



'''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 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    int   				=					StableDiffusionControlNetImgaImgPipeline
							lowerCAmelCase       :    Tuple   				=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							lowerCAmelCase       :    Tuple   				=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCAmelCase       :    Union[str, Any]   				=					IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}   )
							lowerCAmelCase       :    Union[str, Any]   				=					IMAGE_TO_IMAGE_IMAGE_PARAMS







							def 							__lowercase			(							self					:       Any		):
									torch.manual_seed(0		)
									_a     :      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		)
									_a     :      Optional[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)	,)
									torch.manual_seed(0		)
									_a     :      List[str]        =							DDIMScheduler(
									    beta_start=0.0_00_85	,beta_end=0.0_12	,beta_schedule='scaled_linear'	,clip_sample=_UpperCAmelCase	,set_alpha_to_one=_UpperCAmelCase	,)
									torch.manual_seed(0		)
									_a     :      List[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		)
									_a     :      List[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	,)
									_a     :      str        =							CLIPTextModel(_UpperCAmelCase		)
									_a     :      List[str]        =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[str]=0		):
									if str(_UpperCAmelCase		).startswith('mps'		):
											_a     :      Optional[Any]        =							torch.manual_seed(_UpperCAmelCase		)
									else:
											_a     :      Dict        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)

									_a     :      int        =							2
									_a     :      str        =							randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,generator=_UpperCAmelCase	,device=torch.device(_UpperCAmelCase		)	,)
									_a     :      int        =							floats_tensor(control_image.shape	,rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
									_a     :      int        =							image.cpu().permute(0	,2	,3	,1		)[0]
									_a     :      List[Any]        =							Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert('RGB'		).resize((64, 64)		)
									_a     :      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 							__lowercase			(							self					:       Tuple		):
									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 							__lowercase			(							self					:       Optional[int]		):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







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

class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					StableDiffusionControlNetImgaImgPipeline
							lowerCAmelCase       :    Optional[Any]   				=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							lowerCAmelCase       :    Optional[int]   				=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCAmelCase       :    List[Any]   				=					frozenset([]   )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







							def 							__lowercase			(							self					:       Tuple		):
									torch.manual_seed(0		)
									_a     :      Dict        =							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					:       int		):
											if isinstance(_UpperCAmelCase	,torch.nn.Convad		):
													torch.nn.init.normal(m.weight		)
													m.bias.data.fill_(1.0		)

									_a     :      Union[str, 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(_UpperCAmelCase		)

									torch.manual_seed(0		)
									_a     :      Union[str, 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(_UpperCAmelCase		)

									torch.manual_seed(0		)
									_a     :      Optional[int]        =							DDIMScheduler(
									    beta_start=0.0_00_85	,beta_end=0.0_12	,beta_schedule='scaled_linear'	,clip_sample=_UpperCAmelCase	,set_alpha_to_one=_UpperCAmelCase	,)
									torch.manual_seed(0		)
									_a     :      List[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		)
									_a     :      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	,)
									_a     :      Optional[int]        =							CLIPTextModel(_UpperCAmelCase		)
									_a     :      Dict        =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

									_a     :      str        =							MultiControlNetModel([controlneta, controlneta]		)

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







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int=0		):
									if str(_UpperCAmelCase		).startswith('mps'		):
											_a     :      List[str]        =							torch.manual_seed(_UpperCAmelCase		)
									else:
											_a     :      Dict        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)

									_a     :      str        =							2

									_a     :      Union[str, Any]        =							[
									    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     :      str        =							floats_tensor(control_image[0].shape	,rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
									_a     :      int        =							image.cpu().permute(0	,2	,3	,1		)[0]
									_a     :      Optional[Any]        =							Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert('RGB'		).resize((64, 64)		)
									_a     :      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 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Dict        =							self.get_dummy_components()
									_a     :      List[str]        =							self.pipeline_class(**_UpperCAmelCase		)
									pipe.to(_UpperCAmelCase		)

									_a     :      Any        =							10.0
									_a     :      Optional[Any]        =							4

									_a     :      Tuple        =							self.get_dummy_inputs(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							steps
									_a     :      Optional[int]        =							scale
									_a     :      Any        =							pipe(**_UpperCAmelCase		)[0]

									_a     :      Any        =							self.get_dummy_inputs(_UpperCAmelCase		)
									_a     :      Tuple        =							steps
									_a     :      List[str]        =							scale
									_a     :      List[Any]        =							pipe(**_UpperCAmelCase	,control_guidance_start=0.1	,control_guidance_end=0.2		)[0]

									_a     :      str        =							self.get_dummy_inputs(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							steps
									_a     :      int        =							scale
									_a     :      List[str]        =							pipe(**_UpperCAmelCase	,control_guidance_start=[0.1, 0.3]	,control_guidance_end=[0.2, 0.7]		)[0]

									_a     :      str        =							self.get_dummy_inputs(_UpperCAmelCase		)
									_a     :      int        =							steps
									_a     :      str        =							scale
									_a     :      int        =							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 							__lowercase			(							self					:       Optional[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 							__lowercase			(							self					:       List[Any]		):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Optional[int]        =							self.get_dummy_components()
									_a     :      Dict        =							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 	__magic_name__						(    unittest.TestCase   ):







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







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Tuple        =							ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'		)

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

									_a     :      Optional[Any]        =							pipe(
									    _UpperCAmelCase	,_UpperCAmelCase	,control_image=_UpperCAmelCase	,generator=_UpperCAmelCase	,output_type='np'	,num_inference_steps=50	,strength=0.6	,)

									_a     :      Tuple        =							output.images[0]

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

									_a     :      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'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

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




