





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       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=10   , UpperCAmelCase_=3   , UpperCAmelCase_=2   , UpperCAmelCase_=2   , UpperCAmelCase_=2   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=32   , UpperCAmelCase_=5   , UpperCAmelCase_=4   , UpperCAmelCase_=37   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=10   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=0.9   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =tubelet_size
								lowerCamelCase	 =num_frames
								lowerCamelCase	 =is_training
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =mask_ratio
								lowerCamelCase	 =scope

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

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


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

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

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								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=UpperCAmelCase_   , initializer_range=self.initializer_range   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VideoMAEModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VideoMAEForPreTraining(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								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
								lowerCamelCase	 =torch.ones((self.num_masks,)							)
								lowerCamelCase	 =torch.cat([mask, torch.zeros(self.seq_length - mask.size(0							)							)]							)
								lowerCamelCase	 =mask.expand(self.batch_size   , -1							).bool()

								lowerCamelCase	 =model(UpperCAmelCase_   , UpperCAmelCase_							)
								# model only returns predictions for masked patches
								lowerCamelCase	 =mask.sum().item()
								lowerCamelCase	 =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							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					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 _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , has_text_modality=UpperCAmelCase_   , hidden_size=37							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase	 =copy.deepcopy(UpperCAmelCase_							)

								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
												lowerCamelCase	 =torch.ones((self.model_tester.num_masks,)							)
												lowerCamelCase	 =torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0							)							)]							)
												lowerCamelCase	 =mask.expand(self.model_tester.batch_size   , -1							).bool()
												lowerCamelCase	 =bool_masked_pos.to(UpperCAmelCase_							)

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

								return inputs_dict


				def _snake_case       (		self							):
								self.config_tester.run_common_tests()


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


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

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


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_							)


				@slow
				def _snake_case       (		self							):
								for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase	 =VideoMAEModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)


				def _snake_case       (		self							):
								if not self.has_attentions:
												pass

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

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

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

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

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

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

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

																lowerCamelCase	 =outputs.attentions

																self.assertEqual(len(UpperCAmelCase_							)   , 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							):
								def check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												model.to(UpperCAmelCase_							)
												model.eval()

												with torch.no_grad():
																lowerCamelCase	 =model(**self._prepare_for_class(UpperCAmelCase_   , UpperCAmelCase_							)							)

												lowerCamelCase	 =outputs.hidden_states
												lowerCamelCase	 =self.model_tester.num_hidden_layers + 1
												self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

												lowerCamelCase	 =self.model_tester.seq_length - self.model_tester.num_masks
												lowerCamelCase	 =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]   , )

								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowerCamelCase	 =True
												check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)

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

												check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


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





def      _lowercase					(						)						->  List[str]:
				lowerCamelCase	 =hf_hub_download(
				    repo_id="""hf-internal-testing/spaghetti-video"""			,      filename="""eating_spaghetti.npy"""			,      repo_type="""dataset"""				)
				lowerCamelCase	 =np.load(_UpperCAmelCase				)
				return list(_UpperCAmelCase				)




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


				@cached_property
				def _snake_case       (		self							):
								# 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 _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics"""							).to(
								    UpperCAmelCase_							)

								lowerCamelCase	 =self.default_image_processor
								lowerCamelCase	 =prepare_video()
								lowerCamelCase	 =image_processor(UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

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

								lowerCamelCase	 =torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]							).to(UpperCAmelCase_							)

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


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""							).to(UpperCAmelCase_							)

								lowerCamelCase	 =self.default_image_processor
								lowerCamelCase	 =prepare_video()
								lowerCamelCase	 =image_processor(UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

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

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

								# verify the logits
								lowerCamelCase	 =torch.Size([1, 1408, 1536]							)
								lowerCamelCase	 =torch.tensor(
								    [[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   , device=UpperCAmelCase_							)
								self.assertEqual(outputs.logits.shape   , UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   , UpperCAmelCase_   , atol=1E-4							)							)

								# verify the loss (`config.norm_pix_loss` = `True`)
								lowerCamelCase	 =torch.tensor([0.5_1_4_2]   , device=UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.loss   , UpperCAmelCase_   , atol=1E-4							)							)

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

								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

								lowerCamelCase	 =torch.tensor(torch.tensor([0.6_4_6_9]							)   , device=UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.loss   , UpperCAmelCase_   , atol=1E-4							)							)












def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =[0 for i in range(r + 1				)]
				# nc0 = 1
				lowerCamelCase	 =1
				for i in range(1			,      n + 1				):
								# to compute current row from previous row.
								lowerCamelCase	 =min(_UpperCAmelCase			,      _UpperCAmelCase				)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]




print(binomial_coefficient(n=10, r=5))





