



'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class       __magic_name__		:







							def __init__(				self   :			Dict      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			List[Any]=3      ,_UpperCAmelCase   :			Tuple=32      ,_UpperCAmelCase   :			List[Any]=3      ,_UpperCAmelCase   :			List[str]=10      ,_UpperCAmelCase   :			Optional[int]=[10, 20, 30, 40]      ,_UpperCAmelCase   :			int=[1, 1, 2, 1]      ,_UpperCAmelCase   :			Any=True      ,_UpperCAmelCase   :			str=True      ,_UpperCAmelCase   :			Optional[Any]="relu"      ,_UpperCAmelCase   :			List[Any]=3      ,_UpperCAmelCase   :			Tuple=None      ,):
									_a	: int								=				parent
									_a	: int								=				batch_size
									_a	: Any								=				image_size
									_a	: str								=				num_channels
									_a	: Dict								=				embeddings_size
									_a	: Optional[Any]								=				hidden_sizes
									_a	: Optional[int]								=				depths
									_a	: List[str]								=				is_training
									_a	: str								=				use_labels
									_a	: Tuple								=				hidden_act
									_a	: Optional[Any]								=				num_labels
									_a	: Optional[Any]								=				scope
									_a	: int								=				len(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Optional[int]								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_a	: Any								=				None
									if self.use_labels:
											_a	: Dict								=				ids_tensor([self.batch_size]      ,self.num_labels						)

									_a	: List[Any]								=				self.get_config()

									return config, pixel_values, labels







							def 				__lowercase     (				self   :			str						):
									return RegNetConfig(
									    num_channels=self.num_channels      ,embeddings_size=self.embeddings_size      ,hidden_sizes=self.hidden_sizes      ,depths=self.depths      ,hidden_act=self.hidden_act      ,num_labels=self.num_labels      ,)







							def 				__lowercase     (				self   :			str      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Tuple						):
									_a	: int								=				RegNetModel(config=_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									_a	: List[Any]								=				model(_UpperCAmelCase						)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,)







							def 				__lowercase     (				self   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Optional[int]      ,_UpperCAmelCase   :			int						):
									_a	: str								=				self.num_labels
									_a	: Optional[Any]								=				RegNetForImageClassification(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									_a	: Tuple								=				model(_UpperCAmelCase      ,labels=_UpperCAmelCase						)
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				self.prepare_config_and_inputs()
									_a			,   _a			,   _a	: str								=				config_and_inputs
									_a	: Union[str, Any]								=				{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       int  							=   (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							lowerCAmelCase			:       Dict  							=   (
							    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase			:       int  							=   False
							lowerCAmelCase			:       Optional[int]  							=   False
							lowerCAmelCase			:       int  							=   False
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			int						):
									_a	: List[Any]								=				RegNetModelTester(self						)
									_a	: Any								=				ConfigTester(self      ,config_class=_UpperCAmelCase      ,has_text_modality=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







							def 				__lowercase     (				self   :			Any						):
									return







							@unittest.skip(reason='RegNet does not use inputs_embeds'						)
							def 				__lowercase     (				self   :			str						):
									pass







							@unittest.skip(reason='RegNet does not support input and output embeddings'						)
							def 				__lowercase     (				self   :			Optional[Any]						):
									pass







							def 				__lowercase     (				self   :			str						):
									_a			,   _a	: Optional[int]								=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a	: Tuple								=				model_class(_UpperCAmelCase						)
											_a	: int								=				inspect.signature(model.forward						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a	: int								=				[*signature.parameters.keys()]

											_a	: Optional[Any]								=				['pixel_values']
											self.assertListEqual(arg_names[:1]      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Any								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a			,   _a	: List[str]								=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a	: str								=				model_class(config=_UpperCAmelCase						)
											for name, module in model.named_modules():
													if isinstance(_UpperCAmelCase      ,(nn.BatchNormad, nn.GroupNorm)						):
															self.assertTrue(
															    torch.all(module.weight == 1						)      ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)
															self.assertTrue(
															    torch.all(module.bias == 0						)      ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)







							def 				__lowercase     (				self   :			Optional[Any]						):
									def check_hidden_states_output(_UpperCAmelCase   :			Tuple      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Dict						):
											_a	: List[Any]								=				model_class(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()

											with torch.no_grad():
													_a	: List[str]								=				model(**self._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase						)						)

											_a	: str								=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											_a	: Optional[int]								=				self.model_tester.num_stages
											self.assertEqual(len(_UpperCAmelCase						)      ,expected_num_stages + 1						)

											# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)      ,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,)

									_a			,   _a	: Dict								=				self.model_tester.prepare_config_and_inputs_for_common()
									_a	: Tuple								=				['basic', 'bottleneck']
									for model_class in self.all_model_classes:
											for layer_type in layers_type:
													_a	: Any								=				layer_type
													_a	: Dict								=				True
													check_hidden_states_output(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_a	: Optional[int]								=				True

													check_hidden_states_output(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Optional[int]								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a	: Optional[int]								=				RegNetModel.from_pretrained(_UpperCAmelCase						)
											self.assertIsNotNone(_UpperCAmelCase						)



def 	__lowerCamelCase	( )     -> Optional[int]:
		_a	: Optional[Any]								=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
		return image

@require_torch
@require_vision
class       __magic_name__		(       unittest.TestCase  ):







							@cached_property
							def 				__lowercase     (				self   :			List[Any]						):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
									    if is_vision_available()
									    else None
									)







							@slow
							def 				__lowercase     (				self   :			List[Any]						):
									_a	: List[str]								=				RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_UpperCAmelCase						)

									_a	: Dict								=				self.default_image_processor
									_a	: Dict								=				prepare_img()
									_a	: Union[str, Any]								=				image_processor(images=_UpperCAmelCase      ,return_tensors='pt'						).to(_UpperCAmelCase						)

									# forward pass
									with torch.no_grad():
											_a	: int								=				model(**_UpperCAmelCase						)

									# verify the logits
									_a	: Any								=				torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape      ,_UpperCAmelCase						)

									_a	: Union[str, Any]								=				torch.tensor([-0.41_80, -1.50_51, -3.48_36]						).to(_UpperCAmelCase						)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)









'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				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      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, 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      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




