




"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Dict=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_2    ,				__UpperCAmelCase				:						Union[str, Any]=2    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						List[Any]=1_6    ,				__UpperCAmelCase				:						List[Any]=[1, 2, 1]    ,				__UpperCAmelCase				:						List[Any]=[2, 2, 4]    ,				__UpperCAmelCase				:						Any=2    ,				__UpperCAmelCase				:						Optional[Any]=2.0    ,				__UpperCAmelCase				:						Optional[Any]=True    ,				__UpperCAmelCase				:						Union[str, Any]=0.0    ,				__UpperCAmelCase				:						int=0.0    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						str="gelu"    ,				__UpperCAmelCase				:						int=False    ,				__UpperCAmelCase				:						Any=True    ,				__UpperCAmelCase				:						int=0.02    ,				__UpperCAmelCase				:						Optional[int]=1e-5    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						List[str]=None    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						Tuple=1_0    ,				__UpperCAmelCase				:						Tuple=8    ,				)   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      embed_dim
						SCREAMING_SNAKE_CASE__								=      depths
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      window_size
						SCREAMING_SNAKE_CASE__								=      mlp_ratio
						SCREAMING_SNAKE_CASE__								=      qkv_bias
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      drop_path_rate
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      use_absolute_embeddings
						SCREAMING_SNAKE_CASE__								=      patch_norm
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      encoder_stride

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[str]:
						return SwinvaConfig(
						    image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				embed_dim=self.embed_dim    ,				depths=self.depths    ,				num_heads=self.num_heads    ,				window_size=self.window_size    ,				mlp_ratio=self.mlp_ratio    ,				qkv_bias=self.qkv_bias    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				drop_path_rate=self.drop_path_rate    ,				hidden_act=self.hidden_act    ,				use_absolute_embeddings=self.use_absolute_embeddings    ,				path_norm=self.patch_norm    ,				layer_norm_eps=self.layer_norm_eps    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      SwinvaModel(config=__a       )
						model.to(__a       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__a       )

						SCREAMING_SNAKE_CASE__								=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
						SCREAMING_SNAKE_CASE__								=      int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, expected_seq_len, expected_dim)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      SwinvaForMaskedImageModeling(config=__a       )
						model.to(__a       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__a       )
						self.parent.assertEqual(
						    result.logits.shape    ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      SwinvaForMaskedImageModeling(__a       )
						model.to(__a       )
						model.eval()

						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						SCREAMING_SNAKE_CASE__								=      model(__a       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, 1, self.image_size, self.image_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      SwinvaForImageClassification(__a       )
						model.to(__a       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__a    ,				labels=__a       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"pixel_values": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(_UpperCamelCase			,_UpperCamelCase			,unittest.TestCase			):
			lowerCamelCase__   :		List[str]							=							(
			    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
			)
			lowerCamelCase__   :		List[Any]							=							(
			    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Optional[Any]							=							False
			lowerCamelCase__   :		Union[str, Any]							=							False
			lowerCamelCase__   :		List[Any]							=							False
			lowerCamelCase__   :		Dict							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      SwinvaModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__a    ,				embed_dim=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Tuple:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__a       )

			@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[str]:
						pass

			@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["pixel_values"]
									self.assertListEqual(arg_names[:1]    ,				__a       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      False
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__a       )
									model.to(__a       )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__a    ,				__a       )       )
									SCREAMING_SNAKE_CASE__								=      outputs.attentions
									SCREAMING_SNAKE_CASE__								=      len(self.model_tester.depths       )
									self.assertEqual(len(__a       )    ,				__a       )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      config.window_size**2
									SCREAMING_SNAKE_CASE__								=      model_class(__a       )
									model.to(__a       )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__a    ,				__a       )       )
									SCREAMING_SNAKE_CASE__								=      outputs.attentions
									self.assertEqual(len(__a       )    ,				__a       )

									self.assertListEqual(
									    list(attentions[0].shape[-3:]       )    ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,				)
									SCREAMING_SNAKE_CASE__								=      len(__a       )

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

									if hasattr(self.model_tester    ,				"""num_hidden_states_types"""       ):
												SCREAMING_SNAKE_CASE__								=      self.model_tester.num_hidden_states_types
									else:
												# also another +1 for reshaped_hidden_states
												SCREAMING_SNAKE_CASE__								=      2
									self.assertEqual(out_len + added_hidden_states    ,				len(__a       )       )

									SCREAMING_SNAKE_CASE__								=      outputs.attentions

									self.assertEqual(len(__a       )    ,				__a       )

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]       )    ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      model_class(__a       )
						model.to(__a       )
						model.eval()

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__a    ,				__a       )       )

						SCREAMING_SNAKE_CASE__								=      outputs.hidden_states

						SCREAMING_SNAKE_CASE__								=      getattr(
						    self.model_tester    ,				"""expected_num_hidden_layers"""    ,				len(self.model_tester.depths       ) + 1       )
						self.assertEqual(len(__a       )    ,				__a       )

						# Swinv2 has a different seq_length
						SCREAMING_SNAKE_CASE__								=      (
						    config.patch_size
						    if isinstance(config.patch_size    ,				collections.abc.Iterable       )
						    else (config.patch_size, config.patch_size)
						)

						SCREAMING_SNAKE_CASE__								=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

						SCREAMING_SNAKE_CASE__								=      outputs.reshaped_hidden_states
						self.assertEqual(len(__a       )    ,				__a       )

						SCREAMING_SNAKE_CASE__								=      reshaped_hidden_states[0].shape
						SCREAMING_SNAKE_CASE__								=      (
						    reshaped_hidden_states[0].view(__a    ,				__a    ,				height * width       ).permute(0    ,				2    ,				1       )
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]       )    ,				[num_patches, self.model_tester.embed_dim]    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__								=      (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,				collections.abc.Iterable       )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									self.check_hidden_states_output(__a    ,				__a    ,				__a    ,				__a       )

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

									self.check_hidden_states_output(__a    ,				__a    ,				__a    ,				__a       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      3

						SCREAMING_SNAKE_CASE__								=      (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,				collections.abc.Iterable       )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						SCREAMING_SNAKE_CASE__								=      (
						    config.patch_size
						    if isinstance(config.patch_size    ,				collections.abc.Iterable       )
						    else (config.patch_size, config.patch_size)
						)

						SCREAMING_SNAKE_CASE__								=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						SCREAMING_SNAKE_CASE__								=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									self.check_hidden_states_output(__a    ,				__a    ,				__a    ,				(padded_height, padded_width)       )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE__								=      True
									self.check_hidden_states_output(__a    ,				__a    ,				__a    ,				(padded_height, padded_width)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__a       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__a       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Dict:
						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      SwinvaModel.from_pretrained(__a       )
									self.assertIsNotNone(__a       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__								=      _config_zero_init(__a       )
						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class(config=__a       )
									for name, param in model.named_parameters():
												if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1e9).round() / 1e9).item()    ,				[0.0, 1.0]    ,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,				)





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    int:
						return (
						    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       )
						    if is_vision_available()
						    else None
						)





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       ).to(
						    __a       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor

						SCREAMING_SNAKE_CASE__								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__a    ,				return_tensors="""pt"""       ).to(__a       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__a       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__a       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor([-0.3_947, -0.4_306, 0.0_026]       ).to(__a       )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				__a    ,				atol=1e-4       )       )





"""simple docstring"""




import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor





class    lowerCamelCase						(A__			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.config_class(**self.inputs_dict       )
						self.parent.assertTrue(hasattr(__UpperCAmelCase    ,				"""tf_padding"""       )       )
						self.parent.assertTrue(hasattr(__UpperCAmelCase    ,				"""depth_multiplier"""       )       )





class    lowerCamelCase						:

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Any=1_3    ,				__UpperCAmelCase				:						Any=3    ,				__UpperCAmelCase				:						int=3_2    ,				__UpperCAmelCase				:						Tuple=0.25    ,				__UpperCAmelCase				:						Optional[int]=8    ,				__UpperCAmelCase				:						Any=8    ,				__UpperCAmelCase				:						str=6    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						Any="relu6"    ,				__UpperCAmelCase				:						str=1_2_8_0    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Any=0.02    ,				__UpperCAmelCase				:						Any=True    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Dict=1_0    ,				__UpperCAmelCase				:						Optional[int]=None    ,				)   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      depth_multiplier
						SCREAMING_SNAKE_CASE__								=      depth_divisible_by
						SCREAMING_SNAKE_CASE__								=      min_depth
						SCREAMING_SNAKE_CASE__								=      expand_ratio
						SCREAMING_SNAKE_CASE__								=      tf_padding
						SCREAMING_SNAKE_CASE__								=      output_stride
						SCREAMING_SNAKE_CASE__								=      first_layer_is_expansion
						SCREAMING_SNAKE_CASE__								=      finegrained_output
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
						SCREAMING_SNAKE_CASE__								=      classifier_dropout_prob
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      num_labels
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels, pixel_labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						return MobileNetVaConfig(
						    num_channels=self.num_channels    ,				image_size=self.image_size    ,				depth_multiplier=self.depth_multiplier    ,				depth_divisible_by=self.depth_divisible_by    ,				min_depth=self.min_depth    ,				expand_ratio=self.expand_ratio    ,				output_stride=self.output_stride    ,				first_layer_is_expansion=self.first_layer_is_expansion    ,				finegrained_output=self.finegrained_output    ,				hidden_act=self.hidden_act    ,				tf_padding=self.tf_padding    ,				classifier_dropout_prob=self.classifier_dropout_prob    ,				initializer_range=self.initializer_range    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.last_hidden_state.shape    ,				(
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )    ,				)
						self.parent.assertEqual(
						    result.pooler_output.shape    ,				(self.batch_size, self.last_hidden_size)    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						str       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.num_labels
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.num_labels
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForSemanticSegmentation(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.logits.shape    ,				(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )    ,				)
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.logits.shape    ,				(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		str							=							(
			    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		str							=							(
			    {
			        'feature-extraction': MobileNetVaModel,
			        'image-classification': MobileNetVaForImageClassification,
			        'image-segmentation': MobileNetVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		List[Any]							=							False
			lowerCamelCase__   :		Union[str, Any]							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      MobileNetVaConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    int:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Union[str, Any]:
						pass

			@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						pass

			@unittest.skip(reason="""MobileNetV2 does not output attentions"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						def check_hidden_states_output(__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]       ):
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.eval()

									with torch.no_grad():
												SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

									SCREAMING_SNAKE_CASE__								=      outputs.hidden_states

									SCREAMING_SNAKE_CASE__								=      1_6
									self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

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

									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      MobileNetVaModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						return (
						    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""       ) if is_vision_available() else None
						)

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_1)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([0.2_445, -1.1_993, 0.1_905]       ).to(__UpperCAmelCase       )

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





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""       )
						SCREAMING_SNAKE_CASE__								=      model.to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""       )

						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      outputs.logits

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 2_1, 6_5, 6_5)       )
						self.assertEqual(logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [
						        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
						        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
						        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
						    ]    ,				device=__UpperCAmelCase    ,				)

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )


