






'''simple docstring'''







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
			import jax

			from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class _lowercase	(				unittest.TestCase		):




							def __init__(							self:				Optional[int]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[str]=13			,							UpperCamelCase__:				int=30			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				str=True			,							UpperCamelCase__:				Tuple=True			,							UpperCamelCase__:				Optional[Any]=32			,							UpperCamelCase__:				int=5			,							UpperCamelCase__:				List[Any]=4			,							UpperCamelCase__:				List[str]=37			,							UpperCamelCase__:				Any="gelu"			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				Dict=10			,							UpperCamelCase__:				List[Any]=0.02			,							):
														lowerCamelCase__					:	Tuple         =       parent
														lowerCamelCase__					:	Any         =       batch_size
														lowerCamelCase__					:	str         =       image_size
														lowerCamelCase__					:	Any         =       patch_size
														lowerCamelCase__					:	List[str]         =       num_channels
														lowerCamelCase__					:	Any         =       is_training
														lowerCamelCase__					:	int         =       use_labels
														lowerCamelCase__					:	List[Any]         =       hidden_size
														lowerCamelCase__					:	Optional[int]         =       num_hidden_layers
														lowerCamelCase__					:	List[str]         =       num_attention_heads
														lowerCamelCase__					:	Optional[Any]         =       intermediate_size
														lowerCamelCase__					:	List[Any]         =       hidden_act
														lowerCamelCase__					:	str         =       hidden_dropout_prob
														lowerCamelCase__					:	List[str]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Tuple         =       type_sequence_label_size
														lowerCamelCase__					:	List[str]         =       initializer_range

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




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Tuple         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	Any         =       ViTConfig(
														    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




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       FlaxViTModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
														lowerCamelCase__					:	List[Any]         =       (self.image_size, self.image_size)
														lowerCamelCase__					:	Dict         =       (self.patch_size, self.patch_size)
														lowerCamelCase__					:	int         =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.type_sequence_label_size
														lowerCamelCase__					:	Union[str, Any]         =       FlaxViTForImageClassification(config=UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )

														# test greyscale images
														lowerCamelCase__					:	Dict         =       1
														lowerCamelCase__					:	Union[str, Any]         =       FlaxViTForImageClassification(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	str         =       model(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[Any]         =       self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__    
														)  ,		(
														    lowerCamelCase__    
														)  ,		    
														)					:	str         =       config_and_inputs
														lowerCamelCase__					:	List[str]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_flax
class _lowercase	(				_A				,      unittest.TestCase		):
							a				      =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	int         =       FlaxViTModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				int     ):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__					:	Tuple         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[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__					:	int         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																												lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )

																												@jax.jit
																												def model_jitted(UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				Dict     ):
																																			return model(pixel_values=UpperCamelCase__			,							**UpperCamelCase__     )

																												with self.subTest("""JIT Enabled"""     ):
																																			lowerCamelCase__					:	Union[str, Any]         =       model_jitted(**UpperCamelCase__     ).to_tuple()

																												with self.subTest("""JIT Disabled"""     ):
																																			with jax.disable_jit():
																																										lowerCamelCase__					:	int         =       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     )




							@slow
							def 		lowerCamelCase_				(							self:				Tuple     ):






														for model_class_name in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""     )
																					lowerCamelCase__					:	Tuple         =       model(np.ones((1, 3, 224, 224)     )     )
																					self.assertIsNotNone(UpperCamelCase__     )



from __future__ import annotations

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

