






"""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   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__=1_00 ,		snake_case__=13 ,		snake_case__=30 ,		snake_case__=2 ,		snake_case__=3 ,		snake_case__=True ,		snake_case__=True ,		snake_case__=32 ,		snake_case__=5 ,		snake_case__=4 ,		snake_case__=37 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=10 ,		snake_case__=0.02 ,		snake_case__=3 ,		)			->			Tuple:



         """simple docstring"""





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

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



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





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

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

         UpperCAmelCase	    =			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=snake_case__ ,		initializer_range=self.initializer_range ,		)

         return config, pixel_values, labels



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			str:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





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

         # test greyscale images
         UpperCAmelCase	    =			1
         UpperCAmelCase	    =			FlaxBeitForImageClassification(snake_case__				)

         UpperCAmelCase	    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         UpperCAmelCase	    =			model(snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





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





@require_flax
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					Any				=	(
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )



  def 		UpperCamelCase_				(      self				)			->			None:



         """simple docstring"""





         UpperCAmelCase	    =			FlaxBeitModelTester(self				)
         UpperCAmelCase	    =			ConfigTester(self ,		config_class=snake_case__ ,		has_text_modality=snake_case__ ,		hidden_size=37				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         self.config_tester.run_common_tests()



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCAmelCase	    =			["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,		snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__				):
                       UpperCAmelCase	    =			self._prepare_for_class(snake_case__ ,		snake_case__				)
                       UpperCAmelCase	    =			model_class(snake_case__				)

                       @jax.jit
                       def model_jitted(snake_case__ ,		**snake_case__				):
                              return model(pixel_values=snake_case__ ,		**snake_case__				)

                       with self.subTest("""JIT Enabled"""				):
                              UpperCAmelCase	    =			model_jitted(**snake_case__				).to_tuple()

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

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



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         for model_class_name in self.all_model_classes:
                UpperCAmelCase	    =			model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""				)
                UpperCAmelCase	    =			model(np.ones((1, 3, 2_24, 2_24)				)				)
                self.assertIsNotNone(snake_case__				)






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





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



  @cached_property
  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





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



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



         """simple docstring"""





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

         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(images=snake_case__ ,		return_tensors="""np"""				).pixel_values

         # prepare bool_masked_pos
         UpperCAmelCase	    =			np.ones((1, 1_96) ,		dtype=snake_case__				)

         # forward pass
         UpperCAmelCase	    =			model(pixel_values=snake_case__ ,		bool_masked_pos=snake_case__				)
         UpperCAmelCase	    =			outputs.logits

         # verify the logits
         UpperCAmelCase	    =			(1, 1_96, 81_92)
         self.assertEqual(logits.shape ,		snake_case__				)

         UpperCAmelCase	    =			np.array(
             [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]				)

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



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



         """simple docstring"""





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

         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(images=snake_case__ ,		return_tensors="""np"""				)

         # forward pass
         UpperCAmelCase	    =			model(**snake_case__				)
         UpperCAmelCase	    =			outputs.logits

         # verify the logits
         UpperCAmelCase	    =			(1, 10_00)
         self.assertEqual(logits.shape ,		snake_case__				)

         UpperCAmelCase	    =			np.array([-1.2_385, -1.0_987, -1.0_108]				)

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

         UpperCAmelCase	    =			2_81
         self.assertEqual(logits.argmax(-1				).item() ,		snake_case__				)



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



         """simple docstring"""





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

         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(images=snake_case__ ,		return_tensors="""np"""				)

         # forward pass
         UpperCAmelCase	    =			model(**snake_case__				)
         UpperCAmelCase	    =			outputs.logits

         # verify the logits
         UpperCAmelCase	    =			(1, 2_18_41)
         self.assertEqual(logits.shape ,		snake_case__				)

         UpperCAmelCase	    =			np.array([1.6_881, -0.2_787, 0.5_901]				)

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

         UpperCAmelCase	    =			23_96
         self.assertEqual(logits.argmax(-1				).item() ,		snake_case__				)








"""simple docstring"""







import warnings

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





class   UpperCamelCase_ (       a_  ):
  _A       :					Optional[int]				=	['image_processor', 'tokenizer']
  _A       :					Optional[Any]				=	'ViTImageProcessor'
  _A       :					Tuple				=	('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead.""" ,		snake_case__ ,		)
                UpperCAmelCase	    =			kwargs.pop("""feature_extractor"""				)

         UpperCAmelCase	    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""				)
         if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""				)

         super().__init__(snake_case__ ,		snake_case__				)



  def __call__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			List[str]:



         """simple docstring"""





         if text is None and visual_prompt is None and images is None:
                raise ValueError("""You have to specify either text, visual prompt or images."""				)

         if text is not None and visual_prompt is not None:
                raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""				)

         if text is not None:
                UpperCAmelCase	    =			self.tokenizer(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if images is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None and images is not None:
                UpperCAmelCase	    =			{
                    """pixel_values""": image_features.pixel_values,
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                UpperCAmelCase	    =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                UpperCAmelCase	    =			{
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**snake_case__				) ,		tensor_type=snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





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



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,		snake_case__ ,		)
         return self.image_processor_class



  @property
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,		snake_case__ ,		)
         return self.image_processor
