


"""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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

