



'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
							from tests.trainer.test_trainer import (  # noqa
							    RegressionModelConfig,
							    RegressionPreTrainedModel,
							    get_regression_trainer,
							)


set_seed(42)

SCREAMING_SNAKE_CASE__     : Optional[Any]				=   get_tests_dir("""fixtures""")
SCREAMING_SNAKE_CASE__     : Any				=   os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE__     : List[str]				=   dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE__     : Any				=   """10999"""

SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """t5-small"""

# *** Working Models ***
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-albert"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-bert"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-bigbird_pegasus"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-big_bird"""
SCREAMING_SNAKE_CASE__     : int				=   """hf-internal-testing/tiny-random-blenderbot"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """bigscience/bigscience-small-testing"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-deberta"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-deberta-v2"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-distilbert-base-cased"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-electra"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-flaubert"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """stas/tiny-wmt19-en-de"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-funnel"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """sshleifer/tiny-gpt2"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-gptj"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """hf-internal-testing/tiny-random-gpt_neo"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """hf-internal-testing/tiny-layoutlm"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-led"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-longformer"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
SCREAMING_SNAKE_CASE__     : List[Any]				=   """sshleifer/tiny-marian-en-de"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-mbart"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """hf-internal-testing/tiny-random-mobilebert"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-mpnet"""
SCREAMING_SNAKE_CASE__     : int				=   """stas/pegasus-cnn_dailymail-tiny-random"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-prophetnet"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-distilroberta-base"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-squeezebert"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE__     : int				=   """hf-internal-testing/tiny-random-t5-v1.1"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-vit"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """hf-internal-testing/tiny-xlm-roberta"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-mt5"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-camembert"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-convbert"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-layoutlmv2"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-ctrl"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
SCREAMING_SNAKE_CASE__     : Tuple				=   """"""
# - "luke" Entities classes - no plan to make such example
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
SCREAMING_SNAKE_CASE__     : List[Any]				=   """hf-internal-testing/tiny-random-tapas"""







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE=False     ):
		# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
		# - it won't be able to handle that
		# 2. for now testing with just 2 gpus max (since some quality tests may give different
		# results with mode gpus because we use very little data)
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      min(2 ,		get_gpu_count()     ) if distributed else 1
		SCREAMING_SNAKE_CASE_						:Any										=      os.environ.get('DS_TEST_PORT' ,		SCREAMING_SNAKE_CASE     )
		return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      F'{FIXTURE_DIRECTORY}/tests_samples'
		SCREAMING_SNAKE_CASE_						:int										=      F'{data_dir_samples}/wmt_en_ro'
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      F'{data_dir_samples}/xsum'
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

		# try to cover as many models as possible once (it's enough to run on one task per model)
		# but need a tiny model for each
		#
		# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
		SCREAMING_SNAKE_CASE_						:Any										=      {
		    'trans': [
		        'bart',
		        'fsmt',
		        'm2m_100',
		        'marian',
		        'mbart',
		        't5',
		        't5_v1',
		        # "mt5", missing model files
		    ],
		    'sum': [
		        'pegasus',
		    ],
		    'clm': [
		        'big_bird',
		        'bigbird_pegasus',
		        'blenderbot',
		        'bloom',
		        'gpt2',
		        'gpt_neo',
		        'gptj',
		        'xlm-roberta',
		        'prophetnet',
		        # "camembert", missing model files
		    ],
		    'mlm': [
		        'albert',
		        'deberta',
		        'deberta-v2',
		        'distilbert',
		        'electra',
		        'flaubert',
		        'funnel',
		        'layoutlm',
		        # "reformer", # multiple issues with either mlm/qa/clas
		    ],
		    'qa': [
		        'led',
		        'longformer',
		        'mobilebert',
		        'mpnet',
		        'roberta',
		        'squeezebert',
		        # "convbert", # missing tokenizer files
		        # "layoutlmv2", missing model files
		    ],
		    'clas': [
		        'bert',
		        'xlnet',
		        # "hubert", # missing tokenizer files
		        # "ibert", # multiple issues with either mlm/qa/clas
		        # "transfo-xl", # tokenizer issues as ctrl
		        # "ctrl", # tokenizer issues
		        # "openai-gpt", missing model files
		        # "tapas", multiple issues
		    ],
		    'img_clas': [
		        'vit',
		    ],
		}

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      F'{ROOT_DIRECTORY}/examples/pytorch'

		SCREAMING_SNAKE_CASE_						:Any										=      {
		    'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
		    'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
		    'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
		    'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
		    'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
		    'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
		    'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
		}

		SCREAMING_SNAKE_CASE_						:Dict										=      get_launcher(distributed=SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:Tuple										=      {}
		for task, args in tasks.items():
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      args.split()
				for model in tasksamodels[task]:
						SCREAMING_SNAKE_CASE_						:Any										=      globals()[F'{model.upper().replace("-" ,		"_"     )}_TINY']
						SCREAMING_SNAKE_CASE_						:Any										=      F'--model_name_or_path {model_name}'.split()
						SCREAMING_SNAKE_CASE_						:Dict										=      launcher + args + args_model + args_main

						# # generation special case
						# if task == "gen":
						#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
						#     args_model += f"--model_type {model}".split()
						#     cmds[f"{task}_{model}"] = launcher + args + args_model
						# else:

		return cmds


SCREAMING_SNAKE_CASE__     : Tuple				=   make_task_cmds()

SCREAMING_SNAKE_CASE__     : List[str]				=   """zero2"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """zero3"""

SCREAMING_SNAKE_CASE__     : List[str]				=   [ZEROa, ZEROa]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE     ) for x in param.args     )     )
		return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					if task not in task_cmds:
							raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      task_cmds[task]
					SCREAMING_SNAKE_CASE_						:Any										=      f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

					SCREAMING_SNAKE_CASE_						:int										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'--output_dir {output_dir}'.split()

					cmd += args_ds + args_out

					return cmd, output_dir






			@parameterized.expand(SCREAMING_SNAKE_CASE      ,       name_func=SCREAMING_SNAKE_CASE	)
			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_task_cmd(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					# 1. generate the checkpoint
					cmd += "--save_steps 1".split()
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
					execute_subprocess_async(SCREAMING_SNAKE_CASE      ,       env=self.get_env()	)

					# 2. test that the fp32 weights get reconsolidated
					SCREAMING_SNAKE_CASE_						:Dict										=      f'{output_dir}/checkpoint-1'
					SCREAMING_SNAKE_CASE_						:str										=      f'{chkpt_dir}/out.bin'
					SCREAMING_SNAKE_CASE_						:str										=      f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
					subprocess.check_call(SCREAMING_SNAKE_CASE      ,       shell=SCREAMING_SNAKE_CASE	)
					assert os.path.exists(SCREAMING_SNAKE_CASE	), f'{recovered_model_path} was not found'

					# possibly could also test that the resulting saved model is usable but given that we use
					# random models we won't know if it's any good





"""simple docstring"""






# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class    __snake_case(					__A			,			__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset({'''control_image'''}       )  # skip `image` and `mask` for now, only test for control_image
    _A       = TEXT_TO_IMAGE_IMAGE_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDConditionModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=9    ,							out_channels=4    ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,							cross_attention_dim=32    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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        =			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    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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




    def   A   (   self    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

      _SCREAMING_SNAKE_CASE        =			2
      _SCREAMING_SNAKE_CASE        =			randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							)
      _SCREAMING_SNAKE_CASE        =			floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_			)			).to(A_			)
      _SCREAMING_SNAKE_CASE        =			init_image.cpu().permute(0    ,							2    ,							3    ,							1			)[0]

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''mask_image''': mask_image,
          '''control_image''': control_image,
      }

      return inputs




    def   A   (   self			):





      '''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   A   (   self			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




class    __snake_case(					__A       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset([]       )




    def   A   (   self			):





      '''simple docstring'''



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

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




class    __snake_case(					__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDConditionModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=9    ,							out_channels=4    ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,							cross_attention_dim=32    ,							)
      torch.manual_seed(0			)

      def init_weights(A_			):
        if isinstance(A_    ,							torch.nn.Convad			):
          torch.nn.init.normal(m.weight			)
          m.bias.data.fill_(1.0			)

      _SCREAMING_SNAKE_CASE        =			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(A_			)

      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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(A_			)

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

      _SCREAMING_SNAKE_CASE        =			MultiControlNetModel([controlneta, controlneta]			)

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




    def   A   (   self    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

      _SCREAMING_SNAKE_CASE        =			2

      _SCREAMING_SNAKE_CASE        =			[
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							),
      ]
      _SCREAMING_SNAKE_CASE        =			floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_			)			).to(A_			)
      _SCREAMING_SNAKE_CASE        =			init_image.cpu().permute(0    ,							2    ,							3    ,							1			)[0]

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''mask_image''': mask_image,
          '''control_image''': control_image,
      }

      return inputs




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			self.pipeline_class(**A_			)
      pipe.to(A_			)

      _SCREAMING_SNAKE_CASE        =			10.0
      _SCREAMING_SNAKE_CASE        =			4

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_			)[0]

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

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

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_    ,							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   A   (   self			):





      '''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   A   (   self			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



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




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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



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

      _SCREAMING_SNAKE_CASE        =			StableDiffusionControlNetInpaintPipeline.from_pretrained(
          '''runwayml/stable-diffusion-inpainting'''    ,							safety_checker=A_    ,							controlnet=A_			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device='''cpu'''			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''			).resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
          '''/stable_diffusion_inpaint/input_bench_mask.png'''			).resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			'''pitch black hole'''

      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''			).resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							generator=A_    ,							output_type='''np'''    ,							num_inference_steps=3    ,							)

      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''			)

      assert np.abs(expected_image - image			).max() < 9e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''			)

      _SCREAMING_SNAKE_CASE        =			StableDiffusionControlNetInpaintPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''    ,							safety_checker=A_    ,							controlnet=A_			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device='''cpu'''			).manual_seed(33			)

      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''			)
      _SCREAMING_SNAKE_CASE        =			init_image.resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''			)
      _SCREAMING_SNAKE_CASE        =			mask_image.resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			'''a handsome man with ray-ban sunglasses'''

      def make_inpaint_condition(A_    ,							A_			):
        _SCREAMING_SNAKE_CASE        =			np.array(image.convert('''RGB'''			)			).astype(np.floataa			) / 255.0
        _SCREAMING_SNAKE_CASE        =			np.array(image_mask.convert('''L'''			)			).astype(np.floataa			) / 255.0

        assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
        _SCREAMING_SNAKE_CASE        =			-1.0  # set as masked pixel
        _SCREAMING_SNAKE_CASE        =			np.expand_dims(A_    ,							0			).transpose(0    ,							3    ,							1    ,							2			)
        _SCREAMING_SNAKE_CASE        =			torch.from_numpy(A_			)
        return image

      _SCREAMING_SNAKE_CASE        =			make_inpaint_condition(A_    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							guidance_scale=9.0    ,							eta=1.0    ,							generator=A_    ,							num_inference_steps=20    ,							output_type='''np'''    ,							)
      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''			)

      assert np.abs(expected_image - image			).max() < 9e-2
