



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  _lowercase				:
				'''simple docstring'''







				def __init__(	self       ,	snake_case__       ,	snake_case__=13       ,	snake_case__=32       ,	snake_case__=2       ,	snake_case__=3       ,	snake_case__=16       ,	snake_case__=[1, 2, 1]       ,	snake_case__=[2, 2, 4]       ,	snake_case__=2       ,	snake_case__=2.0       ,	snake_case__=True       ,	snake_case__=0.0       ,	snake_case__=0.0       ,	snake_case__=0.1       ,	snake_case__="gelu"       ,	snake_case__=False       ,	snake_case__=True       ,	snake_case__=0.02       ,	snake_case__=1e-5       ,	snake_case__=True       ,	snake_case__=None       ,	snake_case__=True       ,	snake_case__=10       ,	snake_case__=8       ,	snake_case__=["stage1", "stage2"]       ,	snake_case__=[1, 2]       ,	):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

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

									UpperCamelCase_											=				self.get_config()

									return config, pixel_values, labels

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				SwinModel(config=lowercase__      )
									model.to(lowercase__      )
									model.eval()
									UpperCamelCase_											=				model(lowercase__      )

									UpperCamelCase_											=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
									UpperCamelCase_											=				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       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				SwinBackbone(config=lowercase__      )
									model.to(lowercase__      )
									model.eval()
									UpperCamelCase_											=				model(lowercase__      )

									# 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
									UpperCamelCase_											=				None
									UpperCamelCase_											=				SwinBackbone(config=lowercase__      )
									model.to(lowercase__      )
									model.eval()
									UpperCamelCase_											=				model(lowercase__      )

									# 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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

									# test greyscale images
									UpperCamelCase_											=				1
									UpperCamelCase_											=				SwinForMaskedImageModeling(lowercase__      )
									model.to(lowercase__      )
									model.eval()

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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

									# test greyscale images
									UpperCamelCase_											=				1
									UpperCamelCase_											=				SwinForImageClassification(lowercase__      )
									model.to(lowercase__      )
									model.eval()

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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




@require_torch
class  _lowercase				(_UpperCAmelCase ,				_UpperCAmelCase ,				unittest.TestCase ):
				'''simple docstring'''







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

				lowercase__				  =  False
				lowercase__				  =  False
				lowercase__				  =  False

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				SwinModelTester(self      )
									UpperCamelCase_											=				ConfigTester(self       ,	config_class=lowercase__       ,	embed_dim=37      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									return

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."      )
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									pass

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									super().test_training_gradient_checkpointing()

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowercase__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				@unittest.skip(reason="Swin does not use inputs_embeds"      )
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									pass

				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"      )
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									pass

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_											=				True

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

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

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

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

														# also another +1 for reshaped_hidden_states
														UpperCamelCase_											=				1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states       ,	len(lowercase__      )      )

														UpperCamelCase_											=				outputs.attentions

														self.assertEqual(len(lowercase__      )       ,	lowercase__      )

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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

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

									UpperCamelCase_											=				outputs.hidden_states

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

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

									UpperCamelCase_											=				(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":
														UpperCamelCase_											=				outputs.reshaped_hidden_states
														self.assertEqual(len(lowercase__      )       ,	lowercase__      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

														self.check_hidden_states_output(lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_											=				3

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

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

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

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

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_											=				SwinModel.from_pretrained(lowercase__      )
														self.assertIsNotNone(lowercase__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

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







				@cached_property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									return (
									    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"      )
									    if is_vision_available()
									    else None
									)

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"      ).to(lowercase__      )
									UpperCamelCase_											=				self.default_image_processor

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

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

									# verify the logits
									UpperCamelCase_											=				torch.Size((1, 1000)      )
									self.assertEqual(outputs.logits.shape       ,	lowercase__      )
									UpperCamelCase_											=				torch.tensor([-0.0_948, -0.6_454, -0.0_921]      ).to(lowercase__      )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	lowercase__       ,	atol=1e-4      )      )




@require_torch
class  _lowercase				(unittest.TestCase ,				_UpperCAmelCase ):
				'''simple docstring'''







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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				SwinModelTester(self      )





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
	import torch

UpperCAmelCase				:							Any								=logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
	from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



	def 				_lowerCAmelCase      (cls):
						return cls



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		):
					UpperCamelCase_											=				device
					UpperCamelCase_											=				shape[0]

					UpperCamelCase_											=				layout or torch.strided
					UpperCamelCase_											=				device or torch.device("cpu")

					if generator is not None:
										UpperCamelCase_											=				generator.device.type if not isinstance(_lowerCAmelCase				,		_lowerCAmelCase) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															UpperCamelCase_											=				"cpu"
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device.""")
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}.""")

					if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				(1,) + shape[1:]
										UpperCamelCase_											=				[
										    torch.randn(_lowerCAmelCase				,		generator=generator[i]				,		device=_lowerCAmelCase				,		dtype=_lowerCAmelCase				,		layout=_lowerCAmelCase)
										    for i in range(_lowerCAmelCase)
										]
										UpperCamelCase_											=				torch.cat(_lowerCAmelCase				,		dim=0).to(_lowerCAmelCase)
					else:
										UpperCamelCase_											=				torch.randn(_lowerCAmelCase				,		generator=_lowerCAmelCase				,		device=_lowerCAmelCase				,		dtype=_lowerCAmelCase				,		layout=_lowerCAmelCase).to(_lowerCAmelCase)

					return latents






def 				_lowerCAmelCase      (_lowerCAmelCase):
					if is_torch_version("<"				,		"2.0.0") or not hasattr(_lowerCAmelCase				,		"_dynamo"):
										return False
					return isinstance(_lowerCAmelCase				,		torch._dynamo.eval_frame.OptimizedModule)
