

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		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]=1_3   ,SCREAMING_SNAKE_CASE__		:			Dict=1_0   ,SCREAMING_SNAKE_CASE__		:			List[str]=3   ,SCREAMING_SNAKE_CASE__		:			int=2   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=2   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			Any=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=3_2   ,SCREAMING_SNAKE_CASE__		:			str=5   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=4   ,SCREAMING_SNAKE_CASE__		:			Tuple=3_7   ,SCREAMING_SNAKE_CASE__		:			List[Any]="gelu"   ,SCREAMING_SNAKE_CASE__		:			int=0.1   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.1   ,SCREAMING_SNAKE_CASE__		:			Any=1_0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=0.02   ,SCREAMING_SNAKE_CASE__		:			Dict=0.9   ,SCREAMING_SNAKE_CASE__		:			str=None   ,):
								__lowerCamelCase				:				Any					       = parent
								__lowerCamelCase				:				List[str]					       = batch_size
								__lowerCamelCase				:				List[Any]					       = image_size
								__lowerCamelCase				:				Union[str, Any]					       = num_channels
								__lowerCamelCase				:				Optional[Any]					       = patch_size
								__lowerCamelCase				:				int					       = tubelet_size
								__lowerCamelCase				:				Optional[Any]					       = num_frames
								__lowerCamelCase				:				List[str]					       = is_training
								__lowerCamelCase				:				str					       = use_labels
								__lowerCamelCase				:				str					       = hidden_size
								__lowerCamelCase				:				int					       = num_hidden_layers
								__lowerCamelCase				:				int					       = num_attention_heads
								__lowerCamelCase				:				Tuple					       = intermediate_size
								__lowerCamelCase				:				int					       = hidden_act
								__lowerCamelCase				:				Union[str, Any]					       = hidden_dropout_prob
								__lowerCamelCase				:				Tuple					       = attention_probs_dropout_prob
								__lowerCamelCase				:				List[Any]					       = type_sequence_label_size
								__lowerCamelCase				:				List[Any]					       = initializer_range
								__lowerCamelCase				:				Tuple					       = mask_ratio
								__lowerCamelCase				:				List[str]					       = scope

								# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
								__lowerCamelCase				:				Optional[Any]					       = (image_size // patch_size) ** 2
								__lowerCamelCase				:				Union[str, Any]					       = (num_frames // tubelet_size) * self.num_patches_per_frame

								# use this variable to define bool_masked_pos
								__lowerCamelCase				:				Any					       = int(mask_ratio * self.seq_length)
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Any					       = floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase				:				Optional[int]					       = None
								if self.use_labels:
									__lowerCamelCase				:				int					       = ids_tensor([self.batch_size]   ,self.type_sequence_label_size)

								__lowerCamelCase				:				List[Any]					       = self.get_config()

								return config, pixel_values, labels
							def     lowerCAmelCase     (		self		:			Optional[int]):
								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=SCREAMING_SNAKE_CASE__   ,initializer_range=self.initializer_range   ,)
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								__lowerCamelCase				:				List[str]					       = VideoMAEModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase				:				Optional[int]					       = model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								__lowerCamelCase				:				Optional[int]					       = VideoMAEForPreTraining(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								# important: each video needs to have the same number of masked patches
								# hence we define a single mask, which we then repeat for each example in the batch
								__lowerCamelCase				:				int					       = torch.ones((self.num_masks,))
								__lowerCamelCase				:				List[str]					       = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
								__lowerCamelCase				:				Optional[int]					       = mask.expand(self.batch_size   ,-1).bool()

								__lowerCamelCase				:				Optional[Any]					       = model(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								# model only returns predictions for masked patches
								__lowerCamelCase				:				Union[str, Any]					       = mask.sum().item()
								__lowerCamelCase				:				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		:			Tuple):
								__lowerCamelCase				:				Dict					       = self.prepare_config_and_inputs()
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[str]					       = config_and_inputs
								__lowerCamelCase				:				Any					       = {'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  Any						      =      (
							    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
							)
							_UpperCAmelCase    :  Dict						      =      (
							    {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase    :  Optional[Any]						      =      False
							_UpperCAmelCase    :  str						      =      False
							_UpperCAmelCase    :  Optional[int]						      =      False
							_UpperCAmelCase    :  Optional[int]						      =      False
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				str					       = VideoMAEModelTester(self)
								__lowerCamelCase				:				Union[str, Any]					       = ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE__   ,has_text_modality=SCREAMING_SNAKE_CASE__   ,hidden_size=3_7)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=False):
								__lowerCamelCase				:				Union[str, Any]					       = copy.deepcopy(SCREAMING_SNAKE_CASE__)

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

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

								return inputs_dict
							def     lowerCAmelCase     (		self		:			Dict):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='VideoMAE does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			Any):
								pass
							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase       , __lowerCamelCase				:				int					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Dict					       = model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()   ,(nn.Module))
									__lowerCamelCase				:				int					       = model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__   ,nn.Linear))
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase				:				int					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				List[Any]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				str					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__)
							@slow
							def     lowerCAmelCase     (		self		:			int):
								for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase				:				Optional[Any]					       = VideoMAEModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple):
								if not self.has_attentions:
									pass

								else:
									__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase				:				Any					       = True

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

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

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

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

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

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

										__lowerCamelCase				:				List[Any]					       = outputs.attentions

										self.assertEqual(len(SCREAMING_SNAKE_CASE__)   ,self.model_tester.num_hidden_layers)
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:])   ,[self.model_tester.num_attention_heads, seq_len, seq_len]   ,)
							def     lowerCAmelCase     (		self		:			str):
								def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
									__lowerCamelCase				:				str					       = model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									with torch.no_grad():
										__lowerCamelCase				:				Union[str, Any]					       = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__))

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

									__lowerCamelCase				:				Dict					       = self.model_tester.seq_length - self.model_tester.num_masks
									__lowerCamelCase				:				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]   ,)

								__lowerCamelCase       , __lowerCamelCase				:				str					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Optional[Any]					       = True
									check_hidden_states_output(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

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

									check_hidden_states_output(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)


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






def    SCREAMING_SNAKE_CASE__       (  )  ->     Dict:
	__lowerCamelCase				:				Dict					       = hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'	, filename='eating_spaghetti.npy'	, repo_type='dataset'			)
	__lowerCamelCase				:				int					       = np.load(lowerCamelCase__			)
	return list(lowerCamelCase__			)






@require_torch
@require_vision
class        A_		(       unittest.TestCase				):
							@cached_property
							def     lowerCAmelCase     (		self		:			List[Any]):
								# 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     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Dict					       = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics').to(
								    SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				str					       = self.default_image_processor
								__lowerCamelCase				:				int					       = prepare_video()
								__lowerCamelCase				:				str					       = image_processor(SCREAMING_SNAKE_CASE__   ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase				:				Union[str, Any]					       = model(**SCREAMING_SNAKE_CASE__)

								# verify the logits
								__lowerCamelCase				:				Union[str, Any]					       = torch.Size((1, 4_0_0))
								self.assertEqual(outputs.logits.shape   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Tuple					       = torch.tensor([0.3669, -0.0688, -0.2421]).to(SCREAMING_SNAKE_CASE__)

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


							@slow
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Dict					       = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short').to(SCREAMING_SNAKE_CASE__)

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

								# add boolean mask, indicating which patches to mask
								__lowerCamelCase				:				str					       = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'   ,filename='bool_masked_pos.pt')
								__lowerCamelCase				:				Dict					       = torch.load(SCREAMING_SNAKE_CASE__)

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

								# verify the logits
								__lowerCamelCase				:				Any					       = torch.Size([1, 1_4_0_8, 1_5_3_6])
								__lowerCamelCase				:				Optional[int]					       = torch.tensor(
								    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]   ,device=SCREAMING_SNAKE_CASE__)
								self.assertEqual(outputs.logits.shape   ,SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=1E-4))

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

								# verify the loss (`config.norm_pix_loss` = `False`)
								__lowerCamelCase				:				List[Any]					       = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'   ,norm_pix_loss=SCREAMING_SNAKE_CASE__).to(
								    SCREAMING_SNAKE_CASE__)

								with torch.no_grad():
									__lowerCamelCase				:				Optional[int]					       = model(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Optional[Any]					       = torch.tensor(torch.tensor([0.6469])   ,device=SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(outputs.loss   ,SCREAMING_SNAKE_CASE__   ,atol=1E-4))








"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Callable[[int | float], int | float] ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int = 1_00 ,			)   ->						float:
						SCREAMING_SNAKE_CASE													=					x_start
						SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0.0
						for _ in range(SCREAMING_SNAKE_CASE_  ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE													=					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
												area += abs(fxa + fxa  ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE													=					xa
												SCREAMING_SNAKE_CASE													=					fxa
						return area


if __name__ == "__main__":


	def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Dict:
							return x**3 + x**2

	print('''f(x) = x^3 + x^2''')
	print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
	__UpperCamelCase						=      10
	while i <= 100000:
		print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10


