


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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__	(							__snake_case					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		KandinskyVaaControlnetPipeline
			_UpperCamelCase		:					Optional[int]    =		['image_embeds', 'negative_image_embeds', 'hint']
			_UpperCamelCase		:					int    =		['image_embeds', 'negative_image_embeds', 'hint']
			_UpperCamelCase		:					List[str]    =		[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			_UpperCamelCase		:					List[Any]    =		False




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 32




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 32




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.time_input_dim




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.time_input_dim * 4




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 100




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )

						UpperCamelCase__					=       {
						    "in_channels": 8,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "image_hint",
						    "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,
						}

						UpperCamelCase__					=       UNetaDConditionModel(**A_     )
						return model




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return {
						    "block_out_channels": [32, 32, 64, 64],
						    "down_block_types": [
						        "DownEncoderBlock2D",
						        "DownEncoderBlock2D",
						        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )
						UpperCamelCase__					=       VQModel(**self.dummy_movq_kwargs     )
						return model




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.dummy_unet
						UpperCamelCase__					=       self.dummy_movq

						UpperCamelCase__					=       DDIMScheduler(
						    num_train_timesteps=1000      ,						beta_schedule="linear"      ,						beta_start=0.00085      ,						beta_end=0.012      ,						clip_sample=A_      ,						set_alpha_to_one=A_      ,						steps_offset=1      ,						prediction_type="epsilon"      ,						thresholding=A_      ,						)

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=0     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor((1, self.text_embedder_hidden_size)      ,						rng=random.Random(A_     )     ).to(A_     )
						UpperCamelCase__					=       floats_tensor((1, self.text_embedder_hidden_size)      ,						rng=random.Random(seed + 1     )     ).to(
						    A_     )

						# create hint
						UpperCamelCase__					=       floats_tensor((1, 3, 64, 64)      ,						rng=random.Random(A_     )     ).to(A_     )

						if str(A_     ).startswith("mps"     ):
									UpperCamelCase__					=       torch.manual_seed(A_     )
						else:
									UpperCamelCase__					=       torch.Generator(device=A_     ).manual_seed(A_     )
						UpperCamelCase__					=       {
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "hint": hint,
						    "generator": generator,
						    "height": 64,
						    "width": 64,
						    "guidance_scale": 4.0,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}
						return inputs







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       "cpu"

						UpperCamelCase__					=       self.get_dummy_components()

						UpperCamelCase__					=       self.pipeline_class(**A_     )
						UpperCamelCase__					=       pipe.to(A_     )

						pipe.set_progress_bar_config(disable=A_     )

						UpperCamelCase__					=       pipe(**self.get_dummy_inputs(A_     )     )
						UpperCamelCase__					=       output.images

						UpperCamelCase__					=       pipe(
						    **self.get_dummy_inputs(A_     )      ,						return_dict=A_      ,						)[0]

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

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

						UpperCamelCase__					=       np.array(
						    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]     )

						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 				snake_case__				(							self     ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"     )

						UpperCamelCase__					=       load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/hint_image_cat.png"     )
						UpperCamelCase__					=       torch.from_numpy(np.array(A_     )     ).float() / 255.0
						UpperCamelCase__					=       hint.permute(2      ,						0      ,						1     ).unsqueeze(0     )

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

						UpperCamelCase__					=       KandinskyVaaControlnetPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-controlnet-depth"      ,						torch_dtype=torch.floataa     )
						UpperCamelCase__					=       pipeline.to(A_     )
						pipeline.set_progress_bar_config(disable=A_     )

						UpperCamelCase__					=       "A robot, 4k photo"

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

						UpperCamelCase__					=       torch.Generator(device="cuda"     ).manual_seed(0     )
						UpperCamelCase__					=       pipeline(
						    image_embeds=A_      ,						negative_image_embeds=A_      ,						hint=A_      ,						generator=A_      ,						num_inference_steps=100      ,						output_type="np"      ,						)

						UpperCamelCase__					=       output.images[0]

						assert image.shape == (512, 512, 3)

						assert_mean_pixel_difference(A_      ,						A_     )






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    VideoMAEForPreTraining,
						    VideoMAEForVideoClassification,
						    VideoMAEModel,
						)
						from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=10      ,						snake_case=3      ,						snake_case=2      ,						snake_case=2      ,						snake_case=2      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						snake_case=0.9      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       patch_size
						UpperCamelCase__					=       tubelet_size
						UpperCamelCase__					=       num_frames
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       type_sequence_label_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       mask_ratio
						UpperCamelCase__					=       scope

						# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
						UpperCamelCase__					=       (image_size // patch_size) ** 2
						UpperCamelCase__					=       (num_frames // tubelet_size) * self.num_patches_per_frame

						# use this variable to define bool_masked_pos
						UpperCamelCase__					=       int(mask_ratio * self.seq_length     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor(
						    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

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

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return VideoMAEConfig(
						    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						num_frames=self.num_frames      ,						tubelet_size=self.tubelet_size      ,						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=snake_case      ,						initializer_range=self.initializer_range      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining(snake_case     )
						model.to(snake_case     )
						model.eval()
						# important: each video needs to have the same number of masked patches
						# hence we define a single mask, which we then repeat for each example in the batch
						UpperCamelCase__					=       torch.ones((self.num_masks,)     )
						UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0     )     )]     )
						UpperCamelCase__					=       mask.expand(self.batch_size      ,						-1     ).bool()

						UpperCamelCase__					=       model(snake_case      ,						snake_case     )
						# model only returns predictions for masked patches
						UpperCamelCase__					=       mask.sum().item()
						UpperCamelCase__					=       3 * self.tubelet_size * self.patch_size**2
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, num_masked_patches, decoder_num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		(
			    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
			)
			_UpperCamelCase		:					Union[str, Any]    =		(
			    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					int    =		False
			_UpperCamelCase		:					Any    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       copy.deepcopy(snake_case     )

						if model_class == VideoMAEForPreTraining:
									# important: each video needs to have the same number of masked patches
									# hence we define a single mask, which we then repeat for each example in the batch
									UpperCamelCase__					=       torch.ones((self.model_tester.num_masks,)     )
									UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0     )     )]     )
									UpperCamelCase__					=       mask.expand(self.model_tester.batch_size      ,						-1     ).bool()
									UpperCamelCase__					=       bool_masked_pos.to(snake_case     )

						if return_labels:
									if model_class in [
									    *get_values(snake_case     ),
									]:
												UpperCamelCase__					=       torch.zeros(
												    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=snake_case     )

						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="VideoMAE does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       VideoMAEModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if not self.has_attentions:
									pass

						else:
									UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase__					=       True

									for model_class in self.all_model_classes:
												UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
												UpperCamelCase__					=       (
												    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
												)

												UpperCamelCase__					=       True
												UpperCamelCase__					=       False
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)
												UpperCamelCase__					=       len(snake_case     )

												# Check attention is always last and order is fine
												UpperCamelCase__					=       True
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

												self.assertEqual(out_len + 1      ,						len(snake_case     )     )

												UpperCamelCase__					=       outputs.attentions

												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states
									UpperCamelCase__					=       self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(snake_case     )      ,						snake_case     )

									UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
									UpperCamelCase__					=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )      ,						[seq_length, self.model_tester.hidden_size]      ,						)

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       True
									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass



def        UpperCamelCase_(				)->		Union[str, Any]:
			UpperCamelCase__					=       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"				,     filename="eating_spaghetti.npy"				,     repo_type="dataset"  )
			UpperCamelCase__					=       np.load(_A  )
			return list(_A  )







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,						image_std=[0.5, 0.5, 0.5]     )
						    if is_vision_available()
						    else None
						)




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"     ).to(
						    snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 400)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([0.3669, -0.0688, -0.2421]     ).to(snake_case     )

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







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# add boolean mask, indicating which patches to mask
						UpperCamelCase__					=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"      ,						filename="bool_masked_pos.pt"     )
						UpperCamelCase__					=       torch.load(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size([1, 1408, 1536]     )
						UpperCamelCase__					=       torch.tensor(
						    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]      ,						device=snake_case     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `True`)
						UpperCamelCase__					=       torch.tensor([0.5142]      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `False`)
						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"      ,						norm_pix_loss=snake_case     ).to(
						    snake_case     )

						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						UpperCamelCase__					=       torch.tensor(torch.tensor([0.6469]     )      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )


