






'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyInpaintPipeline
 snake_case_  							=							["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 snake_case_  							=							[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						List[str]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       return self.time_input_dim




 @property
 def      __magic_name__	(      self	:							str      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
       return tokenizer




 @property
 def      __magic_name__	(      self	:							Any      )	->						int:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=MCLIPConfig(
           numDims=self.cross_attention_dim					,      transformerDimensions=self.text_embedder_hidden_size					,      hidden_size=self.text_embedder_hidden_size					,      intermediate_size=37					,      num_attention_heads=4					,      num_hidden_layers=5					,      vocab_size=10_05					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=MultilingualCLIP(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=text_encoder.eval()

       return text_encoder




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						int:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									={
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				Dict									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Optional[Any]      )	->						Dict:
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						Any:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				str									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__     :				str									=self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__     :				Dict									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				str									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Any									=DDIMScheduler(
           num_train_timesteps=10_00					,      beta_schedule='''linear'''					,      beta_start=0.00085					,      beta_end=0.012					,      clip_sample=__lowercase					,      set_alpha_to_one=__lowercase					,      steps_offset=1					,      prediction_type='''epsilon'''					,      thresholding=__lowercase					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							Optional[Any]					,      __lowercase	:							int					,      __lowercase	:							List[str]=0      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.cross_attention_dim)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=floats_tensor((1, self.cross_attention_dim)					,      rng=random.Random(seed + 1      )      ).to(__lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				str									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )
       # create mask
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=np.ones((64, 64)					,      dtype=np.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=0

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				str									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									={
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

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

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

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

       print(F"image.shape {image.shape}"      )

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

       SCREAMING_SNAKE_CASE__     :				int									=np.array(
           [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]      )

       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()}"




 def      __magic_name__	(      self	:							List[str]      )	->						str:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






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




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




 def      __magic_name__	(      self	:							List[Any]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=np.ones((7_68, 7_68)					,      dtype=np.floataa      )
       SCREAMING_SNAKE_CASE__     :				str									=0

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''a hat'''

       SCREAMING_SNAKE_CASE__     :				Dict									=KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-inpaint'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipeline.to(__lowercase      )
       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Dict									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline(
           __lowercase					,      image=__lowercase					,      mask_image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )







'''simple docstring'''
import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
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,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class      __SCREAMING_SNAKE_CASE      :




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

       # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
       SCREAMING_SNAKE_CASE__     :				List[str]									=(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__     :				str									=(num_frames) * self.num_patches_per_frame + 1




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=None
       if self.use_labels:
             SCREAMING_SNAKE_CASE__     :				Any									=ids_tensor([self.batch_size]					,      self.num_labels      )

       SCREAMING_SNAKE_CASE__     :				int									=self.get_config()

       return config, pixel_values, labels




 def      __magic_name__	(      self	:							int      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=TimesformerConfig(
           image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      num_frames=self.num_frames					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      initializer_range=self.initializer_range					,      attention_type=self.attention_type					,      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.num_labels
       return config




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Optional[int]					,      __lowercase	:							Dict					,      __lowercase	:							List[Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Tuple									=TimesformerModel(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(__lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							str					,      __lowercase	:							Tuple					,      __lowercase	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=TimesformerForVideoClassification(__lowercase      )
       model.to(__lowercase      )
       model.eval()

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase      )

       # verify the logits shape
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Size((self.batch_size, self.num_labels)      )
       self.parent.assertEqual(result.logits.shape					,      __lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				Any									={'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 snake_case_  							=							(
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False




 def      __magic_name__	(      self	:							str      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Dict									=TimesformerModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=ConfigTester(
           self					,      config_class=__lowercase					,      has_text_modality=__lowercase					,      hidden_size=37      )




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Dict					,      __lowercase	:							str					,      __lowercase	:							Optional[int]=False      )	->						int:
       SCREAMING_SNAKE_CASE__     :				str									=copy.deepcopy(__lowercase      )

       if return_labels:
             if model_class in get_values(__lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				Any									=torch.zeros(
                       self.model_tester.batch_size					,      dtype=torch.long					,      device=__lowercase      )

       return inputs_dict




 def      __magic_name__	(      self	:							List[Any]      )	->						Any:
       self.config_tester.run_common_tests()




 @unittest.skip(reason='''TimeSformer does not use inputs_embeds'''      )
 def      __magic_name__	(      self	:							List[Any]      )	->						Optional[int]:
       pass




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

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




 def      __magic_name__	(      self	:							Any      )	->						Any:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE__     :				Dict									=['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,      __lowercase      )




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_video_classification(*__lowercase      )




 @slow
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=TimesformerModel.from_pretrained(__lowercase      )
             self.assertIsNotNone(__lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[str]:
       if not self.has_attentions:
             pass

       else:
             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE__     :				List[Any]									=True

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.seq_length
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.model_tester.num_frames

                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
                   SCREAMING_SNAKE_CASE__     :				str									=False
                   SCREAMING_SNAKE_CASE__     :				Tuple									=True
                   SCREAMING_SNAKE_CASE__     :				Dict									=model_class(__lowercase      )
                   model.to(__lowercase      )
                   model.eval()
                   with torch.no_grad():
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(**self._prepare_for_class(__lowercase					,      __lowercase      )      )
                   SCREAMING_SNAKE_CASE__     :				Union[str, 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"]
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=True
                   SCREAMING_SNAKE_CASE__     :				List[str]									=model_class(__lowercase      )
                   model.to(__lowercase      )
                   model.eval()
                   with torch.no_grad():
                         SCREAMING_SNAKE_CASE__     :				Any									=model(**self._prepare_for_class(__lowercase					,      __lowercase      )      )
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.attentions
                   self.assertEqual(len(__lowercase      )					,      self.model_tester.num_hidden_layers      )

                   # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]      )					,      [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,      )
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(__lowercase      )

                   # Check attention is always last and order is fine
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=True
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model_class(__lowercase      )
                   model.to(__lowercase      )
                   model.eval()
                   with torch.no_grad():
                         SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(**self._prepare_for_class(__lowercase					,      __lowercase      )      )

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

                   SCREAMING_SNAKE_CASE__     :				List[str]									=outputs.attentions

                   self.assertEqual(len(__lowercase      )					,      self.model_tester.num_hidden_layers      )

                   # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]      )					,      [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,      )




 def      __magic_name__	(      self	:							Tuple      )	->						List[Any]:
       def check_hidden_states_output(__lowercase	:							Tuple					,      __lowercase	:							Dict					,      __lowercase	:							Optional[int]      ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=model_class(__lowercase      )
             model.to(__lowercase      )
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE__     :				int									=model(**self._prepare_for_class(__lowercase					,      __lowercase      )      )

             SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.hidden_states
             SCREAMING_SNAKE_CASE__     :				Tuple									=self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(__lowercase      )					,      __lowercase      )

             SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.seq_length

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

       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__     :				Tuple									=True
             check_hidden_states_output(__lowercase					,      __lowercase					,      __lowercase      )

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

             check_hidden_states_output(__lowercase					,      __lowercase					,      __lowercase      )





def  _a(  ):




      '''simple docstring'''






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







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




 @cached_property
 def      __magic_name__	(      self	:							Any      )	->						List[str]:
       # logits were tested with a different mean and std, so we use the same here
       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      __magic_name__	(      self	:							Any      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''      ).to(
           __lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.default_image_processor
       SCREAMING_SNAKE_CASE__     :				Tuple									=prepare_video()
       SCREAMING_SNAKE_CASE__     :				Any									=image_processor(video[:8]					,      return_tensors='''pt'''      ).to(__lowercase      )

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(**__lowercase      )

       # verify the logits
       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.Size((1, 4_00)      )
       self.assertEqual(outputs.logits.shape					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.tensor([-0.3016, -0.7713, -0.4205]      ).to(__lowercase      )

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