






'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(      UpperCamelCase_			,				unittest.TestCase ):
				a__					       =  KandinskyVaaInpaintPipeline
				a__					       =  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
				a__					       =  [
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				    '''mask_image''',
				]
				a__					       =  [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				a__					       =  False







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 3_2







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 3_2







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.time_input_dim







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.time_input_dim * 4







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 1_0_0







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )

								__magic_name__		:Dict        =      {
								    '''in_channels''': 9,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''image''',
								    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
								    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
								    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
								    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    '''layers_per_block''': 1,
								    '''encoder_hid_dim''': self.text_embedder_hidden_size,
								    '''encoder_hid_dim_type''': '''image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								__magic_name__		:List[Any]        =      UNetaDConditionModel(**__lowerCAmelCase     )
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Optional[Any]        =      VQModel(**self.dummy_movq_kwargs     )
								return model







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.dummy_unet
								__magic_name__		:str        =      self.dummy_movq

								__magic_name__		:List[Any]        =      DDIMScheduler(
								    num_train_timesteps=1_0_0_0       ,					beta_schedule='''linear'''       ,					beta_start=0.00085       ,					beta_end=0.012       ,					clip_sample=__lowerCAmelCase       ,					set_alpha_to_one=__lowerCAmelCase       ,					steps_offset=1       ,					prediction_type='''epsilon'''       ,					thresholding=__lowerCAmelCase       ,					)

								__magic_name__		:Any        =      {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=0     ):
								"""simple docstring"""
								__magic_name__		:str        =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
								__magic_name__		:str        =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1     )     ).to(
								    __lowerCAmelCase     )
								# create init_image
								__magic_name__		:Dict        =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
								__magic_name__		:List[str]        =      image.cpu().permute(0       ,					2       ,					3       ,					1     )[0]
								__magic_name__		:List[str]        =      Image.fromarray(np.uinta(__lowerCAmelCase     )     ).convert('''RGB'''     ).resize((2_5_6, 2_5_6)     )
								# create mask
								__magic_name__		:Tuple        =      np.ones((6_4, 6_4)       ,					dtype=np.floataa     )
								__magic_name__		:int        =      0

								if str(__lowerCAmelCase     ).startswith('''mps'''     ):
												__magic_name__		:int        =      torch.manual_seed(__lowerCAmelCase     )
								else:
												__magic_name__		:Optional[int]        =      torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
								__magic_name__		:List[str]        =      {
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      '''cpu'''

								__magic_name__		:Dict        =      self.get_dummy_components()

								__magic_name__		:Dict        =      self.pipeline_class(**__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      pipe.to(__lowerCAmelCase     )

								pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      pipe(**self.get_dummy_inputs(__lowerCAmelCase     )     )
								__magic_name__		:Tuple        =      output.images

								__magic_name__		:List[str]        =      pipe(
								    **self.get_dummy_inputs(__lowerCAmelCase     )       ,					return_dict=__lowerCAmelCase       ,					)[0]

								__magic_name__		:Tuple        =      image[0, -3:, -3:, -1]
								__magic_name__		:List[Any]        =      image_from_tuple[0, -3:, -3:, -1]

								print(F'''image.shape {image.shape}'''     )

								assert image.shape == (1, 6_4, 6_4, 3)

								__magic_name__		:int        =      np.array(
								    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]     )

								assert (
								    np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
								), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
								), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







				def 			A	(						self     ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







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







				def 			A	(						self     ):
								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''     )

								__magic_name__		:List[Any]        =      load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''     )
								__magic_name__		:int        =      np.ones((7_6_8, 7_6_8)       ,					dtype=np.floataa     )
								__magic_name__		:Dict        =      0

								__magic_name__		:Optional[int]        =      '''a hat'''

								__magic_name__		:Dict        =      KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''       ,					torch_dtype=torch.floataa     )
								pipe_prior.to(__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''       ,					torch_dtype=torch.floataa     )
								__magic_name__		:int        =      pipeline.to(__lowerCAmelCase     )
								pipeline.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:Dict        =      torch.Generator(device='''cpu'''     ).manual_seed(0     )
								__magic_name__		:List[str]        =      pipe_prior(
								    __lowerCAmelCase       ,					generator=__lowerCAmelCase       ,					num_inference_steps=5       ,					negative_prompt=''''''       ,					).to_tuple()

								__magic_name__		:List[Any]        =      pipeline(
								    image=__lowerCAmelCase       ,					mask_image=__lowerCAmelCase       ,					image_embeds=__lowerCAmelCase       ,					negative_image_embeds=__lowerCAmelCase       ,					generator=__lowerCAmelCase       ,					num_inference_steps=1_0_0       ,					height=7_6_8       ,					width=7_6_8       ,					output_type='''np'''       ,					)

								__magic_name__		:Tuple        =      output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(__lowerCAmelCase       ,					__lowerCAmelCase     )














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 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=[1_0, 2_0, 3_0, 4_0]       ,					__lowerCAmelCase=[1, 1, 2, 1]       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase="relu"       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      parent
								__magic_name__		:str        =      batch_size
								__magic_name__		:Union[str, Any]        =      image_size
								__magic_name__		:Optional[Any]        =      num_channels
								__magic_name__		:str        =      embeddings_size
								__magic_name__		:List[Any]        =      hidden_sizes
								__magic_name__		:List[str]        =      depths
								__magic_name__		:str        =      is_training
								__magic_name__		:List[Any]        =      use_labels
								__magic_name__		:int        =      hidden_act
								__magic_name__		:Dict        =      num_labels
								__magic_name__		:Any        =      scope
								__magic_name__		:Optional[int]        =      len(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

								__magic_name__		:int        =      None
								if self.use_labels:
												__magic_name__		:int        =      ids_tensor([self.batch_size]       ,					self.num_labels     )

								__magic_name__		:List[str]        =      self.get_config()

								return config, pixel_values, labels







				def 			A	(						self     ):
								"""simple docstring"""
								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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      RegNetModel(config=__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:Dict        =      model(__lowerCAmelCase     )
								# 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 // 3_2, self.image_size // 3_2)       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.num_labels
								__magic_name__		:str        =      RegNetForImageClassification(__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:str        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      config_and_inputs
								__magic_name__		:Optional[int]        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				a__					       =  (
				    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      RegNetModelTester(self     )
								__magic_name__		:Union[str, Any]        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								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 			A	(						self     ):
								"""simple docstring"""
								return







				@unittest.skip(reason='''RegNet does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@unittest.skip(reason='''RegNet does not support input and output embeddings'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Union[str, Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Dict        =      model_class(__lowerCAmelCase     )
												__magic_name__		:Any        =      inspect.signature(model.forward     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												__magic_name__		:Optional[Any]        =      [*signature.parameters.keys()]

												__magic_name__		:Any        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Tuple        =      model_class(config=__lowerCAmelCase     )
												for name, module in model.named_modules():
																if isinstance(__lowerCAmelCase       ,					(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 			A	(						self     ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Any        =      model_class(__lowerCAmelCase     )
												model.to(__lowerCAmelCase     )
												model.eval()

												with torch.no_grad():
																__magic_name__		:int        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

												__magic_name__		:Optional[int]        =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												__magic_name__		:List[str]        =      self.model_tester.num_stages
												self.assertEqual(len(__lowerCAmelCase     )       ,					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]       ,					)

								__magic_name__     ,				__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs_for_common()
								__magic_name__		:List[Any]        =      ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																__magic_name__		:Optional[Any]        =      layer_type
																__magic_name__		:List[str]        =      True
																check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__		:List[str]        =      True

																check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:Optional[int]        =      RegNetModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image

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







				@cached_property
				def 			A	(						self     ):
								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
								    if is_vision_available()
								    else None
								)







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(__lowerCAmelCase     )

								__magic_name__		:List[str]        =      self.default_image_processor
								__magic_name__		:Any        =      prepare_img()
								__magic_name__		:Optional[Any]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''pt'''     ).to(__lowerCAmelCase     )

								# forward pass
								with torch.no_grad():
												__magic_name__		:int        =      model(**__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:List[Any]        =      torch.Size((1, 1_0_0_0)     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:Any        =      torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(__lowerCAmelCase     )

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






