




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       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




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


         """simple docstring"""



         __A         =	parent
         __A         =	vocab_size
         __A         =	batch_size
         __A         =	image_size
         __A         =	patch_size
         __A         =	num_channels
         __A         =	is_training
         __A         =	use_labels
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	type_sequence_label_size
         __A         =	initializer_range

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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

         __A         =	None
         if self.use_labels:
             __A         =	ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)

         __A         =	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=UpperCamelCase_ ,	initializer_range=self.initializer_range ,	)

         return config, pixel_values, labels




     def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         __A         =	FlaxBeitModel(config=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         __A         =	FlaxBeitForMaskedImageModeling(config=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length - 1, self.vocab_size)				)




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         __A         =	self.type_sequence_label_size
         __A         =	FlaxBeitForImageClassification(config=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)				)

         # test greyscale images
         __A         =	1
         __A         =	FlaxBeitForImageClassification(UpperCamelCase_				)

         __A         =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         __A         =	model(UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	self.prepare_config_and_inputs()
         (
             (
             __A	
         )    ,	(
             __A	
         )    ,	(
             __A	
         )    ,		
         )         =	config_and_inputs
         __A         =	{"""pixel_values""": pixel_values}
         return config, inputs_dict


@require_flax
class       __lowercase      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	FlaxBeitModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_ ,	has_text_modality=UpperCamelCase_ ,	hidden_size=37				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

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

             __A         =	["""pixel_values"""]
             self.assertListEqual(arg_names[:1] ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__				):
                 __A         =	self._prepare_for_class(UpperCamelCase_ ,	UpperCamelCase_				)
                 __A         =	model_class(UpperCamelCase_				)

                 @jax.jit
                 def model_jitted(UpperCamelCase_       :			List[str] ,	**UpperCamelCase_       :			Dict				):
                     return model(pixel_values=UpperCamelCase_ ,	**UpperCamelCase_				)

                 with self.subTest("""JIT Enabled"""				):
                     __A         =	model_jitted(**UpperCamelCase_				).to_tuple()

                 with self.subTest("""JIT Disabled"""				):
                     with jax.disable_jit():
                         __A         =	model_jitted(**UpperCamelCase_				).to_tuple()

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




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_				)





     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         for model_class_name in self.all_model_classes:
             __A         =	model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""				)
             __A         =	model(np.ones((1, 3, 224, 224)				)				)
             self.assertIsNotNone(UpperCamelCase_				)



def 			_SCREAMING_SNAKE_CASE						( )						->      List[Any]:





    """simple docstring"""

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






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







     '''simple docstring'''




     @cached_property
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



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




     @slow
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""				)

         __A         =	self.default_image_processor
         __A         =	prepare_img()
         __A         =	image_processor(images=UpperCamelCase_ ,	return_tensors="""np"""				).pixel_values

         # prepare bool_masked_pos
         __A         =	np.ones((1, 196) ,	dtype=UpperCamelCase_				)

         # forward pass
         __A         =	model(pixel_values=UpperCamelCase_ ,	bool_masked_pos=UpperCamelCase_				)
         __A         =	outputs.logits

         # verify the logits
         __A         =	(1, 196, 8_192)
         self.assertEqual(logits.shape ,	UpperCamelCase_				)

         __A         =	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] ,	UpperCamelCase_ ,	atol=1e-2				)				)




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


         """simple docstring"""



         __A         =	FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""				)

         __A         =	self.default_image_processor
         __A         =	prepare_img()
         __A         =	image_processor(images=UpperCamelCase_ ,	return_tensors="""np"""				)

         # forward pass
         __A         =	model(**UpperCamelCase_				)
         __A         =	outputs.logits

         # verify the logits
         __A         =	(1, 1_000)
         self.assertEqual(logits.shape ,	UpperCamelCase_				)

         __A         =	np.array([-1.2385, -1.0987, -1.0108]				)

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

         __A         =	281
         self.assertEqual(logits.argmax(-1				).item() ,	UpperCamelCase_				)





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


         """simple docstring"""



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

         __A         =	self.default_image_processor
         __A         =	prepare_img()
         __A         =	image_processor(images=UpperCamelCase_ ,	return_tensors="""np"""				)

         # forward pass
         __A         =	model(**UpperCamelCase_				)
         __A         =	outputs.logits

         # verify the logits
         __A         =	(1, 21_841)
         self.assertEqual(logits.shape ,	UpperCamelCase_				)

         __A         =	np.array([1.6881, -0.2787, 0.5901]				)

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

         __A         =	2_396
         self.assertEqual(logits.argmax(-1				).item() ,	UpperCamelCase_				)












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




