor(
         (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,generator=_a   ,device=torch.device(_a							)   ,)
     SCREAMING_SNAKE_CASE:str  					=    floats_tensor(control_image.shape   ,rng=random.Random(_a							)							).to(_a							)
     SCREAMING_SNAKE_CASE:List[str]  					=    image.cpu().permute(0   ,2   ,3   ,1							)[0]
     SCREAMING_SNAKE_CASE:str  					=    Image.fromarray(np.uinta(_a							)							).convert("RGB"							).resize((64, 64)							)
     SCREAMING_SNAKE_CASE:str  					=    {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
         "image": image,
         "control_image": control_image,
     }

     return inputs
   def 			__UpperCamelCase  (       self	:					Optional[Any]							):
     return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3							)
   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()   ,reason="XFormers attention is only available with CUDA and `xformers` installed"   ,)
   def 			__UpperCamelCase  (       self	:					str							):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)







   def 			__UpperCamelCase  (       self	:					List[str]							):
     self._test_inference_batch_single_identical(expected_max_diff=2e-3							)





class 							_snake_case       (				a_		,     a_		,     unittest.TestCase  ):
   _A				:   Dict			      =    StableDiffusionControlNetImgaImgPipeline
   _A				:   Optional[int]			      =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   _A				:   Tuple			      =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _A				:   Any			      =    frozenset([]  )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
   def 			__UpperCamelCase  (       self	:					Dict							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    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   ,)
     torch.manual_seed(0							)

     def init_weights(SCREAMING_SNAKE_CASE__	:					int							):
       if isinstance(_a   ,torch.nn.Convad							):
         torch.nn.init.normal(m.weight							)
         m.bias.data.fill_(1.0							)

     SCREAMING_SNAKE_CASE:int  					=    ControlNetModel(
         block_out_channels=(32, 64)   ,layers_per_block=2   ,in_channels=4   ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,cross_attention_dim=32   ,conditioning_embedding_out_channels=(16, 32)   ,)
     controlneta.controlnet_down_blocks.apply(_a							)

     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    ControlNetModel(
         block_out_channels=(32, 64)   ,layers_per_block=2   ,in_channels=4   ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,cross_attention_dim=32   ,conditioning_embedding_out_channels=(16, 32)   ,)
     controlneta.controlnet_down_blocks.apply(_a							)

     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    DDIMScheduler(
         beta_start=0.00_085   ,beta_end=0.012   ,beta_schedule="scaled_linear"   ,clip_sample=_a   ,set_alpha_to_one=_a   ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:int  					=    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   ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str  					=    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   ,)
     SCREAMING_SNAKE_CASE:List[str]  					=    CLIPTextModel(_a							)
     SCREAMING_SNAKE_CASE:List[Any]  					=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     SCREAMING_SNAKE_CASE:List[str]  					=    MultiControlNetModel([controlneta, controlneta]							)

     SCREAMING_SNAKE_CASE:Any  					=    {
         "unet": unet,
         "controlnet": controlnet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components
   def 			__UpperCamelCase  (       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					Any   ,SCREAMING_SNAKE_CASE__	:					str=0							):
     if str(_a							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:str  					=    torch.manual_seed(_a							)
     else:
       SCREAMING_SNAKE_CASE:Dict  					=    torch.Generator(device=_a							).manual_seed(_a							)

     SCREAMING_SNAKE_CASE:Any  					=    2

     SCREAMING_SNAKE_CASE:Any  					=    [
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,generator=_a   ,device=torch.device(_a							)   ,),
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,generator=_a   ,device=torch.device(_a							)   ,),
     ]

     SCREAMING_SNAKE_CASE:Dict  					=    floats_tensor(control_image[0].shape   ,rng=random.Random(_a							)							).to(_a							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    image.cpu().permute(0   ,2   ,3   ,1							)[0]
     SCREAMING_SNAKE_CASE:Tuple  					=    Image.fromarray(np.uinta(_a							)							).convert("RGB"							).resize((64, 64)							)
     SCREAMING_SNAKE_CASE:Tuple  					=    {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
         "image": image,
         "control_image": control_image,
     }

     return inputs
   def 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:int  					=    self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Tuple  					=    self.pipeline_class(**_a							)
     pipe.to(_a							)

     SCREAMING_SNAKE_CASE:Any  					=    10.0
     SCREAMING_SNAKE_CASE:List[str]  					=    4

     SCREAMING_SNAKE_CASE:List[str]  					=    self.get_dummy_inputs(_a							)
     SCREAMING_SNAKE_CASE:Dict  					=    steps
     SCREAMING_SNAKE_CASE:Dict  					=    scale
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    pipe(**_a							)[0]

     SCREAMING_SNAKE_CASE:Any  					=    self.get_dummy_inputs(_a							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    steps
     SCREAMING_SNAKE_CASE:Optional[int]  					=    scale
     SCREAMING_SNAKE_CASE:Tuple  					=    pipe(**_a   ,control_guidance_start=0.1   ,control_guidance_end=0.2							)[0]

     SCREAMING_SNAKE_CASE:int  					=    self.get_dummy_inputs(_a							)
     SCREAMING_SNAKE_CASE:str  					=    steps
     SCREAMING_SNAKE_CASE:str  					=    scale
     SCREAMING_SNAKE_CASE:List[str]  					=    pipe(**_a   ,control_guidance_start=[0.1, 0.3]   ,control_guidance_end=[0.2, 0.7]							)[0]

     SCREAMING_SNAKE_CASE:Dict  					=    self.get_dummy_inputs(_a							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    steps
     SCREAMING_SNAKE_CASE:List[str]  					=    scale
     SCREAMING_SNAKE_CASE:Any  					=    pipe(**_a   ,control_guidance_start=0.4   ,control_guidance_end=[0.5, 0.8]							)[0]

     # make sure that all outputs are different
     assert np.sum(np.abs(output_a - output_a							)							) > 1e-3
     assert np.sum(np.abs(output_a - output_a							)							) > 1e-3
     assert np.sum(np.abs(output_a - output_a							)							) > 1e-3
   def 			__UpperCamelCase  (       self	:					Union[str, Any]							):
     return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3							)
   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()   ,reason="XFormers attention is only available with CUDA and `xformers` installed"   ,)
   def 			__UpperCamelCase  (       self	:					Dict							):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     self._test_inference_batch_single_identical(expected_max_diff=2e-3							)







   def 			__UpperCamelCase  (       self	:					int							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    self.get_dummy_components()
     SCREAMING_SNAKE_CASE:int  					=    self.pipeline_class(**_a							)
     pipe.to(_a							)
     pipe.set_progress_bar_config(disable=_a							)
     with tempfile.TemporaryDirectory() as tmpdir:
       try:
         # save_pretrained is not implemented for Multi-ControlNet
         pipe.save_pretrained(_a							)
       except NotImplementedError:
         pass





@slow
@require_torch_gpu
class 							_snake_case       (				unittest.TestCase  ):
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"							)

     SCREAMING_SNAKE_CASE:Tuple  					=    StableDiffusionControlNetImgaImgPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"   ,safety_checker=_a   ,controlnet=_a							)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=_a							)

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    torch.Generator(device="cpu"							).manual_seed(0							)
     SCREAMING_SNAKE_CASE:int  					=    "evil space-punk bird"
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"							).resize((512, 512)							)
     SCREAMING_SNAKE_CASE:List[str]  					=    load_image(
         "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"							).resize((512, 512)							)

     SCREAMING_SNAKE_CASE:List[Any]  					=    pipe(
         _a   ,_a   ,control_image=_a   ,generator=_a   ,output_type="np"   ,num_inference_steps=50   ,strength=0.6   ,)

     SCREAMING_SNAKE_CASE:Any  					=    output.images[0]

     assert image.shape == (512, 512, 3)

     SCREAMING_SNAKE_CASE:int  					=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"							)

     assert np.abs(expected_image - image							).max() < 9e-2








"""simple docstring"""




def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       int  ) ->				float:
       lowercase    :				List[Any]       		=							(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total


def   __magic_name__	(      ) ->				int:
       print(sum_of_series(1     , 1     , 10  )  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


