





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							)							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





