






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				:	Union[str, Any]		,     __a				:	List[str]		,     __a				:	List[Any]=100		,     __a				:	Any=13		,     __a				:	Dict=30		,     __a				:	Optional[int]=2		,     __a				:	str=3		,     __a				:	Tuple=True		,     __a				:	str=True		,     __a				:	Union[str, Any]=32		,     __a				:	int=5		,     __a				:	List[Any]=4		,     __a				:	Optional[Any]=37		,     __a				:	Any="gelu"		,     __a				:	List[str]=0.1		,     __a				:	int=0.1		,     __a				:	Tuple=10		,     __a				:	int=0.02		,     __a				:	Tuple=3		,     )			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     parent
												__lowercase      :							Union[str, Any]				=     vocab_size
												__lowercase      :							str				=     batch_size
												__lowercase      :							Optional[Any]				=     image_size
												__lowercase      :							Dict				=     patch_size
												__lowercase      :							Any				=     num_channels
												__lowercase      :							List[Any]				=     is_training
												__lowercase      :							Tuple				=     use_labels
												__lowercase      :							Optional[Any]				=     hidden_size
												__lowercase      :							str				=     num_hidden_layers
												__lowercase      :							Optional[Any]				=     num_attention_heads
												__lowercase      :							Union[str, Any]				=     intermediate_size
												__lowercase      :							int				=     hidden_act
												__lowercase      :							Optional[int]				=     hidden_dropout_prob
												__lowercase      :							Optional[Any]				=     attention_probs_dropout_prob
												__lowercase      :							Optional[Any]				=     type_sequence_label_size
												__lowercase      :							Any				=     initializer_range

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



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  List[str]:


												"""simple docstring"""

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

												__lowercase      :							Optional[Any]				=     None
												if self.use_labels:
																			__lowercase      :							Optional[Any]				=     ids_tensor([self.batch_size]		,     self.type_sequence_label_size							)

												__lowercase      :							str				=     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=A_		,     initializer_range=self.initializer_range		,     )

												return config, pixel_values, labels



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str		,     __a				:	str		,     __a				:	List[str]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     FlaxBeitModel(config=A_							)
												__lowercase      :							str				=     model(A_							)
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Any		,     __a				:	Any		,     __a				:	str							)			->  str:


												"""simple docstring"""

												__lowercase      :							str				=     FlaxBeitForMaskedImageModeling(config=A_							)
												__lowercase      :							List[str]				=     model(A_							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length - 1, self.vocab_size)							)



					def       lowerCAmelCase				(  self				:	str		,     __a				:	List[Any]		,     __a				:	Optional[int]		,     __a				:	List[str]							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.type_sequence_label_size
												__lowercase      :							Dict				=     FlaxBeitForImageClassification(config=A_							)
												__lowercase      :							Union[str, Any]				=     model(A_							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)							)

												# test greyscale images
												__lowercase      :							Optional[int]				=     1
												__lowercase      :							List[Any]				=     FlaxBeitForImageClassification(A_							)

												__lowercase      :							Dict				=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
												__lowercase      :							Optional[Any]				=     model(A_							)



					def       lowerCAmelCase				(  self				:	Dict							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.prepare_config_and_inputs()
												(
												    (
												    __lowercase	
												)			,			(
												    __lowercase	
												)			,			(
												    __lowercase	
												)			,				
												)      :							List[str]				=     config_and_inputs
												__lowercase      :							Dict				=     {"""pixel_values""": pixel_values}
												return config, inputs_dict







@require_flax
class 		lowerCAmelCase     ( _UpperCAmelCase      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Dict									=      (
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  None:


												"""simple docstring"""

												__lowercase      :							Tuple				=     FlaxBeitModelTester(self							)
												__lowercase      :							Any				=     ConfigTester(self		,     config_class=A_		,     has_text_modality=A_		,     hidden_size=37							)



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, Any]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

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

												for model_class in self.all_model_classes:
																			__lowercase      :							Tuple				=     model_class(A_							)
																			__lowercase      :							Dict				=     inspect.signature(model.__call__							)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__lowercase      :							Tuple				=     [*signature.parameters.keys()]

																			__lowercase      :							Optional[Any]				=     ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]		,     A_							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  int:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Any				=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__							):
																										__lowercase      :							List[str]				=     self._prepare_for_class(A_		,     A_							)
																										__lowercase      :							Union[str, Any]				=     model_class(A_							)

																										@jax.jit
																										def model_jitted(__a				:	Union[str, Any]		,     **__a				:	Union[str, Any]							):
																																	return model(pixel_values=A_		,     **A_							)

																										with self.subTest("""JIT Enabled"""							):
																																	__lowercase      :							Any				=     model_jitted(**A_							).to_tuple()

																										with self.subTest("""JIT Disabled"""							):
																																	with jax.disable_jit():
																																								__lowercase      :							Optional[Any]				=     model_jitted(**A_							).to_tuple()

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



					def       lowerCAmelCase				(  self				:	str							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A_							)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*A_							)



					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A_							)



					@slow
					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

												for model_class_name in self.all_model_classes:
																			__lowercase      :							Optional[Any]				=     model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""							)
																			__lowercase      :							str				=     model(np.ones((1, 3, 224, 224)							)							)
																			self.assertIsNotNone(A_							)







def       snake_case_		(     ):
							__lowercase      :							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       lowerCAmelCase				(  self				:	str							)			->  str:


												"""simple docstring"""

												return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""							) if is_vision_available() else None



					@slow
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							)

												__lowercase      :							List[Any]				=     self.default_image_processor
												__lowercase      :							List[Any]				=     prepare_img()
												__lowercase      :							List[str]				=     image_processor(images=A_		,     return_tensors="""np"""							).pixel_values

												# prepare bool_masked_pos
												__lowercase      :							List[str]				=     np.ones((1, 196)		,     dtype=A_							)

												# forward pass
												__lowercase      :							Tuple				=     model(pixel_values=A_		,     bool_masked_pos=A_							)
												__lowercase      :							int				=     outputs.logits

												# verify the logits
												__lowercase      :							Tuple				=     (1, 196, 8192)
												self.assertEqual(logits.shape		,     A_							)

												__lowercase      :							Tuple				=     np.array(
												    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]							)

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



					@slow
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Dict				=     FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							)

												__lowercase      :							Optional[Any]				=     self.default_image_processor
												__lowercase      :							Union[str, Any]				=     prepare_img()
												__lowercase      :							Optional[Any]				=     image_processor(images=A_		,     return_tensors="""np"""							)

												# forward pass
												__lowercase      :							Dict				=     model(**A_							)
												__lowercase      :							List[str]				=     outputs.logits

												# verify the logits
												__lowercase      :							Dict				=     (1, 1000)
												self.assertEqual(logits.shape		,     A_							)

												__lowercase      :							int				=     np.array([-1.2385, -1.0987, -1.0108]							)

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

												__lowercase      :							Dict				=     281
												self.assertEqual(logits.argmax(-1							).item()		,     A_							)



					@slow
					def       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

												__lowercase      :							Tuple				=     FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""							)

												__lowercase      :							Any				=     self.default_image_processor
												__lowercase      :							Any				=     prepare_img()
												__lowercase      :							int				=     image_processor(images=A_		,     return_tensors="""np"""							)

												# forward pass
												__lowercase      :							Any				=     model(**A_							)
												__lowercase      :							List[Any]				=     outputs.logits

												# verify the logits
												__lowercase      :							Optional[Any]				=     (1, 21841)
												self.assertEqual(logits.shape		,     A_							)

												__lowercase      :							int				=     np.array([1.6881, -0.2787, 0.5901]							)

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

												__lowercase      :							Dict				=     2396
												self.assertEqual(logits.argmax(-1							).item()		,     A_							)







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

												warnings.warn(
												    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use BeitImageProcessor instead."""		,     __a		,     )
												super().__init__(*__a		,     **__a							)