






import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a	,      a=13	,      a=32	,      a=2	,      a=3	,      a=16	,      a=[1, 2, 1]	,      a=[2, 2, 4]	,      a=2	,      a=2.0	,      a=True	,      a=0.0	,      a=0.0	,      a=0.1	,      a="gelu"	,      a=False	,      a=True	,      a=0.02	,      a=1E-5	,      a=True	,      a=None	,      a=True	,      a=10	,      a=8	,      a=["stage1", "stage2"]	,      a=[1, 2]	,      )	->   int:
								snake_case_           =							parent
								snake_case_           =							batch_size
								snake_case_           =							image_size
								snake_case_           =							patch_size
								snake_case_           =							num_channels
								snake_case_           =							embed_dim
								snake_case_           =							depths
								snake_case_           =							num_heads
								snake_case_           =							window_size
								snake_case_           =							mlp_ratio
								snake_case_           =							qkv_bias
								snake_case_           =							hidden_dropout_prob
								snake_case_           =							attention_probs_dropout_prob
								snake_case_           =							drop_path_rate
								snake_case_           =							hidden_act
								snake_case_           =							use_absolute_embeddings
								snake_case_           =							patch_norm
								snake_case_           =							layer_norm_eps
								snake_case_           =							initializer_range
								snake_case_           =							is_training
								snake_case_           =							scope
								snake_case_           =							use_labels
								snake_case_           =							type_sequence_label_size
								snake_case_           =							encoder_stride
								snake_case_           =							out_features
								snake_case_           =							out_indices




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_           =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

								snake_case_           =							self.get_config()

								return config, pixel_values, labels




	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								return SwinConfig(
								    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	,      out_features=self.out_features	,      out_indices=self.out_indices	,      )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   Optional[int]:
								snake_case_           =							SwinModel(config=a )
								model.to(a )
								model.eval()
								snake_case_           =							model(a )

								snake_case_           =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
								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 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   List[str]:
								snake_case_           =							SwinBackbone(config=a )
								model.to(a )
								model.eval()
								snake_case_           =							model(a )

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps )	,      len(config.out_features ) )
								self.parent.assertListEqual(list(result.feature_maps[0].shape )	,      [self.batch_size, model.channels[0], 16, 16] )

								# verify channels
								self.parent.assertEqual(len(model.channels )	,      len(config.out_features ) )

								# verify backbone works with out_features=None
								snake_case_           =							None
								snake_case_           =							SwinBackbone(config=a )
								model.to(a )
								model.eval()
								snake_case_           =							model(a )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps )	,      1 )
								self.parent.assertListEqual(list(result.feature_maps[0].shape )	,      [self.batch_size, model.channels[-1], 4, 4] )

								# verify channels
								self.parent.assertEqual(len(model.channels )	,      1 )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   Tuple:
								snake_case_           =							SwinForMaskedImageModeling(config=a )
								model.to(a )
								model.eval()
								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
								snake_case_           =							1
								snake_case_           =							SwinForMaskedImageModeling(a )
								model.to(a )
								model.eval()

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




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   Dict:
								snake_case_           =							self.type_sequence_label_size
								snake_case_           =							SwinForImageClassification(a )
								model.to(a )
								model.eval()
								snake_case_           =							model(a	,      labels=a )
								self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size) )

								# test greyscale images
								snake_case_           =							1
								snake_case_           =							SwinForImageClassification(a )
								model.to(a )
								model.eval()

								snake_case_           =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
								snake_case_           =							model(a )
								self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size) )






	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		
								)           =							config_and_inputs
								snake_case_           =							{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 			UpperCamelCase_				(      snake_case_	, snake_case_	, unittest.TestCase    ):



	'''simple docstring'''




	lowerCAmelCase													=			(
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase													=			(
	    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase													=			True

	lowerCAmelCase													=			False
	lowerCAmelCase													=			False
	lowerCAmelCase													=			False




	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							SwinModelTester(self )
								snake_case_           =							ConfigTester(self	,      config_class=a	,      embed_dim=37 )




	def 							_UpperCamelCase      (	self )	->   Dict:
								self.create_and_test_config_common_properties()
								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 							_UpperCamelCase      (	self )	->   List[Any]:
								return




	def 							_UpperCamelCase      (	self )	->   List[str]:
								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 							_UpperCamelCase      (	self )	->   List[str]:
								pass




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								super().test_training_gradient_checkpointing()




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*a )




	def 							_UpperCamelCase      (	self )	->   List[Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*a )




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a )




	@unittest.skip(reason='Swin does not use inputs_embeds' )
	def 							_UpperCamelCase      (	self )	->   str:
								pass




	@unittest.skip(reason='Swin Transformer does not use feedforward chunking' )
	def 							_UpperCamelCase      (	self )	->   int:
								pass




	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

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




	def 							_UpperCamelCase      (	self )	->   int:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_           =							['pixel_values']
															self.assertListEqual(arg_names[:1]	,      a )




	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_           =							True

								for model_class in self.all_model_classes:
															snake_case_           =							True
															snake_case_           =							False
															snake_case_           =							True
															snake_case_           =							model_class(a )
															model.to(a )
															model.eval()
															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(a	,      a ) )
															snake_case_           =							outputs.attentions
															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"]
															snake_case_           =							True
															snake_case_           =							config.window_size**2
															snake_case_           =							model_class(a )
															model.to(a )
															model.eval()
															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(a	,      a ) )
															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]	,      )
															snake_case_           =							len(a )

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

															# also another +1 for reshaped_hidden_states
															snake_case_           =							1 if model_class.__name__ == 'SwinBackbone' else 2
															self.assertEqual(out_len + added_hidden_states	,      len(a ) )

															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 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a )	->   str:
								snake_case_           =							model_class(a )
								model.to(a )
								model.eval()

								with torch.no_grad():
															snake_case_           =							model(**self._prepare_for_class(a	,      a ) )

								snake_case_           =							outputs.hidden_states

								snake_case_           =							getattr(
								    self.model_tester	,      'expected_num_hidden_layers'	,      len(self.model_tester.depths ) + 1 )
								self.assertEqual(len(a )	,      a )

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

								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]	,      )

								if not model_class.__name__ == "SwinBackbone":
															snake_case_           =							outputs.reshaped_hidden_states
															self.assertEqual(len(a )	,      a )

															snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_           =							reshaped_hidden_states[0].shape
															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 							_UpperCamelCase      (	self )	->   List[str]:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

								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:
															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"]
															snake_case_           =							True

															self.check_hidden_states_output(a	,      a	,      a	,      a )




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_           =							3

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

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

								for model_class in self.all_model_classes:
															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"]
															snake_case_           =							True
															self.check_hidden_states_output(a	,      a	,      a	,      (padded_height, padded_width) )




	@slow
	def 							_UpperCamelCase      (	self )	->   List[Any]:
								for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =							SwinModel.from_pretrained(a )
															self.assertIsNotNone(a )






	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_           =							_config_zero_init(a )
								for model_class in self.all_model_classes:
															snake_case_           =							model_class(config=a )
															for name, param in model.named_parameters():
																						if "embeddings" 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 			UpperCamelCase_				(      unittest.TestCase    ):



	'''simple docstring'''




	@cached_property
	def 							_UpperCamelCase      (	self )	->   Dict:
								return (
								    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224' )
								    if is_vision_available()
								    else None
								)






	@slow
	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224' ).to(a )
								snake_case_           =							self.default_image_processor

								snake_case_           =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
								snake_case_           =							image_processor(images=a	,      return_tensors='pt' ).to(a )

								# forward pass
								with torch.no_grad():
															snake_case_           =							model(**a )

								# verify the logits
								snake_case_           =							torch.Size((1, 10_00) )
								self.assertEqual(outputs.logits.shape	,      a )
								snake_case_           =							torch.tensor([-0.0_948, -0.6_454, -0.0_921] ).to(a )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]	,      a	,      atol=1E-4 ) )






@require_torch
class 			UpperCamelCase_				(      unittest.TestCase	, snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			(SwinBackbone,) if is_torch_available() else ()
	lowerCAmelCase													=			SwinConfig




	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							SwinModelTester(self )













from typing import TYPE_CHECKING

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	lowercase  							= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





