




'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class       __lowercase					(__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  UnCLIPImageVariationPipeline
	_lowerCamelCase              =  IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
	_lowerCamelCase              =  IMAGE_VARIATION_BATCH_PARAMS

	_lowerCamelCase              =  [
	    '''generator''',
	    '''return_dict''',
	    '''decoder_num_inference_steps''',
	    '''super_res_num_inference_steps''',
	]
	_lowerCamelCase              =  False

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						int):
			return self.time_input_dim

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self.time_input_dim * 4

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return 100

	@property
	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Any					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
			return tokenizer

	@property
	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=self.text_embedder_hidden_size  ,					projection_dim=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1_000  ,					)
			return CLIPTextModelWithProjection(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					Tuple					=					CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size  ,					projection_dim=self.text_embedder_hidden_size  ,					num_hidden_layers=5  ,					num_attention_heads=4  ,					image_size=32  ,					intermediate_size=37  ,					patch_size=1  ,					)
			return CLIPVisionModelWithProjection(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)

			UpperCamelCase__				:					Dict					=					{
			    'clip_embeddings_dim': self.text_embedder_hidden_size,
			    'time_embed_dim': self.time_embed_dim,
			    'cross_attention_dim': self.cross_attention_dim,
			}

			UpperCamelCase__				:					int					=					UnCLIPTextProjModel(**UpperCAmelCase_)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)

			UpperCamelCase__				:					str					=					{
			    'sample_size': 32,
			    # RGB in channels
			    'in_channels': 3,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 6,
			    '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,
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': 'identity',
			}

			UpperCamelCase__				:					Optional[Any]					=					UNetaDConditionModel(**UpperCAmelCase_)
			return model

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			return {
			    "sample_size": 64,
			    "layers_per_block": 1,
			    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
			    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
			    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
			    "in_channels": 6,
			    "out_channels": 3,
			}

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

			UpperCamelCase__				:					Any					=					UNetaDModel(**self.dummy_super_res_kwargs)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			# seeded differently to get different unet than `self.dummy_super_res_first`
			torch.manual_seed(1)

			UpperCamelCase__				:					Any					=					UNetaDModel(**self.dummy_super_res_kwargs)
			return model

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.dummy_decoder
			UpperCamelCase__				:					Dict					=					self.dummy_text_proj
			UpperCamelCase__				:					int					=					self.dummy_text_encoder
			UpperCamelCase__				:					Tuple					=					self.dummy_tokenizer
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_super_res_first
			UpperCamelCase__				:					List[str]					=					self.dummy_super_res_last

			UpperCamelCase__				:					Optional[Any]					=					UnCLIPScheduler(
			    variance_type='learned_range'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1_000  ,					)

			UpperCamelCase__				:					Tuple					=					UnCLIPScheduler(
			    variance_type='fixed_small_log'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1_000  ,					)

			UpperCamelCase__				:					List[Any]					=					CLIPImageProcessor(crop_size=32  ,					size=32)

			UpperCamelCase__				:					Optional[Any]					=					self.dummy_image_encoder

			return {
			    "decoder": decoder,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "text_proj": text_proj,
			    "feature_extractor": feature_extractor,
			    "image_encoder": image_encoder,
			    "super_res_first": super_res_first,
			    "super_res_last": super_res_last,
			    "decoder_scheduler": decoder_scheduler,
			    "super_res_scheduler": super_res_scheduler,
			}

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Union[str, Any]=0  ,					UpperCAmelCase_   :						Dict=True):
			UpperCamelCase__				:					Optional[int]					=					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
			if str(UpperCAmelCase_).startswith('mps'):
					UpperCamelCase__				:					Optional[int]					=					torch.manual_seed(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					str					=					torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)

			if pil_image:
					UpperCamelCase__				:					List[Any]					=					input_image * 0.5 + 0.5
					UpperCamelCase__				:					str					=					input_image.clamp(0  ,					1)
					UpperCamelCase__				:					Optional[Any]					=					input_image.cpu().permute(0  ,					2  ,					3  ,					1).float().numpy()
					UpperCamelCase__				:					int					=					DiffusionPipeline.numpy_to_pil(UpperCAmelCase_)[0]

			return {
			    "image": input_image,
			    "generator": generator,
			    "decoder_num_inference_steps": 2,
			    "super_res_num_inference_steps": 2,
			    "output_type": "np",
			}

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Any					=					'cpu'

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

			UpperCamelCase__				:					Dict					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					int					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					int					=					output.images

			UpperCamelCase__				:					Dict					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

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

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

			UpperCamelCase__				:					int					=					np.array(
			    [
			        0.99_97,
			        0.00_02,
			        0.99_97,
			        0.99_97,
			        0.99_69,
			        0.00_23,
			        0.99_97,
			        0.99_69,
			        0.99_70,
			    ])

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Tuple					=					'cpu'

			UpperCamelCase__				:					Dict					=					self.get_dummy_components()

			UpperCamelCase__				:					Any					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					output.images

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					image_from_tuple[0, -3:, -3:, -1]

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

			UpperCamelCase__				:					Dict					=					np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71])

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					int					=					'cpu'

			UpperCamelCase__				:					int					=					self.get_dummy_components()

			UpperCamelCase__				:					Tuple					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					[
			    pipeline_inputs['image'],
			    pipeline_inputs['image'],
			]

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					output.images

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					[
			    tuple_pipeline_inputs['image'],
			    tuple_pipeline_inputs['image'],
			]

			UpperCamelCase__				:					str					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Optional[int]					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (2, 64, 64, 3)

			UpperCamelCase__				:					Any					=					np.array(
			    [
			        0.99_97,
			        0.99_89,
			        0.00_08,
			        0.00_21,
			        0.99_60,
			        0.00_18,
			        0.00_14,
			        0.00_02,
			        0.99_33,
			    ])

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[Any]					=					torch.device('cpu')







			class       __lowercase					:
				_lowerCamelCase              =  1


			UpperCamelCase__				:					List[Any]					=					self.get_dummy_components()

			UpperCamelCase__				:					Dict					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					str					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					pipe.decoder.dtype
			UpperCamelCase__				:					Optional[int]					=					1

			UpperCamelCase__				:					Optional[Any]					=					(
			    batch_size,
			    pipe.decoder.config.in_channels,
			    pipe.decoder.config.sample_size,
			    pipe.decoder.config.sample_size,
			)
			UpperCamelCase__				:					Optional[int]					=					pipe.prepare_latents(
			    UpperCAmelCase_  ,					dtype=UpperCAmelCase_  ,					device=UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					latents=UpperCAmelCase_  ,					scheduler=DummyScheduler())

			UpperCamelCase__				:					Any					=					(
			    batch_size,
			    pipe.super_res_first.config.in_channels // 2,
			    pipe.super_res_first.config.sample_size,
			    pipe.super_res_first.config.sample_size,
			)
			UpperCamelCase__				:					str					=					pipe.prepare_latents(
			    UpperCAmelCase_  ,					dtype=UpperCAmelCase_  ,					device=UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					latents=UpperCAmelCase_  ,					scheduler=DummyScheduler())

			UpperCamelCase__				:					List[str]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					pipe(
			    **UpperCAmelCase_  ,					decoder_latents=UpperCAmelCase_  ,					super_res_latents=UpperCAmelCase_).images

			UpperCamelCase__				:					int					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			# Don't pass image, instead pass embedding
			UpperCamelCase__				:					Dict					=					pipeline_inputs.pop('image')
			UpperCamelCase__				:					int					=					pipe.image_encoder(UpperCAmelCase_).image_embeds

			UpperCamelCase__				:					int					=					pipe(
			    **UpperCAmelCase_  ,					decoder_latents=UpperCAmelCase_  ,					super_res_latents=UpperCAmelCase_  ,					image_embeddings=UpperCAmelCase_  ,					).images

			# make sure passing text embeddings manually is identical
			assert np.abs(img_out_a - img_out_a).max() < 1e-4

	@skip_mps
	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[int]					=					torch_device == 'cpu'

			# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
			UpperCamelCase__				:					Optional[Any]					=					1e-2

			self._test_attention_slicing_forward_pass(
			    test_max_difference=UpperCAmelCase_  ,					expected_max_diff=UpperCAmelCase_)

	@skip_mps
	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					torch_device == 'cpu'
			UpperCamelCase__				:					Dict					=					True
			UpperCamelCase__				:					Optional[int]					=					[
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]

			self._test_inference_batch_single_identical(
			    test_max_difference=UpperCAmelCase_  ,					relax_max_difference=UpperCAmelCase_  ,					additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,					)

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[Any]					=					[
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]

			if torch_device == "mps":
					# TODO: MPS errors with larger batch sizes
					UpperCamelCase__				:					Tuple					=					[2, 3]
					self._test_inference_batch_consistent(
					    batch_sizes=UpperCAmelCase_  ,					additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,					)
			else:
					self._test_inference_batch_consistent(
					    additional_params_copy_to_batched_inputs=UpperCAmelCase_)

	@skip_mps
	def      __UpperCamelCase							(      self   :						List[Any]):
			return super().test_dict_tuple_outputs_equivalent()

	@skip_mps
	def      __UpperCamelCase							(      self   :						str):
			return super().test_save_load_local()





	@skip_mps
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			return super().test_save_load_optional_components()







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

	def      __UpperCamelCase							(      self   :						Optional[int]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					Optional[int]					=					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
			UpperCamelCase__				:					Dict					=					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy')

			UpperCamelCase__				:					Any					=					UnCLIPImageVariationPipeline.from_pretrained(
			    'kakaobrain/karlo-v1-alpha-image-variations'  ,					torch_dtype=torch.floataa)
			UpperCamelCase__				:					Dict					=					pipeline.to(UpperCAmelCase_)
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device='cpu').manual_seed(0)
			UpperCamelCase__				:					Optional[int]					=					pipeline(
			    UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					)

			UpperCamelCase__				:					List[Any]					=					output.images[0]

			assert image.shape == (256, 256, 3)

			assert_mean_pixel_difference(UpperCAmelCase_  ,					UpperCAmelCase_  ,					15)












'''simple docstring'''




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Optional[int]=13  ,					UpperCAmelCase_   :						Tuple=30  ,					UpperCAmelCase_   :						Dict=2  ,					UpperCAmelCase_   :						Dict=3  ,					UpperCAmelCase_   :						Tuple=True  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						Tuple=32  ,					UpperCAmelCase_   :						List[str]=5  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						Optional[int]=37  ,					UpperCAmelCase_   :						str="gelu"  ,					UpperCAmelCase_   :						List[str]=0.1  ,					UpperCAmelCase_   :						Dict=0.1  ,					UpperCAmelCase_   :						Dict=10  ,					UpperCAmelCase_   :						Optional[int]=0.02  ,					UpperCAmelCase_   :						Union[str, Any]=3  ,					UpperCAmelCase_   :						Any=0.6  ,					UpperCAmelCase_   :						Dict=None  ,					):
			UpperCamelCase__				:					Tuple					=					parent
			UpperCamelCase__				:					List[str]					=					batch_size
			UpperCamelCase__				:					Optional[Any]					=					image_size
			UpperCamelCase__				:					Optional[Any]					=					patch_size
			UpperCamelCase__				:					List[str]					=					num_channels
			UpperCamelCase__				:					Union[str, Any]					=					is_training
			UpperCamelCase__				:					int					=					use_labels
			UpperCamelCase__				:					Optional[int]					=					hidden_size
			UpperCamelCase__				:					Any					=					num_hidden_layers
			UpperCamelCase__				:					str					=					num_attention_heads
			UpperCamelCase__				:					str					=					intermediate_size
			UpperCamelCase__				:					Union[str, Any]					=					hidden_act
			UpperCamelCase__				:					Optional[int]					=					hidden_dropout_prob
			UpperCamelCase__				:					Tuple					=					attention_probs_dropout_prob
			UpperCamelCase__				:					Any					=					type_sequence_label_size
			UpperCamelCase__				:					int					=					initializer_range
			UpperCamelCase__				:					Optional[int]					=					mask_ratio
			UpperCamelCase__				:					int					=					scope

			# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
			# (we add 1 for the [CLS] token)
			UpperCamelCase__				:					str					=					(image_size // patch_size) ** 2
			UpperCamelCase__				:					Dict					=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)))

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

			UpperCamelCase__				:					Any					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[Any]):
			return ViTMAEConfig(
			    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  ,					is_decoder=UpperCAmelCase_  ,					initializer_range=self.initializer_range  ,					mask_ratio=self.mask_ratio  ,					)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					ViTMAEModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Optional[int]					=					model(UpperCAmelCase_)
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size))

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Dict					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					(self.image_size // self.patch_size) ** 2
			UpperCamelCase__				:					Optional[int]					=					self.patch_size**2 * self.num_channels
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))

			# test greyscale images
			UpperCamelCase__				:					List[Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					List[str]					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			UpperCamelCase__				:					Union[str, Any]					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.patch_size**2
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[str]					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					config_and_inputs
			UpperCamelCase__				:					Any					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCamelCase              =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[str]					=					ViTMAEModelTester(self)
			UpperCamelCase__				:					Any					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37)

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

	@unittest.skip(reason='ViTMAE does not use inputs_embeds')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Optional[int]					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			# make masks reproducible
			np.random.seed(2)

			UpperCamelCase__				:					str					=					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
			UpperCamelCase__				:					Tuple					=					np.random.uniform(size=(self.model_tester.batch_size, num_patches))
			UpperCamelCase__				:					Optional[Any]					=					torch.from_numpy(UpperCAmelCase_)

			# Add `noise` argument.
			# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
			UpperCamelCase__				:					List[str]					=					pt_noise

			super().check_pt_tf_models(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Optional[Any]					=					model_class(UpperCAmelCase_)
					model.to(UpperCAmelCase_)
					model.eval()
					# make random mask reproducible
					torch.manual_seed(2)
					with torch.no_grad():
							UpperCamelCase__				:					Tuple					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

					UpperCamelCase__				:					Dict					=					outputs[0].cpu().numpy()
					UpperCamelCase__				:					Optional[int]					=					0

					with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(UpperCAmelCase_)
							UpperCamelCase__				:					str					=					model_class.from_pretrained(UpperCAmelCase_)
							model.to(UpperCAmelCase_)
							# make random mask reproducible
							torch.manual_seed(2)
							with torch.no_grad():
									UpperCamelCase__				:					List[str]					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

							# Make sure we don't have nans
							UpperCamelCase__				:					Tuple					=					after_outputs[0].cpu().numpy()
							UpperCamelCase__				:					Any					=					0
							UpperCamelCase__				:					Union[str, Any]					=					np.amax(np.abs(out_a - out_a))
							self.assertLessEqual(UpperCAmelCase_  ,					1e-5)

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass





	@slow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Tuple					=					ViTMAEModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)







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







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

	@cached_property
	def      __UpperCamelCase							(      self   :						int):
			return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None





	@slow
	def      __UpperCamelCase							(      self   :						str):
			# make random mask reproducible across the PT and TF model
			np.random.seed(2)

			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.default_image_processor
			UpperCamelCase__				:					Dict					=					prepare_img()
			UpperCamelCase__				:					Optional[int]					=					image_processor(images=UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# prepare a noise vector that will be also used for testing the TF model
			# (this way we can ensure that the PT and TF models operate on the same inputs)
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEConfig()
			UpperCamelCase__				:					int					=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
			UpperCamelCase__				:					Any					=					np.random.uniform(size=(1, num_patches))

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Dict					=					model(**UpperCAmelCase_  ,					noise=torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_))

			# verify the logits
			UpperCamelCase__				:					Tuple					=					torch.Size((1, 196, 768))
			self.assertEqual(outputs.logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					torch.tensor(
			    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

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




