





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							KandinskyVaaPipeline
					UpperCamelCase      =							[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					]
					UpperCamelCase      =							['''image_embeds''', '''negative_image_embeds''']
					UpperCamelCase      =							[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCamelCase      =							False


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return 32


					@property
					def 	lowerCamelCase       (					self			:int		):
												return 32


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												return self.time_input_dim


					@property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return self.time_input_dim * 4


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return 1_00


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												torch.manual_seed(0		)

												A		=  {
												    "in_channels": 4,
												    # 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,
												}

												A		=  UNetaDConditionModel(**__UpperCamelCase		)
												return model


					@property
					def 	lowerCamelCase       (					self			:List[str]		):
												return {
												    "block_out_channels": [32, 64],
												    "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": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


					@property
					def 	lowerCamelCase       (					self			:Any		):
												torch.manual_seed(0		)
												A		=  VQModel(**self.dummy_movq_kwargs		)
												return model


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.dummy_unet
												A		=  self.dummy_movq

												A		=  DDIMScheduler(
												    num_train_timesteps=10_00     ,       beta_schedule="linear"     ,       beta_start=0.00_085     ,       beta_end=0.012     ,       clip_sample=__UpperCamelCase     ,       set_alpha_to_one=__UpperCamelCase     ,       steps_offset=1     ,       prediction_type="epsilon"     ,       thresholding=__UpperCamelCase     ,       )

												A		=  {
												    "unet": unet,
												    "scheduler": scheduler,
												    "movq": movq,
												}
												return components


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]=0		):
												A		=  floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
												A		=  floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(seed + 1		)		).to(
												    __UpperCamelCase		)
												if str(__UpperCamelCase		).startswith("mps"		):
																			A		=  torch.manual_seed(__UpperCamelCase		)
												else:
																			A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
												A		=  {
												    "image_embeds": image_embeds,
												    "negative_image_embeds": negative_image_embeds,
												    "generator": generator,
												    "height": 64,
												    "width": 64,
												    "guidance_scale": 4.0,
												    "num_inference_steps": 2,
												    "output_type": "np",
												}
												return inputs


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  "cpu"

												A		=  self.get_dummy_components()

												A		=  self.pipeline_class(**__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)

												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  pipe(**self.get_dummy_inputs(__UpperCamelCase		)		)
												A		=  output.images

												A		=  pipe(
												    **self.get_dummy_inputs(__UpperCamelCase		)     ,       return_dict=__UpperCamelCase     ,       )[0]

												A		=  image[0, -3:, -3:, -1]
												A		=  image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												A		=  np.array(
												    [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]		)

												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()}"







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


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


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"		)

												A		=  KandinskyVaaPriorPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-prior"     ,       torch_dtype=torch.floataa		)
												pipe_prior.to(__UpperCamelCase		)

												A		=  KandinskyVaaPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-decoder"     ,       torch_dtype=torch.floataa		)
												A		=  pipeline.to(__UpperCamelCase		)
												pipeline.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "red cat, 4k photo"

												A		=  torch.Generator(device="cuda"		).manual_seed(0		)
												A,	A		=  pipe_prior(
												    __UpperCamelCase     ,       generator=__UpperCamelCase     ,       num_inference_steps=5     ,       negative_prompt=""     ,       ).to_tuple()

												A		=  torch.Generator(device="cuda"		).manual_seed(0		)
												A		=  pipeline(
												    image_embeds=__UpperCamelCase     ,       negative_image_embeds=__UpperCamelCase     ,       generator=__UpperCamelCase     ,       num_inference_steps=1_00     ,       output_type="np"     ,       )

												A		=  output.images[0]

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

												assert_mean_pixel_difference(__UpperCamelCase     ,       __UpperCamelCase		)









"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 					_UpperCAmelCase   :


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]=13     ,       __UpperCamelCase			:Any=30     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:Union[str, Any]=3     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:List[str]=32     ,       __UpperCamelCase			:List[Any]=5     ,       __UpperCamelCase			:Dict=4     ,       __UpperCamelCase			:List[str]=37     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Union[str, Any]=0.1     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Tuple=10     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:int=None     ,       ):
												A		=  parent
												A		=  batch_size
												A		=  image_size
												A		=  patch_size
												A		=  num_channels
												A		=  is_training
												A		=  use_labels
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  type_sequence_label_size
												A		=  initializer_range
												A		=  scope

												# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												A		=  (image_size // patch_size) ** 2
												A		=  num_patches + 1


					def 	lowerCamelCase       (					self			:Any		):
												A		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

												A		=  None
												if self.use_labels:
																			A		=  ids_tensor([self.batch_size]     ,       self.type_sequence_label_size		)

												A		=  self.get_config()

												return config, pixel_values, labels


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTMSNConfig(
												    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       initializer_range=self.initializer_range     ,       )


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Any		):
												A		=  ViTMSNModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  self.type_sequence_label_size
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       labels=__UpperCamelCase		)
												print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
												print("Labels: {labels}"		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

												# test greyscale images
												A		=  1
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()

												A		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  self.prepare_config_and_inputs()
												A,	A,	A		=  config_and_inputs
												A		=  {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					UpperCamelCase      =							(
					    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ViTMSNModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       has_text_modality=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Any		):
												self.config_tester.run_common_tests()


					@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												pass


					def 	lowerCamelCase       (					self			:int		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)		)
																			A		=  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase     ,       nn.Linear		)		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

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

																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  ViTMSNModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def 					A__				(			):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image






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


					@cached_property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None


					@slow
					def 	lowerCamelCase       (					self			:Any		):
												torch.manual_seed(2		)
												A		=  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(__UpperCamelCase		)

												A		=  self.default_image_processor
												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase     ,       return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

												# verify the logits
												A		=  torch.Size((1, 10_00)		)
												self.assertEqual(outputs.logits.shape     ,       __UpperCamelCase		)

												A		=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]		).to(__UpperCamelCase		)

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


