


'''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 	a       (       unittest.TestCase    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=1_00 ,			__magic_name__=13 ,			__magic_name__=30 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10 ,			__magic_name__=0.0_2 ,			__magic_name__=3 ,			)   ->							Dict:
      _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 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _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=SCREAMING_SNAKE_CASE_ ,			initializer_range=self.initializer_range ,			)

      return config, pixel_values, labels
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							FlaxBeitModel(config=SCREAMING_SNAKE_CASE_  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length - 1, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							self.type_sequence_label_size
      _a      =							FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE_  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size)  )

      # test greyscale images
      _a      =							1
      _a      =							FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE_  )

      _a      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.prepare_config_and_inputs()
      (
          _a    
      )      =							config_and_inputs
      _a      =							{"""pixel_values""": pixel_values}
      return config, inputs_dict





@require_flax
class 	a       (       _lowerCAmelCase					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )
 def 							__UpperCAmelCase						(	self  )   ->							None:
      _a      =							FlaxBeitModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=SCREAMING_SNAKE_CASE_ ,			has_text_modality=SCREAMING_SNAKE_CASE_ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =							model_class(SCREAMING_SNAKE_CASE_  )
           _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] ,			SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _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(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
                _a      =							model_class(SCREAMING_SNAKE_CASE_  )

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

                with self.subTest('JIT Enabled'  ):
                     _a      =							model_jitted(**SCREAMING_SNAKE_CASE_  ).to_tuple()

                with self.subTest('JIT Disabled'  ):
                     with jax.disable_jit():
                          _a      =							model_jitted(**SCREAMING_SNAKE_CASE_  ).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE_  ) ,			len(SCREAMING_SNAKE_CASE_  )  )
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
                     self.assertEqual(jitted_output.shape ,			output.shape  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      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, 2_24, 2_24)  )  )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )






def 		_A			()				->					Any:




     '''simple docstring'''



     _a      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
     return image






@require_vision
@require_flax
class 	a       (       unittest.TestCase    ):
 @cached_property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'  ) if is_vision_available() else None
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=SCREAMING_SNAKE_CASE_ ,			return_tensors='np'  ).pixel_values

      # prepare bool_masked_pos
      _a      =							np.ones((1, 1_96) ,			dtype=SCREAMING_SNAKE_CASE_  )

      # forward pass
      _a      =							model(pixel_values=SCREAMING_SNAKE_CASE_ ,			bool_masked_pos=SCREAMING_SNAKE_CASE_  )
      _a      =							outputs.logits

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

      _a      =							np.array(
          [[-3.2_4_3_7, 0.5_0_7_2, -13.91_74], [-3.2_4_5_6, 0.4_9_4_8, -13.94_01], [-3.2_0_3_3, 0.5_1_2_1, -13.85_50]]  )

      self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,			SCREAMING_SNAKE_CASE_ ,			atol=1e-2  )  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=SCREAMING_SNAKE_CASE_ ,			return_tensors='np'  )

      # forward pass
      _a      =							model(**SCREAMING_SNAKE_CASE_  )
      _a      =							outputs.logits

      # verify the logits
      _a      =							(1, 10_00)
      self.assertEqual(logits.shape ,			SCREAMING_SNAKE_CASE_  )

      _a      =							np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]  )

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

      _a      =							2_81
      self.assertEqual(logits.argmax(-1  ).item() ,			SCREAMING_SNAKE_CASE_  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=SCREAMING_SNAKE_CASE_ ,			return_tensors='np'  )

      # forward pass
      _a      =							model(**SCREAMING_SNAKE_CASE_  )
      _a      =							outputs.logits

      # verify the logits
      _a      =							(1, 2_18_41)
      self.assertEqual(logits.shape ,			SCREAMING_SNAKE_CASE_  )

      _a      =							np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]  )

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

      _a      =							23_96
      self.assertEqual(logits.argmax(-1  ).item() ,			SCREAMING_SNAKE_CASE_  )








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

