
"""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 				lowercase__(				lowercase_		,						lowercase_		,						lowercase_		,						unittest.TestCase   ):
	'''simple docstring'''

	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   __lowerCAmelCase (						self    :Any					)							->			Optional[int]:







			'''simple docstring'''




			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[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					)
			SCREAMING_SNAKE_CASE       :     Tuple				= 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					)
			SCREAMING_SNAKE_CASE       :     Tuple				= DDIMScheduler(
			    beta_start=0.0_0_0_8_5		, beta_end=0.0_1_2		, beta_schedule='''scaled_linear'''		, clip_sample=lowerCamelCase_		, set_alpha_to_one=lowerCamelCase_		, )
			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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					)
			SCREAMING_SNAKE_CASE       :     List[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=10_00		, )
			SCREAMING_SNAKE_CASE       :     List[str]				= CLIPTextModel(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			SCREAMING_SNAKE_CASE       :     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   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :int		, lowerCamelCase_    :str=0					)							->			Any:







			'''simple docstring'''




			if str(lowerCamelCase_					).startswith('''mps'''					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.manual_seed(lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= 2
			SCREAMING_SNAKE_CASE       :     Optional[int]				= randn_tensor(
			    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		, generator=lowerCamelCase_		, device=torch.device(lowerCamelCase_					)		, )
			SCREAMING_SNAKE_CASE       :     Any				= floats_tensor(control_image.shape		, rng=random.Random(lowerCamelCase_					)					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= image.cpu().permute(0		, 2		, 3		, 1					)[0]
			SCREAMING_SNAKE_CASE       :     Dict				= Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('''RGB'''					).resize((64, 64)					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= {
			    '''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    :Optional[Any]					)							->			Tuple:







			'''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    :Dict					)							->			int:







			'''simple docstring'''




			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)







	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			self._test_inference_batch_single_identical(expected_max_diff=2E-3					)





class 				lowercase__(				lowercase_		,						lowercase_		,						unittest.TestCase   ):
	'''simple docstring'''

	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   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Any				= 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_    :Tuple					):
					if isinstance(lowerCamelCase_		, torch.nn.Convad					):
							torch.nn.init.normal(m.weight					)
							m.bias.data.fill_(1.0					)

			SCREAMING_SNAKE_CASE       :     Tuple				= 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(lowerCamelCase_					)

			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= 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(lowerCamelCase_					)

			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= DDIMScheduler(
			    beta_start=0.0_0_0_8_5		, beta_end=0.0_1_2		, beta_schedule='''scaled_linear'''		, clip_sample=lowerCamelCase_		, set_alpha_to_one=lowerCamelCase_		, )
			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     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					)
			SCREAMING_SNAKE_CASE       :     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=10_00		, )
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= CLIPTextModel(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= MultiControlNetModel([controlneta, controlneta]					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= {
			    '''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    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Optional[int]=0					)							->			Dict:







			'''simple docstring'''




			if str(lowerCamelCase_					).startswith('''mps'''					):
					SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.manual_seed(lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Dict				= torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= 2

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

			SCREAMING_SNAKE_CASE       :     Tuple				= floats_tensor(control_image[0].shape		, rng=random.Random(lowerCamelCase_					)					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= image.cpu().permute(0		, 2		, 3		, 1					)[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('''RGB'''					).resize((64, 64)					)
			SCREAMING_SNAKE_CASE       :     Tuple				= {
			    '''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    :Tuple					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_dummy_components()
			SCREAMING_SNAKE_CASE       :     Any				= self.pipeline_class(**lowerCamelCase_					)
			pipe.to(lowerCamelCase_					)

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

			SCREAMING_SNAKE_CASE       :     Dict				= self.get_dummy_inputs(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= steps
			SCREAMING_SNAKE_CASE       :     Any				= scale
			SCREAMING_SNAKE_CASE       :     Optional[int]				= pipe(**lowerCamelCase_					)[0]

			SCREAMING_SNAKE_CASE       :     Any				= self.get_dummy_inputs(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= steps
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scale
			SCREAMING_SNAKE_CASE       :     List[str]				= pipe(**lowerCamelCase_		, control_guidance_start=0.1		, control_guidance_end=0.2					)[0]

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

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.get_dummy_inputs(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= steps
			SCREAMING_SNAKE_CASE       :     Dict				= scale
			SCREAMING_SNAKE_CASE       :     List[Any]				= pipe(**lowerCamelCase_		, 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    :List[Any]					)							->			int:







			'''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    :int					)							->			int:







			'''simple docstring'''




			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)





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







			'''simple docstring'''




			self._test_inference_batch_single_identical(expected_max_diff=2E-3					)







	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




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





@slow
@require_torch_gpu
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :Any					)							->			Dict:







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :Dict					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''					)

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

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

			SCREAMING_SNAKE_CASE       :     Optional[int]				= pipe(
			    lowerCamelCase_		, lowerCamelCase_		, control_image=lowerCamelCase_		, generator=lowerCamelCase_		, output_type='''np'''		, num_inference_steps=50		, strength=0.6		, )

			SCREAMING_SNAKE_CASE       :     Any				= output.images[0]

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

			SCREAMING_SNAKE_CASE       :     Tuple				= 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 typing import TYPE_CHECKING

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


lowerCamelCase__ :       Tuple						     =			{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MCTCTForCTC",
					    "MCTCTModel",
					    "MCTCTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       Tuple						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





