




import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
	from cookiecutter.main import cookiecutter

	_lowerCamelCase      			=						True
except ImportError:
	_lowerCamelCase      			=						False

_lowerCamelCase      			=						logging.get_logger(__name__)  # pylint: disable=invalid-name




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Namespace				)  ->							List[str]:
				return AddNewModelCommand(args.testing	,					args.testing_file	,					path=args.path				)



class       a					(       _A      ):

					'''simple docstring'''





					@staticmethod
					def   lowerCamelCase_       (     __snake_case       :      ArgumentParser    ):
									UpperCAmelCase_										=					parser.add_parser('''add-new-model'''    )
									add_new_model_parser.add_argument('''--testing''' ,   action='''store_true''' ,   help='''If in testing mode.'''    )
									add_new_model_parser.add_argument('''--testing_file''' ,   type=__snake_case ,   help='''Configuration file on which to run.'''    )
									add_new_model_parser.add_argument(
									    '''--path''' ,   type=__snake_case ,   help='''Path to cookiecutter. Should only be used for testing purposes.'''    )
									add_new_model_parser.set_defaults(func=__snake_case    )
					def __init__(     self       :      int ,   __snake_case       :      bool ,   __snake_case       :      str ,   __snake_case       :      Union[str, Any]=None ,   *__snake_case       :      Optional[Any]    ):
									UpperCAmelCase_										=					testing
									UpperCAmelCase_										=					testing_file
									UpperCAmelCase_										=					path




					def   lowerCamelCase_       (     self       :      Any    ):
									warnings.warn(
									    '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
									    '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
									    '''checks, you should use `transformers-cli add-new-model-like` instead.'''    )
									if not _has_cookiecutter:
													raise ImportError(
													    '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
													    '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''    )
									# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
									UpperCAmelCase_										=					[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
									if len(__snake_case    ) > 0:
													raise ValueError(
													    '''Several directories starting with `cookiecutter-template-` in current working directory. '''
													    '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
													    '''change your working directory.'''    )

									UpperCAmelCase_										=					(
									    Path(__snake_case    ).parent.parent.parent.parent if self._path is None else Path(self._path    ).parent.parent
									)
									UpperCAmelCase_										=					path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

									# Execute cookiecutter
									if not self._testing:
													cookiecutter(str(__snake_case    )    )
									else:
													with open(self._testing_file ,   '''r'''    ) as configuration_file:
																	UpperCAmelCase_										=					json.load(__snake_case    )

													cookiecutter(
													    str(path_to_cookiecutter if self._path is None else self._path    ) ,   no_input=__snake_case ,   extra_context=__snake_case ,   )

									UpperCAmelCase_										=					[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

									# Retrieve configuration
									with open(directory + '''/configuration.json''' ,   '''r'''    ) as configuration_file:
													UpperCAmelCase_										=					json.load(__snake_case    )

									UpperCAmelCase_										=					configuration['''lowercase_modelname''']
									UpperCAmelCase_										=					configuration['''generate_tensorflow_pytorch_and_flax''']
									os.remove(F'{directory}/configuration.json'    )

									UpperCAmelCase_										=					'''PyTorch''' in generate_tensorflow_pytorch_and_flax
									UpperCAmelCase_										=					'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
									UpperCAmelCase_										=					'''Flax''' in generate_tensorflow_pytorch_and_flax

									UpperCAmelCase_										=					F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
									os.makedirs(__snake_case ,   exist_ok=__snake_case    )
									os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,   exist_ok=__snake_case    )

									# Tests require submodules as they have parent imports
									with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,   '''w'''    ):
													pass

									shutil.move(
									    F'{directory}/__init__.py' ,   F'{model_dir}/__init__.py' ,   )
									shutil.move(
									    F'{directory}/configuration_{lowercase_model_name}.py' ,   F'{model_dir}/configuration_{lowercase_model_name}.py' ,   )

									def remove_copy_lines(__snake_case       :      Optional[Any]    ):
													with open(__snake_case ,   '''r'''    ) as f:
																	UpperCAmelCase_										=					f.readlines()
													with open(__snake_case ,   '''w'''    ) as f:
																	for line in lines:
																					if "# Copied from transformers." not in line:
																									f.write(__snake_case    )

									if output_pytorch:
													if not self._testing:
																	remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'    )

													shutil.move(
													    F'{directory}/modeling_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_{lowercase_model_name}.py' ,   )

													shutil.move(
													    F'{directory}/test_modeling_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,   )
									else:
													os.remove(F'{directory}/modeling_{lowercase_model_name}.py'    )
													os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'    )

									if output_tensorflow:
													if not self._testing:
																	remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'    )

													shutil.move(
													    F'{directory}/modeling_tf_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,   )

													shutil.move(
													    F'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,   )
									else:
													os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'    )
													os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'    )

									if output_flax:
													if not self._testing:
																	remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'    )

													shutil.move(
													    F'{directory}/modeling_flax_{lowercase_model_name}.py' ,   F'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,   )

													shutil.move(
													    F'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,   F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,   )
									else:
													os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'    )
													os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'    )

									shutil.move(
									    F'{directory}/{lowercase_model_name}.md' ,   F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,   )

									shutil.move(
									    F'{directory}/tokenization_{lowercase_model_name}.py' ,   F'{model_dir}/tokenization_{lowercase_model_name}.py' ,   )

									shutil.move(
									    F'{directory}/tokenization_fast_{lowercase_model_name}.py' ,   F'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,   )

									from os import fdopen, remove
									from shutil import copymode, move
									from tempfile import mkstemp

									def replace(__snake_case       :      str ,   __snake_case       :      str ,   __snake_case       :      List[str]    ):
													# Create temp file
													UpperCAmelCase_			,							UpperCAmelCase_										=					mkstemp()
													UpperCAmelCase_										=					False
													with fdopen(__snake_case ,   '''w'''    ) as new_file:
																	with open(__snake_case    ) as old_file:
																					for line in old_file:
																									new_file.write(__snake_case    )
																									if line_to_copy_below in line:
																													UpperCAmelCase_										=					True
																													for line_to_copy in lines_to_copy:
																																	new_file.write(__snake_case    )

													if not line_found:
																	raise ValueError(F'Line {line_to_copy_below} was not found in file.'    )

													# Copy the file permissions from the old file to the new file
													copymode(__snake_case ,   __snake_case    )
													# Remove original file
													remove(__snake_case    )
													# Move new file
													move(__snake_case ,   __snake_case    )

									def skip_units(__snake_case       :      List[str]    ):
													return (
													    ("generating PyTorch" in line and not output_pytorch)
													    or ("generating TensorFlow" in line and not output_tensorflow)
													    or ("generating Flax" in line and not output_flax)
													)

									def replace_in_files(__snake_case       :      Dict    ):
													with open(__snake_case    ) as datafile:
																	UpperCAmelCase_										=					[]
																	UpperCAmelCase_										=					False
																	UpperCAmelCase_										=					False
																	for line in datafile:
																					if "# To replace in: " in line and "##" not in line:
																									UpperCAmelCase_										=					line.split('''"'''    )[1]
																									UpperCAmelCase_										=					skip_units(__snake_case    )
																					elif "# Below: " in line and "##" not in line:
																									UpperCAmelCase_										=					line.split('''"'''    )[1]
																									UpperCAmelCase_										=					skip_units(__snake_case    )
																					elif "# End." in line and "##" not in line:
																									if not skip_file and not skip_snippet:
																													replace(__snake_case ,   __snake_case ,   __snake_case    )

																									UpperCAmelCase_										=					[]
																					elif "# Replace with" in line and "##" not in line:
																									UpperCAmelCase_										=					[]
																					elif "##" not in line:
																									lines_to_copy.append(__snake_case    )

													remove(__snake_case    )

									replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'    )
									os.rmdir(__snake_case    )











# 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       a					(       _A     ,       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[str]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Union[str, Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
     lowerCAmelCase    :	List[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     def   lowerCamelCase_       (     self       :      Dict    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					DDIMScheduler(
             beta_start=0.00_085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=__snake_case ,   set_alpha_to_one=__snake_case ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__snake_case    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         UpperCAmelCase_										=					{
             '''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       :      Union[str, Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Any=0    ):
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )

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




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



class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Dict       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Any       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[int]       		=  frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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(__snake_case       :      Tuple    ):
             if isinstance(__snake_case ,   torch.nn.Convad    ):
                 torch.nn.init.normal(m.weight    )
                 m.bias.data.fill_(1.0    )

         UpperCAmelCase_										=					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(__snake_case    )

         torch.manual_seed(0    )
         UpperCAmelCase_										=					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(__snake_case    )

         torch.manual_seed(0    )
         UpperCAmelCase_										=					DDIMScheduler(
             beta_start=0.00_085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=__snake_case ,   set_alpha_to_one=__snake_case ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					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 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__snake_case    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         UpperCAmelCase_										=					MultiControlNetModel([controlneta, controlneta]    )

         UpperCAmelCase_										=					{
             '''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       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Optional[Any]=0    ):
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )

         UpperCAmelCase_										=					2

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

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

         UpperCAmelCase_										=					10.0
         UpperCAmelCase_										=					4

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					steps
         UpperCAmelCase_										=					scale
         UpperCAmelCase_										=					pipe(**__snake_case    )[0]

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

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

         UpperCAmelCase_										=					self.get_dummy_inputs(__snake_case    )
         UpperCAmelCase_										=					steps
         UpperCAmelCase_										=					scale
         UpperCAmelCase_										=					pipe(**__snake_case ,   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       :      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   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_inference_batch_single_identical(expected_max_diff=2E-3    )




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



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

     '''simple docstring'''





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




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

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

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

         UpperCAmelCase_										=					pipe(
             __snake_case ,   __snake_case ,   control_image=__snake_case ,   generator=__snake_case ,   output_type='''np''' ,   num_inference_steps=50 ,   strength=0.6 ,   )

         UpperCAmelCase_										=					output.images[0]

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

         UpperCAmelCase_										=					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





