


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







				"""simple docstring"""



				__UpperCamelCase    :		int	=	StableDiffusionControlNetImgaImgPipeline
				__UpperCamelCase    :		str	=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				__UpperCamelCase    :		List[Any]	=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__UpperCamelCase    :		List[str]	=	IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
				__UpperCamelCase    :		List[str]	=	IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					_snake_case  (self		):
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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		)
										__lowerCAmelCase							       =				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		)
										__lowerCAmelCase							       =				DDIMScheduler(
										    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule='''scaled_linear'''       ,			clip_sample=__lowercase       ,			set_alpha_to_one=__lowercase       ,			)
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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		)
										__lowerCAmelCase							       =				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=10_00       ,			)
										__lowerCAmelCase							       =				CLIPTextModel(__lowercase		)
										__lowerCAmelCase							       =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=0		):
										if str(__lowercase		).startswith('''mps'''		):
																__lowerCAmelCase							       =				torch.manual_seed(__lowercase		)
										else:
																__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(__lowercase		)

										__lowerCAmelCase							       =				2
										__lowerCAmelCase							       =				randn_tensor(
										    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,			generator=__lowercase       ,			device=torch.device(__lowercase		)       ,			)
										__lowerCAmelCase							       =				floats_tensor(control_image.shape       ,			rng=random.Random(__lowercase		)		).to(__lowercase		)
										__lowerCAmelCase							       =				image.cpu().permute(0       ,			2       ,			3       ,			1		)[0]
										__lowerCAmelCase							       =				Image.fromarray(np.uinta(__lowercase		)		).convert('''RGB'''		).resize((64, 64)		)
										__lowerCAmelCase							       =				{
										    '''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 					_snake_case  (self		):
										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 					_snake_case  (self		):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)




				def 					_snake_case  (self		):
										self._test_inference_batch_single_identical(expected_max_diff=2e-3		)


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







				"""simple docstring"""



				__UpperCamelCase    :		Any	=	StableDiffusionControlNetImgaImgPipeline
				__UpperCamelCase    :		List[str]	=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				__UpperCamelCase    :		Dict	=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__UpperCamelCase    :		str	=	frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




				def 					_snake_case  (self		):
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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		):
																if isinstance(__lowercase       ,			torch.nn.Convad		):
																						torch.nn.init.normal(m.weight		)
																						m.bias.data.fill_(1.0		)

										__lowerCAmelCase							       =				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(__lowercase		)

										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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(__lowercase		)

										torch.manual_seed(0		)
										__lowerCAmelCase							       =				DDIMScheduler(
										    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule='''scaled_linear'''       ,			clip_sample=__lowercase       ,			set_alpha_to_one=__lowercase       ,			)
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				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		)
										__lowerCAmelCase							       =				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=10_00       ,			)
										__lowerCAmelCase							       =				CLIPTextModel(__lowercase		)
										__lowerCAmelCase							       =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

										__lowerCAmelCase							       =				MultiControlNetModel([controlneta, controlneta]		)

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=0		):
										if str(__lowercase		).startswith('''mps'''		):
																__lowerCAmelCase							       =				torch.manual_seed(__lowercase		)
										else:
																__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(__lowercase		)

										__lowerCAmelCase							       =				2

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

										__lowerCAmelCase							       =				floats_tensor(control_image[0].shape       ,			rng=random.Random(__lowercase		)		).to(__lowercase		)
										__lowerCAmelCase							       =				image.cpu().permute(0       ,			2       ,			3       ,			1		)[0]
										__lowerCAmelCase							       =				Image.fromarray(np.uinta(__lowercase		)		).convert('''RGB'''		).resize((64, 64)		)
										__lowerCAmelCase							       =				{
										    '''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 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_dummy_components()
										__lowerCAmelCase							       =				self.pipeline_class(**__lowercase		)
										pipe.to(__lowercase		)

										__lowerCAmelCase							       =				10.0
										__lowerCAmelCase							       =				4

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase		)
										__lowerCAmelCase							       =				steps
										__lowerCAmelCase							       =				scale
										__lowerCAmelCase							       =				pipe(**__lowercase		)[0]

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

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

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase		)
										__lowerCAmelCase							       =				steps
										__lowerCAmelCase							       =				scale
										__lowerCAmelCase							       =				pipe(**__lowercase       ,			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 					_snake_case  (self		):
										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 					_snake_case  (self		):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)




				def 					_snake_case  (self		):
										self._test_inference_batch_single_identical(expected_max_diff=2e-3		)




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


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







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

										__lowerCAmelCase							       =				StableDiffusionControlNetImgaImgPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''       ,			safety_checker=__lowercase       ,			controlnet=__lowercase		)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				torch.Generator(device='''cpu'''		).manual_seed(0		)
										__lowerCAmelCase							       =				'''evil space-punk bird'''
										__lowerCAmelCase							       =				load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''		).resize((5_12, 5_12)		)
										__lowerCAmelCase							       =				load_image(
										    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''		).resize((5_12, 5_12)		)

										__lowerCAmelCase							       =				pipe(
										    __lowercase       ,			__lowercase       ,			control_image=__lowercase       ,			generator=__lowercase       ,			output_type='''np'''       ,			num_inference_steps=50       ,			strength=0.6       ,			)

										__lowerCAmelCase							       =				output.images[0]

										assert image.shape == (5_12, 5_12, 3)

										__lowerCAmelCase							       =				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'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

