'''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     __magic_name__						(    UpperCamelCase__,     UpperCamelCase__,     UpperCamelCase__,     unittest.TestCase):
			UpperCamelCase__				=       StableDiffusionControlNetImgaImgPipeline
			UpperCamelCase__				=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
			UpperCamelCase__				=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			UpperCamelCase__				=       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''})
			UpperCamelCase__				=       IMAGE_TO_IMAGE_IMAGE_PARAMS







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										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    )
										lowercase_					:			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    )
										lowercase_					:			Tuple										=       DDIMScheduler(
										    beta_start=0.0_00_85				,			beta_end=0.0_12				,			beta_schedule="""scaled_linear"""				,			clip_sample=__a				,			set_alpha_to_one=__a				,			)
										torch.manual_seed(0    )
										lowercase_					:			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[Any]										=       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_					:			str										=       CLIPTextModel(__a    )
										lowercase_					:			Union[str, Any]										=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

										lowercase_					:			Union[str, Any]										=       {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[Any]=0    ):
										if str(__a    ).startswith("""mps"""    ):
																	lowercase_					:			Any										=       torch.manual_seed(__a    )
										else:
																	lowercase_					:			Optional[Any]										=       torch.Generator(device=__a    ).manual_seed(__a    )

										lowercase_					:			Union[str, Any]										=       2
										lowercase_					:			List[Any]										=       randn_tensor(
										    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,			generator=__a				,			device=torch.device(__a    )				,			)
										lowercase_					:			Dict										=       floats_tensor(control_image.shape				,			rng=random.Random(__a    )    ).to(__a    )
										lowercase_					:			Tuple										=       image.cpu().permute(0				,			2				,			3				,			1    )[0]
										lowercase_					:			Optional[Any]										=       Image.fromarray(np.uinta(__a    )    ).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 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										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 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										self._test_inference_batch_single_identical(expected_max_diff=2E-3    )





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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										torch.manual_seed(0    )
										lowercase_					:			List[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    )

										def init_weights(lowercase_				:  Union[str, Any]    ):
																	if isinstance(__a				,			torch.nn.Convad    ):
																								torch.nn.init.normal(m.weight    )
																								m.bias.data.fill_(1.0    )

										lowercase_					:			Optional[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    )
										lowercase_					:			Optional[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    )
										lowercase_					:			Optional[Any]										=       DDIMScheduler(
										    beta_start=0.0_00_85				,			beta_end=0.0_12				,			beta_schedule="""scaled_linear"""				,			clip_sample=__a				,			set_alpha_to_one=__a				,			)
										torch.manual_seed(0    )
										lowercase_					:			Tuple										=       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_					:			Dict										=       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_					:			Dict										=       CLIPTextModel(__a    )
										lowercase_					:			Union[str, Any]										=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

										lowercase_					:			Tuple										=       MultiControlNetModel([controlneta, controlneta]    )

										lowercase_					:			Dict										=       {
										    """unet""": unet,
										    """controlnet""": controlnet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  int				,			lowercase_				:  str=0    ):
										if str(__a    ).startswith("""mps"""    ):
																	lowercase_					:			Dict										=       torch.manual_seed(__a    )
										else:
																	lowercase_					:			Any										=       torch.Generator(device=__a    ).manual_seed(__a    )

										lowercase_					:			Optional[Any]										=       2

										lowercase_					:			List[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    )				,			),
										]

										lowercase_					:			Any										=       floats_tensor(control_image[0].shape				,			rng=random.Random(__a    )    ).to(__a    )
										lowercase_					:			int										=       image.cpu().permute(0				,			2				,			3				,			1    )[0]
										lowercase_					:			Dict										=       Image.fromarray(np.uinta(__a    )    ).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 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       self.get_dummy_components()
										lowercase_					:			Union[str, Any]										=       self.pipeline_class(**__a    )
										pipe.to(__a    )

										lowercase_					:			List[Any]										=       10.0
										lowercase_					:			List[Any]										=       4

										lowercase_					:			int										=       self.get_dummy_inputs(__a    )
										lowercase_					:			Optional[int]										=       steps
										lowercase_					:			str										=       scale
										lowercase_					:			Any										=       pipe(**__a    )[0]

										lowercase_					:			int										=       self.get_dummy_inputs(__a    )
										lowercase_					:			Optional[int]										=       steps
										lowercase_					:			List[str]										=       scale
										lowercase_					:			Union[str, Any]										=       pipe(**__a				,			control_guidance_start=0.1				,			control_guidance_end=0.2    )[0]

										lowercase_					:			Tuple										=       self.get_dummy_inputs(__a    )
										lowercase_					:			List[Any]										=       steps
										lowercase_					:			Tuple										=       scale
										lowercase_					:			str										=       pipe(**__a				,			control_guidance_start=[0.1, 0.3]				,			control_guidance_end=[0.2, 0.7]    )[0]

										lowercase_					:			int										=       self.get_dummy_inputs(__a    )
										lowercase_					:			int										=       steps
										lowercase_					:			Union[str, Any]										=       scale
										lowercase_					:			Optional[int]										=       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 	SCREAMING_SNAKE_CASE_     (	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 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Any										=       self.get_dummy_components()
										lowercase_					:			str										=       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     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Tuple										=       ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""    )

										lowercase_					:			str										=       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    )

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

										lowercase_					:			Optional[int]										=       pipe(
										    __a				,			__a				,			control_image=__a				,			generator=__a				,			output_type="""np"""				,			num_inference_steps=50				,			strength=0.6				,			)

										lowercase_					:			List[str]										=       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'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.get_logger(__name__)




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			*__a    :    Dict   ,			**__a    :    str ):
         warnings.warn(
             "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use CLIPImageProcessor instead."   ,			__a   ,			)
         super().__init__(*__a   ,			**__a )




