"""simple docstring"""
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class        __snake_case	(					unittest.TestCase    ):
      a__				=		JukeboxTokenizer
      a__				=		{
          """artist""": """Zac Brown Band""",
          """genres""": """Country""",
          """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
      }



      @require_torch
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        import torch

        a__:					str      							=     JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics')
        a__:					Tuple      							=     tokenizer(**self.metas)['input_ids']
        # fmt: off
        a__:					Tuple      							=     [
            torch.tensor([[
                0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                76, 76]]),
            torch.tensor([[0, 0, 0, 10_69, 11]]),
            torch.tensor([[0, 0, 0, 10_69, 11]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0]   ,      EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1]   ,      EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2]   ,      EXPECTED_OUTPUT[2]))



      @require_torch
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        import torch

        a__:					Optional[Any]      							=     JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics')
        a__:					Tuple      							=     tokenizer(**self.metas)['input_ids']
        # fmt: off
        a__:					Tuple      							=     [
            torch.tensor([[
                0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                77, 77, 77, 77, 77, 77]]),
            torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
            torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0]   ,      EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1]   ,      EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2]   ,      EXPECTED_OUTPUT[2]))



"""simple docstring"""
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        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=13   ,      lowercase=10   ,      lowercase=3   ,      lowercase=2   ,      lowercase=2   ,      lowercase=2   ,      lowercase=True   ,      lowercase=True   ,      lowercase=32   ,      lowercase=5   ,      lowercase=4   ,      lowercase=37   ,      lowercase="gelu"   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=10   ,      lowercase=0.02   ,      lowercase=0.9   ,      lowercase=None   ,      )     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     parent
        a__:					int      							=     batch_size
        a__:					int      							=     image_size
        a__:					Optional[int]      							=     num_channels
        a__:					List[str]      							=     patch_size
        a__:					List[str]      							=     tubelet_size
        a__:					Any      							=     num_frames
        a__:					Any      							=     is_training
        a__:					Dict      							=     use_labels
        a__:					Optional[Any]      							=     hidden_size
        a__:					Optional[int]      							=     num_hidden_layers
        a__:					Optional[Any]      							=     num_attention_heads
        a__:					Optional[Any]      							=     intermediate_size
        a__:					Any      							=     hidden_act
        a__:					Dict      							=     hidden_dropout_prob
        a__:					Union[str, Any]      							=     attention_probs_dropout_prob
        a__:					List[Any]      							=     type_sequence_label_size
        a__:					Optional[Any]      							=     initializer_range
        a__:					List[str]      							=     mask_ratio
        a__:					Union[str, Any]      							=     scope

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

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



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

        a__:					Any      							=     None
        if self.use_labels:
          a__:					Tuple      							=     ids_tensor([self.batch_size]   ,      self.type_sequence_label_size)

        a__:					Optional[int]      							=     self.get_config()

        return config, pixel_values, labels



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





        '''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=lowercase   ,      initializer_range=self.initializer_range   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Any      							=     VideoMAEModel(config=lowercase)
        model.to(lowercase)
        model.eval()
        a__:					Optional[Any]      							=     model(lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size))



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					List[str]      							=     VideoMAEForPreTraining(lowercase)
        model.to(lowercase)
        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
        a__:					int      							=     torch.ones((self.num_masks,))
        a__:					Any      							=     torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
        a__:					int      							=     mask.expand(self.batch_size   ,      -1).bool()

        a__:					Union[str, Any]      							=     model(lowercase   ,      lowercase)
        # model only returns predictions for masked patches
        a__:					List[str]      							=     mask.sum().item()
        a__:					str      							=     3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, num_masked_patches, decoder_num_labels))



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					Dict      							=     config_and_inputs
        a__:					Any      							=     {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(
          (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
      )
      a__				=		(
          {"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
          if is_torch_available()
          else {}
      )

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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     VideoMAEModelTester(self)
        a__:					str      							=     ConfigTester(self   ,      config_class=lowercase   ,      has_text_modality=lowercase   ,      hidden_size=37)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase=False)     ->      Any:





        '''simple docstring'''





        a__:					Optional[int]      							=     copy.deepcopy(lowercase)

        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
          a__:					List[Any]      							=     torch.ones((self.model_tester.num_masks,))
          a__:					List[Any]      							=     torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
          a__:					Optional[int]      							=     mask.expand(self.model_tester.batch_size   ,      -1).bool()
          a__:					Union[str, Any]      							=     bool_masked_pos.to(lowercase)

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

        return inputs_dict



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      @unittest.skip(reason='VideoMAE does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__							,   a__:					Any      							=     self.model_tester.prepare_config_and_inputs_for_common()

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



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__							,   a__:					Union[str, Any]      							=     self.model_tester.prepare_config_and_inputs_for_common()

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

          a__:					Tuple      							=     ['pixel_values']
          self.assertListEqual(arg_names[:1]   ,      lowercase)



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





        '''simple docstring'''





        a__:					Dict      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					int      							=     VideoMAEModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



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





        '''simple docstring'''





        if not self.has_attentions:
          pass

        else:
          a__							,   a__:					Any      							=     self.model_tester.prepare_config_and_inputs_for_common()
          a__:					str      							=     True

          for model_class in self.all_model_classes:
            a__:					Optional[int]      							=     self.model_tester.seq_length - self.model_tester.num_masks
            a__:					List[str]      							=     (
                num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
            )

            a__:					Tuple      							=     True
            a__:					str      							=     False
            a__:					Dict      							=     True
            a__:					List[Any]      							=     model_class(lowercase)
            model.to(lowercase)
            model.eval()
            with torch.no_grad():
              a__:					int      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))
            a__:					Any      							=     outputs.attentions
            self.assertEqual(len(lowercase)   ,      self.model_tester.num_hidden_layers)

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

            self.assertListEqual(
                list(attentions[0].shape[-3:])   ,      [self.model_tester.num_attention_heads, seq_len, seq_len]   ,      )
            a__:					Optional[Any]      							=     len(lowercase)

            # Check attention is always last and order is fine
            a__:					str      							=     True
            a__:					Dict      							=     True
            a__:					Tuple      							=     model_class(lowercase)
            model.to(lowercase)
            model.eval()
            with torch.no_grad():
              a__:					Optional[Any]      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

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

            a__:					int      							=     outputs.attentions

            self.assertEqual(len(lowercase)   ,      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   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        def check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase):
          a__:					Union[str, Any]      							=     model_class(lowercase)
          model.to(lowercase)
          model.eval()

          with torch.no_grad():
            a__:					Tuple      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

          a__:					Dict      							=     outputs.hidden_states
          a__:					Union[str, Any]      							=     self.model_tester.num_hidden_layers + 1
          self.assertEqual(len(lowercase)   ,      lowercase)

          a__:					Union[str, Any]      							=     self.model_tester.seq_length - self.model_tester.num_masks
          a__:					Optional[Any]      							=     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]   ,      )

        a__							,   a__:					List[str]      							=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:					Dict      							=     True
          check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)

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

          check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)



      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        pass







def 						__a       (     )     ->List[Any]:
  a__:					List[str]      							=     hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'		,					filename='eating_spaghetti.npy'		,					repo_type='dataset'       )
  a__:					Dict      							=     np.load(_SCREAMING_SNAKE_CASE       )
  return list(_SCREAMING_SNAKE_CASE       )




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



      @cached_property
      def   lowerCamelCase_       (						self)     ->      str:





        '''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   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Dict      							=     VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics').to(
            lowercase)

        a__:					Dict      							=     self.default_image_processor
        a__:					str      							=     prepare_video()
        a__:					Tuple      							=     image_processor(lowercase   ,      return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:					List[Any]      							=     model(**lowercase)

        # verify the logits
        a__:					str      							=     torch.Size((1, 4_00))
        self.assertEqual(outputs.logits.shape   ,      lowercase)

        a__:					Optional[Any]      							=     torch.tensor([0.3669, -0.0688, -0.2421]).to(lowercase)

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



      @slow
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short').to(lowercase)

        a__:					Optional[Any]      							=     self.default_image_processor
        a__:					List[Any]      							=     prepare_video()
        a__:					Union[str, Any]      							=     image_processor(lowercase   ,      return_tensors='pt').to(lowercase)

        # add boolean mask, indicating which patches to mask
        a__:					Optional[Any]      							=     hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'   ,      filename='bool_masked_pos.pt')
        a__:					Any      							=     torch.load(lowercase)

        # forward pass
        with torch.no_grad():
          a__:					Any      							=     model(**lowercase)

        # verify the logits
        a__:					Union[str, Any]      							=     torch.Size([1, 14_08, 15_36])
        a__:					Union[str, Any]      							=     torch.tensor(
            [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]   ,      device=lowercase)
        self.assertEqual(outputs.logits.shape   ,      lowercase)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,      lowercase   ,      atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `True`)
        a__:					Optional[int]      							=     torch.tensor([0.5142]   ,      device=lowercase)
        self.assertTrue(torch.allclose(outputs.loss   ,      lowercase   ,      atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `False`)
        a__:					int      							=     VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'   ,      norm_pix_loss=lowercase).to(
            lowercase)

        with torch.no_grad():
          a__:					Union[str, Any]      							=     model(**lowercase)

        a__:					Optional[int]      							=     torch.tensor(torch.tensor([0.6469])   ,      device=lowercase)
        self.assertTrue(torch.allclose(outputs.loss   ,      lowercase   ,      atol=1e-4))


