





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 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	Union[str, Any],				lowerCamelCase_:	int,				lowerCamelCase_:	Tuple=13,				lowerCamelCase_:	List[str]=32,				lowerCamelCase_:	Optional[int]=2,				lowerCamelCase_:	Dict=3,				lowerCamelCase_:	List[Any]=16,				lowerCamelCase_:	int=[1, 2, 1],				lowerCamelCase_:	List[Any]=[2, 2, 4],				lowerCamelCase_:	List[str]=2,				lowerCamelCase_:	List[Any]=2.0,				lowerCamelCase_:	Any=True,				lowerCamelCase_:	Dict=0.0,				lowerCamelCase_:	List[Any]=0.0,				lowerCamelCase_:	Union[str, Any]=0.1,				lowerCamelCase_:	Optional[Any]="gelu",				lowerCamelCase_:	Optional[int]=False,				lowerCamelCase_:	int=True,				lowerCamelCase_:	Dict=0.0_2,				lowerCamelCase_:	Optional[Any]=1E-5,				lowerCamelCase_:	Union[str, Any]=True,				lowerCamelCase_:	int=None,				lowerCamelCase_:	str=True,				lowerCamelCase_:	List[Any]=10,				lowerCamelCase_:	Dict=8,				):
			lowercase__				:			Dict            =		parent
			lowercase__				:			int            =		batch_size
			lowercase__				:			str            =		image_size
			lowercase__				:			List[Any]            =		patch_size
			lowercase__				:			Any            =		num_channels
			lowercase__				:			int            =		embed_dim
			lowercase__				:			Optional[Any]            =		depths
			lowercase__				:			Optional[Any]            =		num_heads
			lowercase__				:			List[Any]            =		window_size
			lowercase__				:			Dict            =		mlp_ratio
			lowercase__				:			Any            =		qkv_bias
			lowercase__				:			Any            =		hidden_dropout_prob
			lowercase__				:			Union[str, Any]            =		attention_probs_dropout_prob
			lowercase__				:			str            =		drop_path_rate
			lowercase__				:			Dict            =		hidden_act
			lowercase__				:			List[str]            =		use_absolute_embeddings
			lowercase__				:			str            =		patch_norm
			lowercase__				:			List[str]            =		layer_norm_eps
			lowercase__				:			Optional[Any]            =		initializer_range
			lowercase__				:			str            =		is_training
			lowercase__				:			Any            =		scope
			lowercase__				:			Union[str, Any]            =		use_labels
			lowercase__				:			Optional[int]            =		type_sequence_label_size
			lowercase__				:			Optional[Any]            =		encoder_stride

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			str            =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

			lowercase__				:			int            =		self.get_config()

			return config, pixel_values, labels

	def       snake_case__(	self:	List[str] ):
			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       snake_case__(	self:	str,				lowerCamelCase_:	Tuple,				lowerCamelCase_:	List[str],				lowerCamelCase_:	Tuple ):
			lowercase__				:			Any            =		TFSwinModel(config=__a )
			lowercase__				:			List[str]            =		model(__a )

			lowercase__				:			Dict            =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
			lowercase__				:			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       snake_case__(	self:	int,				lowerCamelCase_:	List[Any],				lowerCamelCase_:	int,				lowerCamelCase_:	List[Any] ):
			lowercase__				:			Any            =		TFSwinForMaskedImageModeling(config=__a )
			lowercase__				:			Tuple            =		model(__a )
			self.parent.assertEqual(
			    result.logits.shape,				(self.batch_size, self.num_channels, self.image_size, self.image_size) )

			# test greyscale images
			lowercase__				:			str            =		1
			lowercase__				:			Union[str, Any]            =		TFSwinForMaskedImageModeling(__a )

			lowercase__				:			Optional[int]            =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
			lowercase__				:			Any            =		model(__a )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, 1, self.image_size, self.image_size) )

	def       snake_case__(	self:	List[str],				lowerCamelCase_:	str,				lowerCamelCase_:	Dict,				lowerCamelCase_:	Tuple ):
			lowercase__				:			Dict            =		self.type_sequence_label_size
			lowercase__				:			Tuple            =		TFSwinForImageClassification(__a )
			lowercase__				:			Any            =		model(__a,				labels=__a )
			self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.type_sequence_label_size) )

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





	def       snake_case__(	self:	int ):
			lowercase__				:			List[Any]            =		self.prepare_config_and_inputs()
			lowercase__				:			Tuple            =		config_and_inputs
			lowercase__				:			Dict            =		{'pixel_values': pixel_values}
			return config, inputs_dict


@require_tf
class 			__lowerCAmelCase				(					lowerCAmelCase_      ,						lowerCAmelCase_      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							(
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	_A       	=							(
	    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_A       	=							False
	_A       	=							False
	_A       	=							False
	_A       	=							False

	def       snake_case__(	self:	int ):
			lowercase__				:			Optional[int]            =		TFSwinModelTester(self )
			lowercase__				:			Union[str, Any]            =		ConfigTester(self,				config_class=__a,				embed_dim=37 )

	def       snake_case__(	self:	Optional[Any] ):
			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       snake_case__(	self:	List[Any] ):
			return

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			List[str]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__a )

	def       snake_case__(	self:	Any ):
			lowercase__				:			List[Any]            =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*__a )

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Optional[Any]            =		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       snake_case__(	self:	Tuple ):
			pass

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			int            =		self.model_tester.prepare_config_and_inputs_for_common()

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

	def       snake_case__(	self:	Tuple ):
			lowercase__				:			Dict            =		self.model_tester.prepare_config_and_inputs_for_common()

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

					lowercase__				:			str            =		['pixel_values']
					self.assertListEqual(arg_names[:1],				__a )

	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			Any            =		self.model_tester.prepare_config_and_inputs_for_common()
			lowercase__				:			Optional[int]            =		True

			for model_class in self.all_model_classes:
					lowercase__				:			Optional[int]            =		True
					lowercase__				:			Dict            =		False
					lowercase__				:			Any            =		True
					lowercase__				:			Tuple            =		model_class(__a )
					lowercase__				:			List[str]            =		model(**self._prepare_for_class(__a,				__a ) )
					lowercase__				:			int            =		outputs.attentions
					lowercase__				:			List[Any]            =		len(self.model_tester.depths )
					self.assertEqual(len(__a ),				__a )

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					lowercase__				:			Any            =		True
					lowercase__				:			List[Any]            =		config.window_size**2
					lowercase__				:			Optional[Any]            =		model_class(__a )
					lowercase__				:			Tuple            =		model(**self._prepare_for_class(__a,				__a ) )
					lowercase__				:			str            =		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],				)
					lowercase__				:			Any            =		len(__a )

					# Check attention is always last and order is fine
					lowercase__				:			int            =		True
					lowercase__				:			Any            =		True
					lowercase__				:			str            =		model_class(__a )
					lowercase__				:			str            =		model(**self._prepare_for_class(__a,				__a ) )

					if hasattr(self.model_tester,				'num_hidden_states_types' ):
							lowercase__				:			Optional[int]            =		self.model_tester.num_hidden_states_types
					else:
							# also another +1 for reshaped_hidden_states
							lowercase__				:			Union[str, Any]            =		2
					self.assertEqual(out_len + added_hidden_states,				len(__a ) )

					lowercase__				:			Dict            =		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       snake_case__(	self:	str,				lowerCamelCase_:	str,				lowerCamelCase_:	Dict,				lowerCamelCase_:	Dict,				lowerCamelCase_:	str ):
			lowercase__				:			Optional[Any]            =		model_class(__a )
			lowercase__				:			int            =		model(**self._prepare_for_class(__a,				__a ) )
			lowercase__				:			Tuple            =		outputs.hidden_states

			lowercase__				:			Any            =		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
			lowercase__				:			Optional[int]            =		to_atuple(config.patch_size )

			lowercase__				:			List[str]            =		(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__				:			str            =		outputs.reshaped_hidden_states
			self.assertEqual(len(__a ),				__a )

			lowercase__				:			str            =		reshaped_hidden_states[0].shape

			lowercase__				:			int            =		tf.reshape(reshaped_hidden_states[0],				(batch_size, num_channels, height * width) )
			lowercase__				:			List[str]            =		tf.transpose(__a,				(0, 2, 1) )

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

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			Optional[Any]            =		self.model_tester.prepare_config_and_inputs_for_common()

			lowercase__				:			Union[str, Any]            =		to_atuple(self.model_tester.image_size )

			for model_class in self.all_model_classes:
					lowercase__				:			Optional[int]            =		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"]
					lowercase__				:			Tuple            =		True

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

	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[str]            =		self.model_tester.prepare_config_and_inputs_for_common()
			lowercase__				:			List[str]            =		3

			lowercase__				:			Any            =		to_atuple(self.model_tester.image_size )
			lowercase__				:			Any            =		to_atuple(config.patch_size )

			lowercase__				:			Any            =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			lowercase__				:			Optional[int]            =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes:
					lowercase__				:			Union[str, Any]            =		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"]
					lowercase__				:			int            =		True
					self.check_hidden_states_output(__a,				__a,				__a,				(padded_height, padded_width) )





	@slow
	def       snake_case__(	self:	List[str] ):





			for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowercase__				:			List[Any]            =		TFSwinModel.from_pretrained(__a )
					self.assertIsNotNone(__a )


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


	'''simple docstring'''





	@cached_property
	def       snake_case__(	self:	Optional[int] ):
			return (
			    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224' )
			    if is_vision_available()
			    else None
			)





	@slow
	def       snake_case__(	self:	Any ):
			lowercase__				:			Optional[Any]            =		TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224' )
			lowercase__				:			str            =		self.default_image_processor

			lowercase__				:			Optional[Any]            =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			lowercase__				:			Optional[Any]            =		image_processor(images=__a,				return_tensors='tf' )

			# forward pass
			lowercase__				:			List[Any]            =		model(__a )

			# verify the logits
			lowercase__				:			List[str]            =		tf.TensorShape((1, 1000) )
			self.assertEqual(outputs.logits.shape,				__a )
			lowercase__				:			Optional[Any]            =		tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1] )
			self.assertTrue(np.allclose(outputs.logits[0, :3],				__a,				atol=1E-4 ) )









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


