





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 						lowerCamelCase       (__lowerCamelCase							,     unittest.TestCase      ):







       """simple docstring"""




       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  A_     ( self     :					Tuple						)				->			Union[str, Any]:





              """simple docstring"""
              return 3_2






       @property
       def  A_     ( self     :					Union[str, Any]						)				->			Union[str, Any]:





              """simple docstring"""
              return 3_2






       @property
       def  A_     ( self     :					Tuple						)				->			Any:





              """simple docstring"""
              return self.time_input_dim






       @property
       def  A_     ( self     :					str						)				->			List[Any]:





              """simple docstring"""
              return self.time_input_dim * 4






       @property
       def  A_     ( self     :					List[Any]						)				->			Optional[int]:





              """simple docstring"""
              return 1_0_0






       @property
       def  A_     ( self     :					str						)				->			Optional[int]:





              """simple docstring"""
              torch.manual_seed(0						)

              SCREAMING_SNAKE_CASE__       :      List[str]     =						{
                  "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,
              }

              SCREAMING_SNAKE_CASE__       :      str     =						UNetaDConditionModel(**_UpperCAmelCase						)
              return model






       @property
       def  A_     ( self     :					Dict						)				->			List[Any]:





              """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     :					int						)				->			Dict:





              """simple docstring"""
              torch.manual_seed(0						)
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						VQModel(**self.dummy_movq_kwargs						)
              return model






       def  A_     ( self     :					Tuple						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      int     =						self.dummy_unet
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.dummy_movq

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						DDIMScheduler(
                  num_train_timesteps=1_0_0_0,			beta_schedule="linear",			beta_start=0.00085,			beta_end=0.012,			clip_sample=_UpperCAmelCase,			set_alpha_to_one=_UpperCAmelCase,			steps_offset=1,			prediction_type="epsilon",			thresholding=_UpperCAmelCase,			)

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						{
                  "unet": unet,
                  "scheduler": scheduler,
                  "movq": movq,
              }
              return components






       def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					int=0						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(seed + 1						)						).to(
                  _UpperCAmelCase						)
              if str(_UpperCAmelCase						).startswith("mps"						):
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.manual_seed(_UpperCAmelCase						)
              else:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{
                  "image_embeds": image_embeds,
                  "negative_image_embeds": negative_image_embeds,
                  "generator": generator,
                  "height": 6_4,
                  "width": 6_4,
                  "guidance_scale": 4.0,
                  "num_inference_steps": 2,
                  "output_type": "np",
              }
              return inputs






       def  A_     ( self     :					List[Any]						)				->			Tuple:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						"cpu"

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_dummy_components()

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.pipeline_class(**_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						pipe.to(_UpperCAmelCase						)

              pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      List[str]     =						pipe(**self.get_dummy_inputs(_UpperCAmelCase						)						)
              SCREAMING_SNAKE_CASE__       :      List[str]     =						output.images

              SCREAMING_SNAKE_CASE__       :      Tuple     =						pipe(
                  **self.get_dummy_inputs(_UpperCAmelCase						),			return_dict=_UpperCAmelCase,			)[0]

              SCREAMING_SNAKE_CASE__       :      Dict     =						image[0, -3:, -3:, -1]
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_from_tuple[0, -3:, -3:, -1]

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

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						np.array(
                  [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]						)

              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 						lowerCamelCase       (unittest.TestCase      ):







       """simple docstring"""




       def  A_     ( self     :					List[Any]						)				->			Union[str, Any]:





              """simple docstring"""
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def  A_     ( self     :					Optional[int]						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"						)

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						KandinskyVaaPriorPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-2-prior",			torch_dtype=torch.floataa						)
              pipe_prior.to(_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Any     =						KandinskyVaaPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-2-decoder",			torch_dtype=torch.floataa						)
              SCREAMING_SNAKE_CASE__       :      Any     =						pipeline.to(_UpperCAmelCase						)
              pipeline.set_progress_bar_config(disable=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      int     =						"red cat, 4k photo"

              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.Generator(device="cuda"						).manual_seed(0						)
              SCREAMING_SNAKE_CASE__       :      str     =						pipe_prior(
                  _UpperCAmelCase,			generator=_UpperCAmelCase,			num_inference_steps=5,			negative_prompt="",			).to_tuple()

              SCREAMING_SNAKE_CASE__       :      Any     =						torch.Generator(device="cuda"						).manual_seed(0						)
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						pipeline(
                  image_embeds=_UpperCAmelCase,			negative_image_embeds=_UpperCAmelCase,			generator=_UpperCAmelCase,			num_inference_steps=1_0_0,			output_type="np",			)

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						output.images[0]

              assert image.shape == (5_1_2, 5_1_2, 3)

              assert_mean_pixel_difference(_UpperCAmelCase,			_UpperCAmelCase						)













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 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      str     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
														SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						scope

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






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						None
														if self.use_labels:
																					SCREAMING_SNAKE_CASE__       :      int     =						ids_tensor([self.batch_size],			self.type_sequence_label_size						)

														SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

														return config, pixel_values, labels






							def  A_     ( self     :					int						)				->			Tuple:





														"""simple docstring"""
														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  A_     ( self     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						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
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






							def  A_     ( self     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
														return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							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  A_     ( self     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model_class(_UpperCAmelCase						)
																					self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_UpperCAmelCase,			nn.Linear						)						)






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Optional[int]						)				->			List[Any]:





														"""simple docstring"""
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"						) if is_vision_available() else None






							@slow
							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														torch.manual_seed(2						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

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






