
"""simple docstring"""



# 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 					lowerCAmelCase			(				snake_case_     ,						snake_case_     ,						snake_case_     ,						unittest.TestCase					):
     """simple docstring"""







     __lowercase						:List[str]          =    StableDiffusionControlNetImgaImgPipeline
     __lowercase						:Union[str, Any]          =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     __lowercase						:Tuple          =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __lowercase						:Dict          =    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}					)
     __lowercase						:List[str]          =    IMAGE_TO_IMAGE_IMAGE_PARAMS




     def    _lowerCAmelCase		(      self							)					->  int:







            '''simple docstring'''





            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=0.00_085 ,		beta_end=0.012 ,		beta_schedule='''scaled_linear''' ,		clip_sample=UpperCamelCase__ ,		set_alpha_to_one=UpperCamelCase__ ,		)
            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=1_000 ,		)
            lowerCamelCase_       							=		CLIPTextModel(UpperCamelCase__							)
            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    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__=0							)					->  int:







            '''simple docstring'''





            if str(UpperCamelCase__							).startswith('''mps'''							):
                   lowerCamelCase_       							=		torch.manual_seed(UpperCamelCase__							)
            else:
                   lowerCamelCase_       							=		torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)

            lowerCamelCase_       							=		2
            lowerCamelCase_       							=		randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,		generator=UpperCamelCase__ ,		device=torch.device(UpperCamelCase__							) ,		)
            lowerCamelCase_       							=		floats_tensor(control_image.shape ,		rng=random.Random(UpperCamelCase__							)							).to(UpperCamelCase__							)
            lowerCamelCase_       							=		image.cpu().permute(0 ,		2 ,		3 ,		1							)[0]
            lowerCamelCase_       							=		Image.fromarray(np.uinta(UpperCamelCase__							)							).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    _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							)					->  List[str]:







            '''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 					lowerCAmelCase			(				snake_case_     ,						snake_case_     ,						unittest.TestCase					):
     """simple docstring"""







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




     def    _lowerCAmelCase		(      self							)					->  Dict:







            '''simple docstring'''





            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(UpperCamelCase__							):
                   if isinstance(UpperCamelCase__ ,		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(UpperCamelCase__							)

            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(UpperCamelCase__							)

            torch.manual_seed(0							)
            lowerCamelCase_       							=		DDIMScheduler(
                beta_start=0.00_085 ,		beta_end=0.012 ,		beta_schedule='''scaled_linear''' ,		clip_sample=UpperCamelCase__ ,		set_alpha_to_one=UpperCamelCase__ ,		)
            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=1_000 ,		)
            lowerCamelCase_       							=		CLIPTextModel(UpperCamelCase__							)
            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    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__=0							)					->  List[Any]:







            '''simple docstring'''





            if str(UpperCamelCase__							).startswith('''mps'''							):
                   lowerCamelCase_       							=		torch.manual_seed(UpperCamelCase__							)
            else:
                   lowerCamelCase_       							=		torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)

            lowerCamelCase_       							=		2

            lowerCamelCase_       							=		[
                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__							) ,		),
            ]

            lowerCamelCase_       							=		floats_tensor(control_image[0].shape ,		rng=random.Random(UpperCamelCase__							)							).to(UpperCamelCase__							)
            lowerCamelCase_       							=		image.cpu().permute(0 ,		2 ,		3 ,		1							)[0]
            lowerCamelCase_       							=		Image.fromarray(np.uinta(UpperCamelCase__							)							).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    _lowerCAmelCase		(      self							)					->  List[Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		self.get_dummy_components()
            lowerCamelCase_       							=		self.pipeline_class(**UpperCamelCase__							)
            pipe.to(UpperCamelCase__							)

            lowerCamelCase_       							=		10.0
            lowerCamelCase_       							=		4

            lowerCamelCase_       							=		self.get_dummy_inputs(UpperCamelCase__							)
            lowerCamelCase_       							=		steps
            lowerCamelCase_       							=		scale
            lowerCamelCase_       							=		pipe(**UpperCamelCase__							)[0]

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

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

            lowerCamelCase_       							=		self.get_dummy_inputs(UpperCamelCase__							)
            lowerCamelCase_       							=		steps
            lowerCamelCase_       							=		scale
            lowerCamelCase_       							=		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							)					->  Dict:







            '''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[int]:







            '''simple docstring'''





            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)




     def    _lowerCAmelCase		(      self							)					->  Union[str, Any]:







            '''simple docstring'''





            self._test_inference_batch_single_identical(expected_max_diff=2e-3							)




     def    _lowerCAmelCase		(      self							)					->  int:







            '''simple docstring'''





            lowerCamelCase_       							=		self.get_dummy_components()
            lowerCamelCase_       							=		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 					lowerCAmelCase			(				unittest.TestCase					):
     """simple docstring"""







     def    _lowerCAmelCase		(      self							)					->  List[Any]:







            '''simple docstring'''





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




     def    _lowerCAmelCase		(      self							)					->  Optional[int]:







            '''simple docstring'''





            lowerCamelCase_       							=		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

            lowerCamelCase_       							=		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__							)

            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((512, 512)							)
            lowerCamelCase_       							=		load_image(
                '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''							).resize((512, 512)							)

            lowerCamelCase_       							=		pipe(
                UpperCamelCase__ ,		UpperCamelCase__ ,		control_image=UpperCamelCase__ ,		generator=UpperCamelCase__ ,		output_type='''np''' ,		num_inference_steps=50 ,		strength=0.6 ,		)

            lowerCamelCase_       							=		output.images[0]

            assert image.shape == (512, 512, 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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


