


'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script







@require_cpu
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          debug_launcher(test_script.main		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          debug_launcher(test_ops.main		)





'''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 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=100			,     lowerCamelCase=13			,     lowerCamelCase=30			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     lowerCamelCase=3			,     ):


          '''simple docstring'''





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

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

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

          __A	:       Dict      =							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=lowerCamelCase			,     initializer_range=self.initializer_range			,     )

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModel(config=lowerCamelCase		)
          __A	:       Optional[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							FlaxBeitForMaskedImageModeling(config=lowerCamelCase		)
          __A	:       Union[str, Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							self.type_sequence_label_size
          __A	:       List[str]      =							FlaxBeitForImageClassification(config=lowerCamelCase		)
          __A	:       Tuple      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          __A	:       Tuple      =							1
          __A	:       Optional[Any]      =							FlaxBeitForImageClassification(lowerCamelCase		)

          __A	:       Union[str, Any]      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A	:       str      =							model(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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







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

     """simple docstring"""






     lowerCamelCase__       						=				(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModelTester(self		)
          __A	:       List[Any]      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

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

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                    __A	:       Optional[int]      =							self._prepare_for_class(lowerCamelCase			,     lowerCamelCase		)
                    __A	:       Optional[int]      =							model_class(lowerCamelCase		)

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

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

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

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


def 		_lowercase ():




     '''simple docstring'''





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







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

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"		)

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

          # prepare bool_masked_pos
          __A	:       List[Any]      =							np.ones((1, 196)			,     dtype=lowerCamelCase		)

          # forward pass
          __A	:       Union[str, Any]      =							model(pixel_values=lowerCamelCase			,     bool_masked_pos=lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       int      =							(1, 196, 8192)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       Optional[int]      =							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]			,     lowerCamelCase			,     atol=1E-2		)		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"		)

          __A	:       Any      =							self.default_image_processor
          __A	:       List[Any]      =							prepare_img()
          __A	:       List[Any]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       int      =							model(**lowerCamelCase		)
          __A	:       Any      =							outputs.logits

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

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

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

          __A	:       Optional[int]      =							281
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          __A	:       Optional[int]      =							self.default_image_processor
          __A	:       Tuple      =							prepare_img()
          __A	:       List[str]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       List[Any]      =							model(**lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       Union[str, Any]      =							(1, 2_1841)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

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

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

          __A	:       Optional[int]      =							2396
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)

