
'''simple docstring'''

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  ):


 def __init__(    self  : str			,							a__  : str			,							a__  : Any=100			,							a__  : Union[str, Any]=13			,							a__  : str=30			,							a__  : Any=2			,							a__  : Any=3			,							a__  : Optional[Any]=True			,							a__  : Dict=True			,							a__  : str=32			,							a__  : str=5			,							a__  : Dict=4			,							a__  : Tuple=37			,							a__  : Dict="gelu"			,							a__  : int=0.1			,							a__  : Optional[int]=0.1			,							a__  : List[Any]=10			,							a__  : Dict=0.02			,							a__  : Optional[int]=3			,							):
  '''simple docstring'''

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

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


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   Tuple     		=	None
  if self.use_labels:
   lowerCAmelCase__     :   Union[str, Any]     		=	ids_tensor([self.batch_size]			,							self.type_sequence_label_size    )

  lowerCAmelCase__     :   Tuple     		=	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    _A	(    self  : Optional[int]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	FlaxBeitModel(config=a__    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )


 def    _A	(    self  : Optional[int]			,							a__  : List[str]			,							a__  : Dict			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	FlaxBeitForMaskedImageModeling(config=a__    )
  lowerCAmelCase__     :   Tuple     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length - 1, self.vocab_size)    )


 def    _A	(    self  : str			,							a__  : Tuple			,							a__  : str			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Union[str, Any]     		=	FlaxBeitForImageClassification(config=a__    )
  lowerCAmelCase__     :   List[Any]     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Tuple     		=	1
  lowerCAmelCase__     :   List[Any]     		=	FlaxBeitForImageClassification(a__    )

  lowerCAmelCase__     :   List[Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   List[Any]     		=	model(a__    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

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




@require_flax
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Optional[Any]	=			(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	FlaxBeitModelTester(self    )
  lowerCAmelCase__     :   Union[str, Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : int    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

   lowerCAmelCase__     :   Any     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__    ):
    lowerCAmelCase__     :   Tuple     		=	self._prepare_for_class(a__			,							a__    )
    lowerCAmelCase__     :   int     		=	model_class(a__    )

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

    with self.subTest("JIT Enabled"    ):
     lowerCAmelCase__     :   Union[str, Any]     		=	model_jitted(**a__    ).to_tuple()

    with self.subTest("JIT Disabled"    ):
     with jax.disable_jit():
      lowerCAmelCase__     :   Dict     		=	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    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 @slow
 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  for model_class_name in self.all_model_classes:
   lowerCAmelCase__     :   Tuple     		=	model_class_name.from_pretrained("microsoft/beit-base-patch16-224"    )
   lowerCAmelCase__     :   List[str]     		=	model(np.ones((1, 3, 224, 224)    )    )
   self.assertIsNotNone(a__    )



def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
 return image







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


 @cached_property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

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


 @slow
 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"    )

  lowerCAmelCase__     :   str     		=	self.default_image_processor
  lowerCAmelCase__     :   str     		=	prepare_img()
  lowerCAmelCase__     :   int     		=	image_processor(images=a__			,							return_tensors="np"    ).pixel_values

  # prepare bool_masked_pos
  lowerCAmelCase__     :   int     		=	np.ones((1, 196)			,							dtype=a__    )

  # forward pass
  lowerCAmelCase__     :   str     		=	model(pixel_values=a__			,							bool_masked_pos=a__    )
  lowerCAmelCase__     :   Any     		=	outputs.logits

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

  lowerCAmelCase__     :   Any     		=	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    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"    )

  lowerCAmelCase__     :   Optional[int]     		=	self.default_image_processor
  lowerCAmelCase__     :   Dict     		=	prepare_img()
  lowerCAmelCase__     :   List[Any]     		=	image_processor(images=a__			,							return_tensors="np"    )

  # forward pass
  lowerCAmelCase__     :   Any     		=	model(**a__    )
  lowerCAmelCase__     :   str     		=	outputs.logits

  # verify the logits
  lowerCAmelCase__     :   str     		=	(1, 1000)
  self.assertEqual(logits.shape			,							a__    )

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

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

  lowerCAmelCase__     :   Tuple     		=	281
  self.assertEqual(logits.argmax(-1    ).item()			,							a__    )


 @slow
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.default_image_processor
  lowerCAmelCase__     :   List[Any]     		=	prepare_img()
  lowerCAmelCase__     :   Union[str, Any]     		=	image_processor(images=a__			,							return_tensors="np"    )

  # forward pass
  lowerCAmelCase__     :   Union[str, Any]     		=	model(**a__    )
  lowerCAmelCase__     :   Any     		=	outputs.logits

  # verify the logits
  lowerCAmelCase__     :   Tuple     		=	(1, 2_1841)
  self.assertEqual(logits.shape			,							a__    )

  lowerCAmelCase__     :   Dict     		=	np.array([1.6881, -0.2787, 0.5901]    )

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

  lowerCAmelCase__     :   List[Any]     		=	2396
  self.assertEqual(logits.argmax(-1    ).item()			,							a__    )







'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return number & 1 == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()





