


"""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 							A   :
					'''simple docstring'''






					def __init__(self   :						Dict				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Union[str, Any]=13				, _UpperCAmelCase   :						Optional[Any]=32				, _UpperCAmelCase   :						Tuple=2				, _UpperCAmelCase   :						Optional[int]=3				, _UpperCAmelCase   :						Tuple=16				, _UpperCAmelCase   :						str=[1, 2, 1]				, _UpperCAmelCase   :						Optional[int]=[2, 2, 4]				, _UpperCAmelCase   :						str=2				, _UpperCAmelCase   :						Union[str, Any]=2.0				, _UpperCAmelCase   :						Tuple=True				, _UpperCAmelCase   :						List[Any]=0.0				, _UpperCAmelCase   :						Union[str, Any]=0.0				, _UpperCAmelCase   :						Any=0.1				, _UpperCAmelCase   :						Tuple="gelu"				, _UpperCAmelCase   :						List[str]=False				, _UpperCAmelCase   :						Union[str, Any]=True				, _UpperCAmelCase   :						List[str]=0.02				, _UpperCAmelCase   :						Tuple=1E-5				, _UpperCAmelCase   :						Union[str, Any]=True				, _UpperCAmelCase   :						Optional[Any]=None				, _UpperCAmelCase   :						Tuple=True				, _UpperCAmelCase   :						Dict=10				, _UpperCAmelCase   :						int=8				, )						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		patch_size
							lowercase__					      =		num_channels
							lowercase__					      =		embed_dim
							lowercase__					      =		depths
							lowercase__					      =		num_heads
							lowercase__					      =		window_size
							lowercase__					      =		mlp_ratio
							lowercase__					      =		qkv_bias
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		drop_path_rate
							lowercase__					      =		hidden_act
							lowercase__					      =		use_absolute_embeddings
							lowercase__					      =		patch_norm
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		initializer_range
							lowercase__					      =		is_training
							lowercase__					      =		scope
							lowercase__					      =		use_labels
							lowercase__					      =		type_sequence_label_size
							lowercase__					      =		encoder_stride

					def   lowerCamelCase__					(self   :						int		)						->       Any:




							"""simple docstring"""


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

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

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						Dict		)						->       str:




							"""simple docstring"""


							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   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		SwinvaModel(config=__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							lowercase__					      =		model(__SCREAMING_SNAKE_CASE		)

							lowercase__					      =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
							lowercase__					      =		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   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							lowercase__					      =		model(__SCREAMING_SNAKE_CASE		)
							self.parent.assertEqual(
							    result.logits.shape				, (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

							# test greyscale images
							lowercase__					      =		1
							lowercase__					      =		SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()

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

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		self.type_sequence_label_size
							lowercase__					      =		SwinvaForImageClassification(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()
							lowercase__					      =		model(__SCREAMING_SNAKE_CASE				, labels=__SCREAMING_SNAKE_CASE		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)		)


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




							"""simple docstring"""


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






@require_torch
class 							A   (	lowerCAmelCase_			,  lowerCAmelCase_			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					A__      =			(
					    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						Dict		)						->       str:




							"""simple docstring"""


							lowercase__					      =		SwinvaModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=__SCREAMING_SNAKE_CASE				, embed_dim=37		)

					def   lowerCamelCase__					(self   :						int		)						->       Union[str, Any]:




							"""simple docstring"""


							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   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE		)

					@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""		)
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							pass

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




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, __SCREAMING_SNAKE_CASE		)

					def   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""simple docstring"""


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

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

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

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

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

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

									lowercase__					      =		outputs.attentions

									self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				, __SCREAMING_SNAKE_CASE		)

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

					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Any		)						->       str:




							"""simple docstring"""


							lowercase__					      =		model_class(__SCREAMING_SNAKE_CASE		)
							model.to(__SCREAMING_SNAKE_CASE		)
							model.eval()

							with torch.no_grad():
									lowercase__					      =		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE		)		)

							lowercase__					      =		outputs.hidden_states

							lowercase__					      =		getattr(
							    self.model_tester				, """expected_num_hidden_layers"""				, len(self.model_tester.depths		) + 1		)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				, __SCREAMING_SNAKE_CASE		)

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

							lowercase__					      =		(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]				, )

							lowercase__					      =		outputs.reshaped_hidden_states
							self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				, __SCREAMING_SNAKE_CASE		)

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

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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					      =		(
							    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:
									lowercase__					      =		True
									self.check_hidden_states_output(__SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE		)

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

									self.check_hidden_states_output(__SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE		)

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__					      =		3

							lowercase__					      =		(
							    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)
							)
							lowercase__					      =		(
							    config.patch_size
							    if isinstance(config.patch_size				, collections.abc.Iterable		)
							    else (config.patch_size, config.patch_size)
							)

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

							for model_class in self.all_model_classes:
									lowercase__					      =		True
									self.check_hidden_states_output(__SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, (padded_height, padded_width)		)

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

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE		)

					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
									self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)


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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					      =		_config_zero_init(__SCREAMING_SNAKE_CASE		)
							for model_class in self.all_model_classes:
									lowercase__					      =		model_class(config=__SCREAMING_SNAKE_CASE		)
									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 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@cached_property
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""		)
							    if is_vision_available()
							    else None
							)


					@slow
					def   lowerCamelCase__					(self   :						List[str]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""		).to(
							    __SCREAMING_SNAKE_CASE		)
							lowercase__					      =		self.default_image_processor

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

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**__SCREAMING_SNAKE_CASE		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 1000)		)
							self.assertEqual(outputs.logits.shape				, __SCREAMING_SNAKE_CASE		)
							lowercase__					      =		torch.tensor([-0.3_947, -0.4_306, 0.0_026]		).to(__SCREAMING_SNAKE_CASE		)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]				, __SCREAMING_SNAKE_CASE				, atol=1E-4		)		)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

