




import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
			import jax

			from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
			from PIL import Image

			from transformers import BeitImageProcessor


class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def __init__( self			:   Tuple       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Dict=100       ,						lowerCamelCase_			:   List[Any]=13       ,						lowerCamelCase_			:   int=30       ,						lowerCamelCase_			:   List[Any]=2       ,						lowerCamelCase_			:   Tuple=3       ,						lowerCamelCase_			:   Union[str, Any]=True       ,						lowerCamelCase_			:   List[str]=True       ,						lowerCamelCase_			:   List[str]=32       ,						lowerCamelCase_			:   Optional[int]=5       ,						lowerCamelCase_			:   Tuple=4       ,						lowerCamelCase_			:   Union[str, Any]=37       ,						lowerCamelCase_			:   List[str]="gelu"       ,						lowerCamelCase_			:   str=0.1       ,						lowerCamelCase_			:   Tuple=0.1       ,						lowerCamelCase_			:   Dict=10       ,						lowerCamelCase_			:   Union[str, Any]=0.0_2       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						)						->   str:
								__magic_name__							:	int    							=							parent
								__magic_name__							:	List[str]    							=							vocab_size
								__magic_name__							:	Union[str, Any]    							=							batch_size
								__magic_name__							:	Union[str, Any]    							=							image_size
								__magic_name__							:	Any    							=							patch_size
								__magic_name__							:	Tuple    							=							num_channels
								__magic_name__							:	Optional[int]    							=							is_training
								__magic_name__							:	int    							=							use_labels
								__magic_name__							:	List[str]    							=							hidden_size
								__magic_name__							:	Dict    							=							num_hidden_layers
								__magic_name__							:	Dict    							=							num_attention_heads
								__magic_name__							:	List[str]    							=							intermediate_size
								__magic_name__							:	Dict    							=							hidden_act
								__magic_name__							:	Any    							=							hidden_dropout_prob
								__magic_name__							:	str    							=							attention_probs_dropout_prob
								__magic_name__							:	Tuple    							=							type_sequence_label_size
								__magic_name__							:	Any    							=							initializer_range

								# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__magic_name__							:	Dict    							=							(image_size // patch_size) ** 2
								__magic_name__							:	str    							=							num_patches + 1

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   str:
								__magic_name__							:	Tuple    							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								__magic_name__							:	Optional[int]    							=							None
								if self.use_labels:
													__magic_name__							:	List[str]    							=							ids_tensor([self.batch_size]       ,						self.type_sequence_label_size  )

								__magic_name__							:	List[Any]    							=							BeitConfig(
								    vocab_size=self.vocab_size       ,						image_size=self.image_size       ,						patch_size=self.patch_size       ,						num_channels=self.num_channels       ,						hidden_size=self.hidden_size       ,						num_hidden_layers=self.num_hidden_layers       ,						num_attention_heads=self.num_attention_heads       ,						intermediate_size=self.intermediate_size       ,						hidden_act=self.hidden_act       ,						hidden_dropout_prob=self.hidden_dropout_prob       ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,						is_decoder=lowerCamelCase_       ,						initializer_range=self.initializer_range       ,						)

								return config, pixel_values, labels

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Tuple:
								__magic_name__							:	Union[str, Any]    							=							FlaxBeitModel(config=lowerCamelCase_  )
								__magic_name__							:	int    							=							model(lowerCamelCase_  )
								self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, self.seq_length, self.hidden_size)  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Any:
								__magic_name__							:	Dict    							=							FlaxBeitForMaskedImageModeling(config=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							model(lowerCamelCase_  )
								self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length - 1, self.vocab_size)  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Any  )						->   str:
								__magic_name__							:	int    							=							self.type_sequence_label_size
								__magic_name__							:	Dict    							=							FlaxBeitForImageClassification(config=lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							model(lowerCamelCase_  )
								self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.type_sequence_label_size)  )

								# test greyscale images
								__magic_name__							:	str    							=							1
								__magic_name__							:	Union[str, Any]    							=							FlaxBeitForImageClassification(lowerCamelCase_  )

								__magic_name__							:	Any    							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								__magic_name__							:	int    							=							model(lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   str:
								__magic_name__							:	List[str]    							=							self.prepare_config_and_inputs()
								(
								    __magic_name__    
								)							:	Union[str, Any]    							=							config_and_inputs
								__magic_name__							:	str    							=							{'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_flax
class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=(
			    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
			)

			def 							UpperCAmelCase__       ( self			:   str  )						->   None:
								__magic_name__							:	List[str]    							=							FlaxBeitModelTester(self  )
								__magic_name__							:	str    							=							ConfigTester(self       ,						config_class=lowerCamelCase_       ,						has_text_modality=lowerCamelCase_       ,						hidden_size=37  )

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Tuple:
								self.config_tester.run_common_tests()

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   int:
								__magic_name__							:	Any    							=							self.model_tester.prepare_config_and_inputs_for_common()

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

													__magic_name__							:	Union[str, Any]    							=							['''pixel_values''']
													self.assertListEqual(arg_names[:1]       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Union[str, Any]:
								__magic_name__							:	Optional[Any]    							=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																		__magic_name__							:	str    							=							self._prepare_for_class(lowerCamelCase_       ,						lowerCamelCase_  )
																		__magic_name__							:	Dict    							=							model_class(lowerCamelCase_  )

																		@jax.jit
																		def model_jitted(lowerCamelCase_			:   Tuple       ,						**lowerCamelCase_			:   Optional[Any]  ):
																							return model(pixel_values=lowerCamelCase_       ,						**lowerCamelCase_  )

																		with self.subTest('''JIT Enabled'''  ):
																							__magic_name__							:	Any    							=							model_jitted(**lowerCamelCase_  ).to_tuple()

																		with self.subTest('''JIT Disabled'''  ):
																							with jax.disable_jit():
																												__magic_name__							:	int    							=							model_jitted(**lowerCamelCase_  ).to_tuple()

																		self.assertEqual(len(lowerCamelCase_  )       ,						len(lowerCamelCase_  )  )
																		for jitted_output, output in zip(lowerCamelCase_       ,						lowerCamelCase_  ):
																							self.assertEqual(jitted_output.shape       ,						output.shape  )

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   int:
								__magic_name__							:	List[str]    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								__magic_name__							:	List[Any]    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[Any]:
								__magic_name__							:	List[Any]    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_  )


			@slow
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								for model_class_name in self.all_model_classes:
													__magic_name__							:	Dict    							=							model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''  )
													__magic_name__							:	Dict    							=							model(np.ones((1, 3, 224, 224)  )  )
													self.assertIsNotNone(lowerCamelCase_  )







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
					return image


@require_vision
@require_flax
class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			@cached_property
			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   List[Any]:
								return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''  ) if is_vision_available() else None

			@slow
			def 							UpperCAmelCase__       ( self			:   Any  )						->   Union[str, Any]:
								__magic_name__							:	List[str]    							=							FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''  )

								__magic_name__							:	Union[str, Any]    							=							self.default_image_processor
								__magic_name__							:	List[str]    							=							prepare_img()
								__magic_name__							:	List[str]    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''np'''  ).pixel_values

								# prepare bool_masked_pos
								__magic_name__							:	Optional[Any]    							=							np.ones((1, 196)       ,						dtype=lowerCamelCase_  )

								# forward pass
								__magic_name__							:	int    							=							model(pixel_values=lowerCamelCase_       ,						bool_masked_pos=lowerCamelCase_  )
								__magic_name__							:	str    							=							outputs.logits

								# verify the logits
								__magic_name__							:	str    							=							(1, 196, 8192)
								self.assertEqual(logits.shape       ,						lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							np.array(
								    [[-3.2_4_3_7, 0.5_0_7_2, -13.9174], [-3.2_4_5_6, 0.4_9_4_8, -13.9401], [-3.2_0_3_3, 0.5_1_2_1, -13.8550]]  )

								self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]       ,						lowerCamelCase_       ,						atol=1E-2  )  )

			@slow
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   str:
								__magic_name__							:	List[str]    							=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''  )

								__magic_name__							:	Tuple    							=							self.default_image_processor
								__magic_name__							:	List[Any]    							=							prepare_img()
								__magic_name__							:	List[Any]    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''np'''  )

								# forward pass
								__magic_name__							:	Dict    							=							model(**lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							outputs.logits

								# verify the logits
								__magic_name__							:	Tuple    							=							(1, 1000)
								self.assertEqual(logits.shape       ,						lowerCamelCase_  )

								__magic_name__							:	Any    							=							np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]  )

								self.assertTrue(np.allclose(logits[0, :3]       ,						lowerCamelCase_       ,						atol=1E-4  )  )

								__magic_name__							:	int    							=							281
								self.assertEqual(logits.argmax(-1  ).item()       ,						lowerCamelCase_  )


			@slow
			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	Optional[int]    							=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''  )

								__magic_name__							:	Dict    							=							self.default_image_processor
								__magic_name__							:	Union[str, Any]    							=							prepare_img()
								__magic_name__							:	Union[str, Any]    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''np'''  )

								# forward pass
								__magic_name__							:	str    							=							model(**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							outputs.logits

								# verify the logits
								__magic_name__							:	Union[str, Any]    							=							(1, 21841)
								self.assertEqual(logits.shape       ,						lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]  )

								self.assertTrue(np.allclose(logits[0, :3]       ,						lowerCamelCase_       ,						atol=1E-4  )  )

								__magic_name__							:	Optional[Any]    							=							2396
								self.assertEqual(logits.argmax(-1  ).item()       ,						lowerCamelCase_  )









from typing import TYPE_CHECKING

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


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



