






# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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    __lowercase   (						lowercase_      ,   lowercase_      ,   lowercase_      ,   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							:      Dict					):


         """simple docstring"""



         torch.manual_seed(0					)
         __A       =						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					)
         __A       =						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					)
         __A       =						DDIMScheduler(
             beta_start=0.00085			,						beta_end=0.012			,						beta_schedule="""scaled_linear"""			,						clip_sample=UpperCamelCase_			,						set_alpha_to_one=UpperCamelCase_			,						)
         torch.manual_seed(0					)
         __A       =						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					)
         __A       =						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			,						)
         __A       =						CLIPTextModel(UpperCamelCase_					)
         __A       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

         __A       =						{
             """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							:      Tuple			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[str]=0					):


         """simple docstring"""



         if str(UpperCamelCase_					).startswith("""mps"""					):
             __A       =						torch.manual_seed(UpperCamelCase_					)
         else:
             __A       =						torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

         __A       =						2
         __A       =						randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,						generator=UpperCamelCase_			,						device=torch.device(UpperCamelCase_					)			,						)
         __A       =						floats_tensor(control_image.shape			,						rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
         __A       =						image.cpu().permute(0			,						2			,						3			,						1					)[0]
         __A       =						Image.fromarray(np.uinta(UpperCamelCase_					)					).convert("""RGB"""					).resize((64, 64)					)
         __A       =						{
             """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							:      List[str]					):


         """simple docstring"""



         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							:      Optional[Any]					):


         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)





     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         self._test_inference_batch_single_identical(expected_max_diff=2e-3					)


class    __lowercase   (						lowercase_      ,   lowercase_      ,   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]					):


         """simple docstring"""



         torch.manual_seed(0					)
         __A       =						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(UpperCamelCase_							:      Tuple					):
             if isinstance(UpperCamelCase_			,						torch.nn.Convad					):
                 torch.nn.init.normal(m.weight					)
                 m.bias.data.fill_(1.0					)

         __A       =						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(UpperCamelCase_					)

         torch.manual_seed(0					)
         __A       =						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(UpperCamelCase_					)

         torch.manual_seed(0					)
         __A       =						DDIMScheduler(
             beta_start=0.00085			,						beta_end=0.012			,						beta_schedule="""scaled_linear"""			,						clip_sample=UpperCamelCase_			,						set_alpha_to_one=UpperCamelCase_			,						)
         torch.manual_seed(0					)
         __A       =						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					)
         __A       =						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			,						)
         __A       =						CLIPTextModel(UpperCamelCase_					)
         __A       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

         __A       =						MultiControlNetModel([controlneta, controlneta]					)

         __A       =						{
             """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							:      Optional[int]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Optional[Any]=0					):


         """simple docstring"""



         if str(UpperCamelCase_					).startswith("""mps"""					):
             __A       =						torch.manual_seed(UpperCamelCase_					)
         else:
             __A       =						torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

         __A       =						2

         __A       =						[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,						generator=UpperCamelCase_			,						device=torch.device(UpperCamelCase_					)			,						),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,						generator=UpperCamelCase_			,						device=torch.device(UpperCamelCase_					)			,						),
         ]

         __A       =						floats_tensor(control_image[0].shape			,						rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
         __A       =						image.cpu().permute(0			,						2			,						3			,						1					)[0]
         __A       =						Image.fromarray(np.uinta(UpperCamelCase_					)					).convert("""RGB"""					).resize((64, 64)					)
         __A       =						{
             """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							:      int					):


         """simple docstring"""



         __A       =						self.get_dummy_components()
         __A       =						self.pipeline_class(**UpperCamelCase_					)
         pipe.to(UpperCamelCase_					)

         __A       =						10.0
         __A       =						4

         __A       =						self.get_dummy_inputs(UpperCamelCase_					)
         __A       =						steps
         __A       =						scale
         __A       =						pipe(**UpperCamelCase_					)[0]

         __A       =						self.get_dummy_inputs(UpperCamelCase_					)
         __A       =						steps
         __A       =						scale
         __A       =						pipe(**UpperCamelCase_			,						control_guidance_start=0.1			,						control_guidance_end=0.2					)[0]

         __A       =						self.get_dummy_inputs(UpperCamelCase_					)
         __A       =						steps
         __A       =						scale
         __A       =						pipe(**UpperCamelCase_			,						control_guidance_start=[0.1, 0.3]			,						control_guidance_end=[0.2, 0.7]					)[0]

         __A       =						self.get_dummy_inputs(UpperCamelCase_					)
         __A       =						steps
         __A       =						scale
         __A       =						pipe(**UpperCamelCase_			,						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]					):


         """simple docstring"""



         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[str]					):


         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						self.get_dummy_components()
         __A       =						self.pipeline_class(**UpperCamelCase_					)
         pipe.to(UpperCamelCase_					)
         pipe.set_progress_bar_config(disable=UpperCamelCase_					)
         with tempfile.TemporaryDirectory() as tmpdir:
             try:
                 # save_pretrained is not implemented for Multi-ControlNet
                 pipe.save_pretrained(UpperCamelCase_					)
             except NotImplementedError:
                 pass


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







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""					)

         __A       =						StableDiffusionControlNetImgaImgPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""			,						safety_checker=UpperCamelCase_			,						controlnet=UpperCamelCase_					)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=UpperCamelCase_					)

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

         __A       =						pipe(
             UpperCamelCase_			,						UpperCamelCase_			,						control_image=UpperCamelCase_			,						generator=UpperCamelCase_			,						output_type="""np"""			,						num_inference_steps=50			,						strength=0.6			,						)

         __A       =						output.images[0]

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

         __A       =						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












from __future__ import annotations

from PIL import Image

# Define glider example
__a   :							Tuple						= [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__a   :							Optional[Any]						= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]     )    ->   list[list[int]]:





    """simple docstring"""

    __A       =						[]
    for i in range(len(__lowercase     )     ):
        __A       =						[]
        for j in range(len(cells[i]     )     ):
            # Get the number of live neighbours
            __A       =						0
            if i > 0 and j > 0:
                neighbour_count += cells[i - 1][j - 1]
            if i > 0:
                neighbour_count += cells[i - 1][j]
            if i > 0 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i - 1][j + 1]
            if j > 0:
                neighbour_count += cells[i][j - 1]
            if j < len(cells[i]     ) - 1:
                neighbour_count += cells[i][j + 1]
            if i < len(__lowercase     ) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(__lowercase     ) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(__lowercase     ) - 1 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i + 1][j + 1]

            # Rules of the game of life (excerpt from Wikipedia):
            # 1. Any live cell with two or three live neighbours survives.
            # 2. Any dead cell with three live neighbours becomes a live cell.
            # 3. All other live cells die in the next generation.
            #    Similarly, all other dead cells stay dead.
            __A       =						cells[i][j] == 1
            if (
                (alive and 2 <= neighbour_count <= 3)
                or not alive
                and neighbour_count == 3
            ):
                next_generation_row.append(1     )
            else:
                next_generation_row.append(0     )

        next_generation.append(__lowercase     )
    return next_generation



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]      ,					__lowercase :      int     )    ->   list[Image.Image]:





    """simple docstring"""

    __A       =						[]
    for _ in range(__lowercase     ):
        # Create output image
        __A       =						Image.new("""RGB"""      ,					(len(cells[0]     ), len(__lowercase     ))     )
        __A       =						img.load()

        # Save cells to image
        for x in range(len(__lowercase     )     ):
            for y in range(len(cells[0]     )     ):
                __A       =						2_5_5 - cells[y][x] * 2_5_5
                __A       =						(colour, colour, colour)

        # Save image
        images.append(__lowercase     )
        __A       =						new_generation(__lowercase     )
    return images


if __name__ == "__main__":
  __a   :							Optional[int]						= generate_images(GLIDER, 16)
  images[0].save("out.gif", save_all=True, append_images=images[1:])




