


"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
		from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from transformers import VivitImageProcessor
class snake_case					:







		def __init__(    self					:				List[Any]			,      a__					:				Optional[int]			,      a__					:				Optional[int]=2			,      a__					:				Optional[int]=True			,      a__					:				Union[str, Any]=True			,      a__					:				Union[str, Any]=10			,      a__					:				Union[str, Any]=10			,      a__					:				Any=8			,      a__					:				Dict=[2, 4, 4]			,      a__					:				List[str]=3			,      a__					:				List[str]=7_68			,      a__					:				int=5			,      a__					:				Any=4			,      a__					:				Optional[int]=37			,      a__					:				List[str]="gelu_fast"			,      a__					:				Optional[int]=0.0			,      a__					:				Union[str, Any]=0.0			,      a__					:				Union[str, Any]=0.0_2			,      a__					:				str=1E-0_6			,      a__					:				List[Any]=True			,      a__					:				Any=None			,      )				->  Any:





						'''simple docstring'''







						_A         =	parent
						_A         =	batch_size
						_A         =	is_training
						_A         =	use_labels
						_A         =	num_labels
						_A         =	image_size
						_A         =	num_frames
						_A         =	tubelet_size
						_A         =	num_channels
						_A         =	hidden_size
						_A         =	num_hidden_layers
						_A         =	num_attention_heads
						_A         =	intermediate_size
						_A         =	hidden_act
						_A         =	hidden_dropout_prob
						_A         =	attention_probs_dropout_prob
						_A         =	initializer_range
						_A         =	layer_norm_eps
						_A         =	qkv_bias
						_A         =	scope

						_A         =	(
						    (self.image_size // self.tubelet_size[2])
						    * (self.image_size // self.tubelet_size[1])
						    * (self.num_frames // self.tubelet_size[0])
						) + 1  # CLS token







		def        a_       (    self					:				List[str]						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	floats_tensor(
						    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

						_A         =	None
						if self.use_labels:
										_A         =	ids_tensor([self.batch_size]			,      self.num_labels						)

						_A         =	self.get_config()

						return config, pixel_values, labels







		def        a_       (    self					:				List[str]						)				->  Optional[Any]:





						'''simple docstring'''







						_A         =	VivitConfig(
						    num_frames=self.num_frames			,      image_size=self.image_size			,      tubelet_size=self.tubelet_size			,      num_channels=self.num_channels			,      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			,      layer_norm_eps=self.layer_norm_eps			,      qkv_bias=self.qkv_bias			,      )
						_A         =	self.num_labels
						return config







		def        a_       (    self					:				Dict			,      a__					:				Optional[int]			,      a__					:				Union[str, Any]			,      a__					:				int						)				->  int:





						'''simple docstring'''







						_A         =	VivitModel(config=a__						)
						model.to(a__						)
						model.eval()
						_A         =	model(a__						)
						self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size)						)







		def        a_       (    self					:				Union[str, Any]			,      a__					:				Dict			,      a__					:				Tuple			,      a__					:				Union[str, Any]						)				->  Dict:





						'''simple docstring'''







						_A         =	VivitForVideoClassification(a__						)
						model.to(a__						)
						model.eval()

						_A         =	model(a__						)

						# verify the logits shape
						_A         =	torch.Size((self.batch_size, self.num_labels)						)
						self.parent.assertEqual(result.logits.shape			,      a__						)







		def        a_       (    self					:				List[Any]						)				->  Tuple:





						'''simple docstring'''







						_A         =	self.prepare_config_and_inputs()
						_A     ,						_A     ,						_A         =	config_and_inputs
						_A         =	{"pixel_values": pixel_values}
						return config, inputs_dict





@require_torch
class snake_case					(     _UpperCamelCase   , unittest.TestCase):
		__UpperCamelCase							=	(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

		__UpperCamelCase							=	False
		__UpperCamelCase							=	False
		__UpperCamelCase							=	False
		__UpperCamelCase							=	False







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





						'''simple docstring'''







						_A         =	VivitModelTester(self						)
						_A         =	ConfigTester(self			,      config_class=a__			,      has_text_modality=a__			,      hidden_size=37						)







		def        a_       (    self					:				Tuple			,      a__					:				Dict			,      a__					:				List[Any]			,      a__					:				Optional[Any]=False						)				->  List[Any]:





						'''simple docstring'''







						_A         =	copy.deepcopy(a__						)

						if return_labels:
										if model_class in get_values(a__						):
														_A         =	torch.zeros(
														    self.model_tester.batch_size			,      dtype=torch.long			,      device=a__						)

						return inputs_dict







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





						'''simple docstring'''







						self.config_tester.run_common_tests()







		@unittest.skip(reason="Vivit does not use inputs_embeds"						)
		def        a_       (    self					:				Optional[int]						)				->  int:





						'''simple docstring'''







						pass







		def        a_       (    self					:				str						)				->  Optional[int]:





						'''simple docstring'''







						_A     ,						_A         =	self.model_tester.prepare_config_and_inputs_for_common()

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







		def        a_       (    self					:				int						)				->  Dict:





						'''simple docstring'''







						_A     ,						_A         =	self.model_tester.prepare_config_and_inputs_for_common()

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

										_A         =	["pixel_values", "head_mask"]
										self.assertListEqual(arg_names[:2]			,      a__						)







		def        a_       (    self					:				List[str]						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_video_classification(*a__						)







		@slow
		def        a_       (    self					:				Any						)				->  str:





						'''simple docstring'''







						for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A         =	VivitModel.from_pretrained(a__						)
										self.assertIsNotNone(a__						)







		def        a_       (    self					:				Optional[Any]						)				->  List[Any]:





						'''simple docstring'''







						_A     ,						_A         =	self.model_tester.prepare_config_and_inputs_for_common()
						_A         =	True

						for model_class in self.all_model_classes:
										_A         =	self.model_tester.seq_length

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

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

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

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

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

										_A         =	outputs.attentions

										self.assertEqual(len(a__						)			,      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        a_       (    self					:				Optional[int]						)				->  Dict:





						'''simple docstring'''







						def check_hidden_states_output(a__					:				List[Any]			,      a__					:				Tuple			,      a__					:				List[Any]						):
										_A         =	model_class(a__						)
										model.to(a__						)
										model.eval()

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

										_A         =	outputs.hidden_states
										_A         =	self.model_tester.num_hidden_layers + 1
										self.assertEqual(len(a__						)			,      a__						)

										_A         =	self.model_tester.seq_length

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

						_A     ,						_A         =	self.model_tester.prepare_config_and_inputs_for_common()

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

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

										check_hidden_states_output(a__			,      a__			,      a__						)







		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
		def        a_       (    self					:				List[Any]						)				->  Optional[int]:





						'''simple docstring'''







						pass






def    a__       ( )		->		Any:
				_A         =	hf_hub_download(
				    repo_id="hf-internal-testing/spaghetti-video"   ,	filename="eating_spaghetti_32_frames.npy"   ,	repo_type="dataset"      )
				_A         =	np.load(__lowercase      )
				return list(__lowercase      )




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







		@cached_property
		def        a_       (    self					:				Dict						)				->  Tuple:





						'''simple docstring'''







						return VivitImageProcessor() if is_vision_available() else None







		@slow
		def        a_       (    self					:				int						)				->  str:





						'''simple docstring'''







						_A         =	VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400"						).to(a__						)

						_A         =	self.default_image_processor
						_A         =	prepare_video()
						_A         =	image_processor(a__			,      return_tensors="pt"						).to(a__						)

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

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

						# taken from original model
						_A         =	torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]						).to(a__						)

						self.assertTrue(torch.allclose(outputs.logits[0, :5]			,      a__			,      atol=1E-4						)						)



"""simple docstring"""


from __future__ import annotations

a_											=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class snake_case					:







		def __init__(    self					:				Optional[int]			,      a__					:				int						)				->  None:





						'''simple docstring'''







						_A         =	-1
						_A         =	-1
						_A         =	[-1] * length
						_A         =	None
						_A         =	None







		def __repr__(    self					:				Any						)				->  str:





						'''simple docstring'''







						return F"""Node(min_value={self.minn} max_value={self.maxx})"""






def    a__       ( __lowercase      )		->		Node | None:
				_A         =	Node(len(__lowercase      )      )
				_A     ,						_A         =	min(__lowercase      ), max(__lowercase      )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_A         =	(root.minn + root.maxx) // 2

				_A         =	[]
				_A         =	[]

				for index, num in enumerate(__lowercase      ):
								if num <= pivot:
												left_arr.append(__lowercase      )
								else:
												right_arr.append(__lowercase      )
								_A         =	len(__lowercase      )
				_A         =	build_tree(__lowercase      )
				_A         =	build_tree(__lowercase      )
				return root






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_A         =	(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left   ,	__lowercase   ,	node.map_left[index] - 1      )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right   ,	__lowercase   ,	index - node.map_left[index]      )






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if start > end:
								return 0
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	__lowercase      )
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	start - 1      )
				return rank_till_end - rank_before_start






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_A         =	node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left   ,	__lowercase   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	)
				else:
								return quantile(
								    node.right   ,	index - num_elements_in_left_tree   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	)






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_A         =	range_counting(
				    node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowercase   ,	__lowercase   ,	)
				_A         =	range_counting(
				    node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowercase   ,	__lowercase   ,	)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()