


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.swin.modeling_tf_swin import (
	    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFSwinForImageClassification,
	    TFSwinForMaskedImageModeling,
	    TFSwinModel,
	)


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class 					snake_case__  :





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






								a_       :   Optional[Any]			=	parent
								a_       :   List[Any]			=	batch_size
								a_       :   Optional[Any]			=	image_size
								a_       :   Dict			=	patch_size
								a_       :   Optional[Any]			=	num_channels
								a_       :   Union[str, Any]			=	embed_dim
								a_       :   Dict			=	depths
								a_       :   Optional[Any]			=	num_heads
								a_       :   Tuple			=	window_size
								a_       :   List[str]			=	mlp_ratio
								a_       :   Optional[int]			=	qkv_bias
								a_       :   Optional[int]			=	hidden_dropout_prob
								a_       :   List[str]			=	attention_probs_dropout_prob
								a_       :   Any			=	drop_path_rate
								a_       :   Union[str, Any]			=	hidden_act
								a_       :   Tuple			=	use_absolute_embeddings
								a_       :   Tuple			=	patch_norm
								a_       :   List[str]			=	layer_norm_eps
								a_       :   Optional[Any]			=	initializer_range
								a_       :   Optional[Any]			=	is_training
								a_       :   str			=	scope
								a_       :   Optional[Any]			=	use_labels
								a_       :   Optional[int]			=	type_sequence_label_size
								a_       :   List[str]			=	encoder_stride





	def     A				(  self							)		->			List[str]:
								"""simple docstring"""






								a_       :   List[Any]			=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								a_       :   Dict			=	None
								if self.use_labels:
															a_       :   List[str]			=	ids_tensor([self.batch_size]				,    self.type_sequence_label_size							)

								a_       :   Tuple			=	self.get_config()

								return config, pixel_values, labels





	def     A				(  self							)		->			int:
								"""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				,    )





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Dict			=	TFSwinModel(config=UpperCamelCase_							)
								a_       :   Optional[Any]			=	model(UpperCamelCase_							)

								a_       :   List[Any]			=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
								a_       :   Any			=	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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			List[str]:
								"""simple docstring"""






								a_       :   List[Any]			=	TFSwinForMaskedImageModeling(config=UpperCamelCase_							)
								a_       :   Tuple			=	model(UpperCamelCase_							)
								self.parent.assertEqual(
								    result.logits.shape				,    (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

								# test greyscale images
								a_       :   List[str]			=	1
								a_       :   List[str]			=	TFSwinForMaskedImageModeling(UpperCamelCase_							)

								a_       :   List[str]			=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								a_       :   Optional[int]			=	model(UpperCamelCase_							)
								self.parent.assertEqual(result.logits.shape				,    (self.batch_size, 1, self.image_size, self.image_size)							)





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Dict:
								"""simple docstring"""






								a_       :   Optional[int]			=	self.type_sequence_label_size
								a_       :   List[str]			=	TFSwinForImageClassification(UpperCamelCase_							)
								a_       :   Union[str, Any]			=	model(UpperCamelCase_				,    labels=UpperCamelCase_							)
								self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)							)

								# test greyscale images
								a_       :   Optional[Any]			=	1
								a_       :   Tuple			=	TFSwinForImageClassification(UpperCamelCase_							)
								a_       :   List[Any]			=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								a_       :   int			=	model(UpperCamelCase_							)
								self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)							)





	def     A				(  self							)		->			Dict:
								"""simple docstring"""






								a_       :   Dict			=	self.prepare_config_and_inputs()
								a_		,				a_		,				a_       :   Optional[int]			=	config_and_inputs
								a_       :   Tuple			=	{"""pixel_values""": pixel_values}
								return config, inputs_dict





@require_tf
class 					snake_case__  ( __A      ,    __A      ,    unittest.TestCase       ):
	UpperCAmelCase   :							int         = (
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	UpperCAmelCase   :							List[Any]         = (
	    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCAmelCase   :							Optional[int]         = False
	UpperCAmelCase   :							Dict         = False
	UpperCAmelCase   :							List[str]         = False
	UpperCAmelCase   :							List[str]         = False





	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								a_       :   str			=	TFSwinModelTester(self							)
								a_       :   Dict			=	ConfigTester(self				,    config_class=UpperCamelCase_				,    embed_dim=37							)





	def     A				(  self							)		->			Dict:
								"""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     A				(  self							)		->			Dict:
								"""simple docstring"""






								return





	def     A				(  self							)		->			int:
								"""simple docstring"""






								a_       :   Tuple			=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase_							)





	def     A				(  self							)		->			Optional[Any]:
								"""simple docstring"""






								a_       :   Optional[Any]			=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)





	def     A				(  self							)		->			Any:
								"""simple docstring"""






								a_       :   Optional[Any]			=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_							)





	@unittest.skip(reason="""Swin does not use inputs_embeds"""							)
	def     A				(  self							)		->			Optional[Any]:
								"""simple docstring"""






								pass





	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								a_		,				a_       :   Optional[int]			=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															a_       :   Dict			=	model_class(UpperCamelCase_							)
															self.assertIsInstance(model.get_input_embeddings()				,    tf.keras.layers.Layer							)
															a_       :   Any			=	model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(UpperCamelCase_				,    tf.keras.layers.Dense							)							)





	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								a_		,				a_       :   int			=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															a_       :   Union[str, Any]			=	model_class(UpperCamelCase_							)
															a_       :   List[Any]			=	inspect.signature(model.call							)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															a_       :   Tuple			=	[*signature.parameters.keys()]

															a_       :   List[str]			=	["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,    UpperCamelCase_							)





	def     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_		,				a_       :   int			=	self.model_tester.prepare_config_and_inputs_for_common()
								a_       :   int			=	True

								for model_class in self.all_model_classes:
															a_       :   Union[str, Any]			=	True
															a_       :   Dict			=	False
															a_       :   List[Any]			=	True
															a_       :   str			=	model_class(UpperCamelCase_							)
															a_       :   Union[str, Any]			=	model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_							)							)
															a_       :   List[str]			=	outputs.attentions
															a_       :   Dict			=	len(self.model_tester.depths							)
															self.assertEqual(len(UpperCamelCase_							)				,    UpperCamelCase_							)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															a_       :   Dict			=	True
															a_       :   Union[str, Any]			=	config.window_size**2
															a_       :   List[str]			=	model_class(UpperCamelCase_							)
															a_       :   int			=	model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_							)							)
															a_       :   int			=	outputs.attentions
															self.assertEqual(len(UpperCamelCase_							)				,    UpperCamelCase_							)

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

															# Check attention is always last and order is fine
															a_       :   Optional[Any]			=	True
															a_       :   Union[str, Any]			=	True
															a_       :   int			=	model_class(UpperCamelCase_							)
															a_       :   Dict			=	model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_							)							)

															if hasattr(self.model_tester				,    """num_hidden_states_types"""							):
																						a_       :   List[Any]			=	self.model_tester.num_hidden_states_types
															else:
																						# also another +1 for reshaped_hidden_states
																						a_       :   Dict			=	2
															self.assertEqual(out_len + added_hidden_states				,    len(UpperCamelCase_							)							)

															a_       :   str			=	outputs.attentions

															self.assertEqual(len(UpperCamelCase_							)				,    UpperCamelCase_							)

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





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								a_       :   str			=	model_class(UpperCamelCase_							)
								a_       :   str			=	model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_							)							)
								a_       :   List[Any]			=	outputs.hidden_states

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

								# Swin has a different seq_length
								a_       :   Optional[Any]			=	to_atuple(config.patch_size							)

								a_       :   Any			=	(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]				,    )

								a_       :   int			=	outputs.reshaped_hidden_states
								self.assertEqual(len(UpperCamelCase_							)				,    UpperCamelCase_							)

								a_		,				a_		,				a_		,				a_       :   List[str]			=	reshaped_hidden_states[0].shape

								a_       :   Union[str, Any]			=	tf.reshape(reshaped_hidden_states[0]				,    (batch_size, num_channels, height * width)							)
								a_       :   List[str]			=	tf.transpose(UpperCamelCase_				,    (0, 2, 1)							)

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





	def     A				(  self							)		->			Optional[int]:
								"""simple docstring"""






								a_		,				a_       :   Dict			=	self.model_tester.prepare_config_and_inputs_for_common()

								a_       :   Optional[int]			=	to_atuple(self.model_tester.image_size							)

								for model_class in self.all_model_classes:
															a_       :   List[Any]			=	True
															self.check_hidden_states_output(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															a_       :   Any			=	True

															self.check_hidden_states_output(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)





	def     A				(  self							)		->			List[str]:
								"""simple docstring"""






								a_		,				a_       :   int			=	self.model_tester.prepare_config_and_inputs_for_common()
								a_       :   Union[str, Any]			=	3

								a_       :   Union[str, Any]			=	to_atuple(self.model_tester.image_size							)
								a_       :   Optional[Any]			=	to_atuple(config.patch_size							)

								a_       :   Union[str, Any]			=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								a_       :   int			=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
															a_       :   List[str]			=	True
															self.check_hidden_states_output(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    (padded_height, padded_width)							)

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





	@slow
	def     A				(  self							)		->			Optional[int]:
								"""simple docstring"""






								for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a_       :   Tuple			=	TFSwinModel.from_pretrained(UpperCamelCase_							)
															self.assertIsNotNone(UpperCamelCase_							)





@require_vision
@require_tf
class 					snake_case__  ( unittest.TestCase       ):





	@cached_property
	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






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





	@slow
	def     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
								a_       :   List[str]			=	self.default_image_processor

								a_       :   Any			=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								a_       :   Tuple			=	image_processor(images=UpperCamelCase_				,    return_tensors="""tf"""							)

								# forward pass
								a_       :   int			=	model(UpperCamelCase_							)

								# verify the logits
								a_       :   Union[str, Any]			=	tf.TensorShape((1, 1000)							)
								self.assertEqual(outputs.logits.shape				,    UpperCamelCase_							)
								a_       :   str			=	tf.constant([-0.0948, -0.6454, -0.0921]							)
								self.assertTrue(np.allclose(outputs.logits[0, :3]				,    UpperCamelCase_				,    atol=1e-4							)							)






from __future__ import annotations





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):






							"""simple docstring"""



							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	len(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	length >> 1
							return (
							    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
							    if length % 2 == 0
							    else sorted_list[mid_index]
							)





def 						_lowerCamelCase		(							):






							"""simple docstring"""



							import doctest

							doctest.testmod()


if __name__ == "__main__":
	main()


