






'''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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




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


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				parent
            __lowerCamelCase						:       str				   =				vocab_size
            __lowerCamelCase						:       int				   =				batch_size
            __lowerCamelCase						:       Dict				   =				image_size
            __lowerCamelCase						:       str				   =				patch_size
            __lowerCamelCase						:       str				   =				num_channels
            __lowerCamelCase						:       int				   =				is_training
            __lowerCamelCase						:       List[Any]				   =				use_labels
            __lowerCamelCase						:       Dict				   =				hidden_size
            __lowerCamelCase						:       int				   =				num_hidden_layers
            __lowerCamelCase						:       int				   =				num_attention_heads
            __lowerCamelCase						:       str				   =				intermediate_size
            __lowerCamelCase						:       List[Any]				   =				hidden_act
            __lowerCamelCase						:       Optional[Any]				   =				hidden_dropout_prob
            __lowerCamelCase						:       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[Any]				   =				(image_size // patch_size) ** 2
            __lowerCamelCase						:       Union[str, Any]				   =				num_patches + 1




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

            __lowerCamelCase						:       List[str]				   =				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      : List[str]	,    A__      : Optional[Any]	,    A__      : Any	,    A__      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				FlaxBeitModel(config=A__					)
            __lowerCamelCase						:       int				   =				model(A__					)
            self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)					)




       def 					a_					(					self      : str	,    A__      : Dict	,    A__      : Any	,    A__      : Optional[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Dict	,    A__      : Union[str, Any]	,    A__      : Tuple	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.type_sequence_label_size
            __lowerCamelCase						:       Tuple				   =				FlaxBeitForImageClassification(config=A__					)
            __lowerCamelCase						:       Tuple				   =				model(A__					)
            self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)					)

            # test greyscale images
            __lowerCamelCase						:       str				   =				1
            __lowerCamelCase						:       Union[str, Any]				   =				FlaxBeitForImageClassification(A__					)

            __lowerCamelCase						:       str				   =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            __lowerCamelCase						:       Tuple				   =				model(A__					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







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







@require_flax
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Optional[int]   		=			(
           (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
       )




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				FlaxBeitModelTester(self					)
            __lowerCamelCase						:       Any				   =				ConfigTester(self	,    config_class=A__	,    has_text_modality=A__	,    hidden_size=37					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            self.config_tester.run_common_tests()




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase   ,      __lowerCamelCase						:       Tuple				   =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 __lowerCamelCase						:       str				   =				["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]	,    A__					)




       def 					a_					(					self      : List[Any]					):


            """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						:       Dict				   =				self._prepare_for_class(A__	,    A__					)
                      __lowerCamelCase						:       int				   =				model_class(A__					)

                      @jax.jit
                      def model_jitted(A__      : Tuple	,    **A__      : Dict					):
                           return model(pixel_values=A__	,    **A__					)

                      with self.subTest("""JIT Enabled"""					):
                           __lowerCamelCase						:       int				   =				model_jitted(**A__					).to_tuple()

                      with self.subTest("""JIT Disabled"""					):
                           with jax.disable_jit():
                                __lowerCamelCase						:       Optional[int]				   =				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      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*A__					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*A__					)




       @slow
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowerCamelCase						:       str				   =				model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""					)
                 __lowerCamelCase						:       Union[str, Any]				   =				model(np.ones((1, 3, 224, 224)					)					)
                 self.assertIsNotNone(A__					)

def     __lowercase  ()   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Optional[Any]				   =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
     return image







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




       @cached_property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







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




       @slow
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""					)

            __lowerCamelCase						:       str				   =				self.default_image_processor
            __lowerCamelCase						:       str				   =				prepare_img()
            __lowerCamelCase						:       List[str]				   =				image_processor(images=A__	,    return_tensors="""np"""					).pixel_values

            # prepare bool_masked_pos
            __lowerCamelCase						:       Any				   =				np.ones((1, 196)	,    dtype=A__					)

            # forward pass
            __lowerCamelCase						:       int				   =				model(pixel_values=A__	,    bool_masked_pos=A__					)
            __lowerCamelCase						:       Tuple				   =				outputs.logits

            # verify the logits
            __lowerCamelCase						:       Any				   =				(1, 196, 8192)
            self.assertEqual(logits.shape	,    A__					)

            __lowerCamelCase						:       str				   =				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      : int					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""					)

            __lowerCamelCase						:       Any				   =				self.default_image_processor
            __lowerCamelCase						:       str				   =				prepare_img()
            __lowerCamelCase						:       Optional[int]				   =				image_processor(images=A__	,    return_tensors="""np"""					)

            # forward pass
            __lowerCamelCase						:       Any				   =				model(**A__					)
            __lowerCamelCase						:       Optional[Any]				   =				outputs.logits

            # verify the logits
            __lowerCamelCase						:       Dict				   =				(1, 1000)
            self.assertEqual(logits.shape	,    A__					)

            __lowerCamelCase						:       str				   =				np.array([-1.2385, -1.0987, -1.0108]					)

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

            __lowerCamelCase						:       Optional[Any]				   =				281
            self.assertEqual(logits.argmax(-1					).item()	,    A__					)




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


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""					)

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

            # forward pass
            __lowerCamelCase						:       Optional[int]				   =				model(**A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				outputs.logits

            # verify the logits
            __lowerCamelCase						:       int				   =				(1, 21841)
            self.assertEqual(logits.shape	,    A__					)

            __lowerCamelCase						:       str				   =				np.array([1.6881, -0.2787, 0.5901]					)

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

            __lowerCamelCase						:       Dict				   =				2396
            self.assertEqual(logits.argmax(-1					).item()	,    A__					)











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



