




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor







class     A:



 '''simple docstring'''


 def __init__(							self   :    List[Any]		,				A_   :    Dict		,				A_   :    Union[str, Any]=13		,				A_   :    List[Any]=30		,				A_   :    Optional[Any]=2		,				A_   :    List[str]=3		,				A_   :    List[str]=True		,				A_   :    Dict=True		,				A_   :    List[Any]=32		,				A_   :    Any=2		,				A_   :    Any=4		,				A_   :    Optional[int]=37		,				A_   :    Dict="gelu"		,				A_   :    List[Any]=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    Union[str, Any]=10		,				A_   :    Optional[Any]=0.02		,				A_   :    List[Any]=3		,				A_   :    str=None		,				)  ->      str:

       """simple docstring"""






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

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

 def 							a__					(							self   :    List[str]    )  ->      Dict:

       """simple docstring"""






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

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

       lowerCamelCase_							=       self.get_config()

       return config, pixel_values, labels

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

       """simple docstring"""






       return 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=A_		,				initializer_range=self.initializer_range		,				)

 def 							a__					(							self   :    Any		,				A_   :    int		,				A_   :    int		,				A_   :    int    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModel(config=A_    )
       lowerCamelCase_							=       model(A_		,				training=A_    )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)    )

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_							=       self.image_size // 2
       lowerCamelCase_							=       pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_							=       model(A_		,				interpolate_pos_encoding=A_		,				training=A_    )
       lowerCamelCase_							=       (image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, seq_length, self.hidden_size)    )

 def 							a__					(							self   :    List[Any]		,				A_   :    List[Any]		,				A_   :    Any		,				A_   :    Any    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.type_sequence_label_size
       lowerCamelCase_							=       TFViTForImageClassification(A_    )
       lowerCamelCase_							=       model(A_		,				labels=A_		,				training=A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_							=       self.image_size // 2
       lowerCamelCase_							=       pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_							=       model(A_		,				interpolate_pos_encoding=A_		,				training=A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )

       # test greyscale images
       lowerCamelCase_							=       1
       lowerCamelCase_							=       TFViTForImageClassification(A_    )
       lowerCamelCase_							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       lowerCamelCase_							=       model(A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )





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

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       config_and_inputs
       lowerCamelCase_							=       {'pixel_values': pixel_values}
       return config, inputs_dict







@require_tf
class     A(		UpperCamelCase				, UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 UpperCamelCase		  =		(
     {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCamelCase		  =		False
 UpperCamelCase		  =		False
 UpperCamelCase		  =		False

 def 							a__					(							self   :    int    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModelTester(self    )
       lowerCamelCase_							=       ConfigTester(self		,				config_class=A_		,				has_text_modality=A_		,				hidden_size=37    )

 def 							a__					(							self   :    int    )  ->      List[str]:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='ViT does not use inputs_embeds'    )
 def 							a__					(							self   :    List[str]    )  ->      Tuple:

       """simple docstring"""






       pass

 @unittest.skip(reason='ViT does not use inputs_embeds'    )
 def 							a__					(							self   :    int    )  ->      Optional[Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    str    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=       model_class(A_    )
             self.assertIsInstance(model.get_input_embeddings()		,				(tf.keras.layers.Layer)    )
             lowerCamelCase_							=       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A_		,				tf.keras.layers.Layer    )    )

 def 							a__					(							self   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_							=       ['pixel_values']
             self.assertListEqual(arg_names[:1]		,				A_    )

 def 							a__					(							self   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_    )

 def 							a__					(							self   :    Tuple    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_    )





 @slow
 def 							a__					(							self   :    Any    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModel.from_pretrained('google/vit-base-patch16-224'    )
       self.assertIsNotNone(A_    )

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



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







@require_tf
@require_vision
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 @cached_property
 def 							a__					(							self   :    int    )  ->      Tuple:

       """simple docstring"""






       return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'    ) if is_vision_available() else None





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

       """simple docstring"""






       lowerCamelCase_							=       TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'    )

       lowerCamelCase_							=       self.default_image_processor
       lowerCamelCase_							=       prepare_img()
       lowerCamelCase_							=       image_processor(images=A_		,				return_tensors='tf'    )

       # forward pass
       lowerCamelCase_							=       model(**A_    )

       # verify the logits
       lowerCamelCase_							=       tf.TensorShape((1, 1000)    )
       self.assertEqual(outputs.logits.shape		,				A_    )

       lowerCamelCase_							=       tf.constant([-0.2744, 0.8215, -0.0836]    )

       tf.debugging.assert_near(outputs.logits[0, :3]		,				A_		,				atol=1E-4    )







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

