'''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      UpperCAmelCase    (		a__       ,		a__       ,		a__       ,		unittest.TestCase   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE   						=       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}   )
       SCREAMING_SNAKE_CASE   						=       IMAGE_TO_IMAGE_IMAGE_PARAMS

       def _lowerCAmelCase(		self					)    ->    Union[str, Any]:
        torch.manual_seed(0					)
        lowercase__							:       Tuple										=   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					)
        lowercase__							:       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)      ,  )
        torch.manual_seed(0					)
        lowercase__							:       Optional[Any]										=   DDIMScheduler(
            beta_start=0.0_0_0_8_5      ,  beta_end=0.0_1_2      ,  beta_schedule='''scaled_linear'''      ,  clip_sample=_snake_case      ,  set_alpha_to_one=_snake_case      ,  )
        torch.manual_seed(0					)
        lowercase__							:       List[str]										=   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					)
        lowercase__							:       Optional[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=1000      ,  )
        lowercase__							:       Union[str, Any]										=   CLIPTextModel(_snake_case					)
        lowercase__							:       List[str]										=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

        lowercase__							:       Tuple										=   {
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0					)    ->    List[str]:






        if str(_snake_case					).startswith('''mps'''					):
         lowercase__							:       str										=   torch.manual_seed(_snake_case					)
        else:
         lowercase__							:       str										=   torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

        lowercase__							:       Tuple										=   2
        lowercase__							:       Dict										=   randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,  generator=_snake_case      ,  device=torch.device(_snake_case					)      ,  )
        lowercase__							:       Any										=   floats_tensor(control_image.shape      ,  rng=random.Random(_snake_case					)					).to(_snake_case					)
        lowercase__							:       Optional[int]										=   image.cpu().permute(0      ,  2      ,  3      ,  1					)[0]
        lowercase__							:       Optional[Any]										=   Image.fromarray(np.uinta(_snake_case					)					).convert('''RGB'''					).resize((64, 64)					)
        lowercase__							:       List[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 _lowerCAmelCase(		self					)    ->    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 _lowerCAmelCase(		self					)    ->    List[Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)


       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        self._test_inference_batch_single_identical(expected_max_diff=2E-3					)



class      UpperCAmelCase    (		a__       ,		a__       ,		unittest.TestCase   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       SCREAMING_SNAKE_CASE   						=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE   						=       frozenset([]   )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        torch.manual_seed(0					)
        lowercase__							:       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(__lowerCAmelCase					):
         if isinstance(_snake_case      ,  torch.nn.Convad					):
          torch.nn.init.normal(m.weight					)
          m.bias.data.fill_(1.0					)

        lowercase__							:       List[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)      ,  )
        controlneta.controlnet_down_blocks.apply(_snake_case					)

        torch.manual_seed(0					)
        lowercase__							:       List[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)      ,  )
        controlneta.controlnet_down_blocks.apply(_snake_case					)

        torch.manual_seed(0					)
        lowercase__							:       List[str]										=   DDIMScheduler(
            beta_start=0.0_0_0_8_5      ,  beta_end=0.0_1_2      ,  beta_schedule='''scaled_linear'''      ,  clip_sample=_snake_case      ,  set_alpha_to_one=_snake_case      ,  )
        torch.manual_seed(0					)
        lowercase__							:       Optional[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					)
        lowercase__							:       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=1000      ,  )
        lowercase__							:       Optional[Any]										=   CLIPTextModel(_snake_case					)
        lowercase__							:       Tuple										=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

        lowercase__							:       str										=   MultiControlNetModel([controlneta, controlneta]					)

        lowercase__							:       int										=   {
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0					)    ->    List[Any]:






        if str(_snake_case					).startswith('''mps'''					):
         lowercase__							:       List[Any]										=   torch.manual_seed(_snake_case					)
        else:
         lowercase__							:       int										=   torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

        lowercase__							:       Tuple										=   2

        lowercase__							:       List[Any]										=   [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,  generator=_snake_case      ,  device=torch.device(_snake_case					)      ,  ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,  generator=_snake_case      ,  device=torch.device(_snake_case					)      ,  ),
        ]

        lowercase__							:       Optional[int]										=   floats_tensor(control_image[0].shape      ,  rng=random.Random(_snake_case					)					).to(_snake_case					)
        lowercase__							:       Any										=   image.cpu().permute(0      ,  2      ,  3      ,  1					)[0]
        lowercase__							:       Union[str, Any]										=   Image.fromarray(np.uinta(_snake_case					)					).convert('''RGB'''					).resize((64, 64)					)
        lowercase__							:       Any										=   {
            '''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 _lowerCAmelCase(		self					)    ->    str:
        lowercase__							:       Tuple										=   self.get_dummy_components()
        lowercase__							:       Dict										=   self.pipeline_class(**_snake_case					)
        pipe.to(_snake_case					)

        lowercase__							:       List[str]										=   1_0.0
        lowercase__							:       Union[str, Any]										=   4

        lowercase__							:       List[str]										=   self.get_dummy_inputs(_snake_case					)
        lowercase__							:       Dict										=   steps
        lowercase__							:       Any										=   scale
        lowercase__							:       Optional[int]										=   pipe(**_snake_case					)[0]

        lowercase__							:       int										=   self.get_dummy_inputs(_snake_case					)
        lowercase__							:       Any										=   steps
        lowercase__							:       int										=   scale
        lowercase__							:       Any										=   pipe(**_snake_case      ,  control_guidance_start=0.1      ,  control_guidance_end=0.2					)[0]

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

        lowercase__							:       Any										=   self.get_dummy_inputs(_snake_case					)
        lowercase__							:       Dict										=   steps
        lowercase__							:       List[str]										=   scale
        lowercase__							:       List[str]										=   pipe(**_snake_case      ,  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 _lowerCAmelCase(		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 _lowerCAmelCase(		self					)    ->    Dict:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)

       def _lowerCAmelCase(		self					)    ->    List[str]:
        self._test_inference_batch_single_identical(expected_max_diff=2E-3					)


       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        lowercase__							:       Dict										=   self.get_dummy_components()
        lowercase__							:       Any										=   self.pipeline_class(**_snake_case					)
        pipe.to(_snake_case					)
        pipe.set_progress_bar_config(disable=_snake_case					)
        with tempfile.TemporaryDirectory() as tmpdir:
         try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(_snake_case					)
         except NotImplementedError:
          pass



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





       '''simple docstring'''



       def _lowerCAmelCase(		self					)    ->    str:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


       def _lowerCAmelCase(		self					)    ->    str:
        lowercase__							:       Any										=   ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''					)

        lowercase__							:       List[str]										=   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''      ,  safety_checker=_snake_case      ,  controlnet=_snake_case					)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_snake_case					)

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

        lowercase__							:       int										=   pipe(
            _snake_case      ,  _snake_case      ,  control_image=_snake_case      ,  generator=_snake_case      ,  output_type='''np'''      ,  num_inference_steps=50      ,  strength=0.6      ,  )

        lowercase__							:       Union[str, Any]										=   output.images[0]

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

        lowercase__							:       Union[str, Any]										=   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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





