"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
	    TFDeiTForImageClassification,
	    TFDeiTForImageClassificationWithTeacher,
	    TFDeiTForMaskedImageModeling,
	    TFDeiTModel,
	)
	from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DeiTImageProcessor

class 	UpperCamelCase    :



				def __init__(self       :   Dict			,						_A       :   Tuple			,						_A       :   Tuple=13			,						_A       :   Any=30			,						_A       :   List[Any]=2			,						_A       :   str=3			,						_A       :   List[str]=True			,						_A       :   int=True			,						_A       :   Union[str, Any]=32			,						_A       :   str=2			,						_A       :   Tuple=4			,						_A       :   Optional[Any]=37			,						_A       :   str="gelu"			,						_A       :   Tuple=0.1			,						_A       :   Any=0.1			,						_A       :   Optional[Any]=10			,						_A       :   Optional[Any]=0.02			,						_A       :   Union[str, Any]=3			,						_A       :   Tuple=None			,						_A       :   Any=2			,						)					->			Any:
					__snake_case    :							Optional[int]   	=				parent
					__snake_case    :							Optional[Any]   	=				batch_size
					__snake_case    :							int   	=				image_size
					__snake_case    :							Dict   	=				patch_size
					__snake_case    :							Dict   	=				num_channels
					__snake_case    :							Tuple   	=				is_training
					__snake_case    :							str   	=				use_labels
					__snake_case    :							Optional[int]   	=				hidden_size
					__snake_case    :							Union[str, Any]   	=				num_hidden_layers
					__snake_case    :							str   	=				num_attention_heads
					__snake_case    :							List[Any]   	=				intermediate_size
					__snake_case    :							Optional[Any]   	=				hidden_act
					__snake_case    :							Dict   	=				hidden_dropout_prob
					__snake_case    :							List[Any]   	=				attention_probs_dropout_prob
					__snake_case    :							Optional[Any]   	=				type_sequence_label_size
					__snake_case    :							str   	=				initializer_range
					__snake_case    :							List[Any]   	=				scope
					__snake_case    :							int   	=				encoder_stride

					# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
					__snake_case    :							List[str]   	=				(image_size // patch_size) ** 2
					__snake_case    :							str   	=				num_patches + 2



				def       _lowercase  (self       :   List[Any])					->			List[str]:
					__snake_case    :							List[Any]   	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

					__snake_case    :							Dict   	=				self.get_config()

					return config, pixel_values, labels



				def       _lowercase  (self       :   Any)					->			Optional[int]:
					return DeiTConfig(
					    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			,						encoder_stride=self.encoder_stride			,						)



				def       _lowercase  (self       :   str			,						_A       :   List[Any]			,						_A       :   List[Any]			,						_A       :   Union[str, Any])					->			Union[str, Any]:
					__snake_case    :							int   	=				TFDeiTModel(config=_A)
					__snake_case    :							Union[str, Any]   	=				model(_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Union[str, Any]			,						_A       :   Optional[int]			,						_A       :   Union[str, Any])					->			int:
					__snake_case    :							List[str]   	=				TFDeiTForMaskedImageModeling(config=_A)
					__snake_case    :							List[str]   	=				model(_A)
					self.parent.assertEqual(
					    result.reconstruction.shape			,						(self.batch_size, self.num_channels, self.image_size, self.image_size))

					# test greyscale images
					__snake_case    :							Optional[int]   	=				1
					__snake_case    :							Any   	=				TFDeiTForMaskedImageModeling(_A)

					__snake_case    :							Optional[Any]   	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
					__snake_case    :							Tuple   	=				model(_A)
					self.parent.assertEqual(result.reconstruction.shape			,						(self.batch_size, 1, self.image_size, self.image_size))



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   int			,						_A       :   Optional[Any]			,						_A       :   Any)					->			Union[str, Any]:
					__snake_case    :							Optional[Any]   	=				self.type_sequence_label_size
					__snake_case    :							str   	=				TFDeiTForImageClassification(_A)
					__snake_case    :							List[str]   	=				model(_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.type_sequence_label_size))

					# test greyscale images
					__snake_case    :							str   	=				1
					__snake_case    :							List[Any]   	=				TFDeiTForImageClassification(_A)

					__snake_case    :							Tuple   	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
					__snake_case    :							Dict   	=				model(_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.type_sequence_label_size))






				def       _lowercase  (self       :   Optional[int])					->			Any:
					__snake_case    :							Tuple   	=				self.prepare_config_and_inputs()
					__snake_case	,       __snake_case	,       __snake_case    :							List[Any]   	=				config_and_inputs
					__snake_case    :							List[str]   	=				{'pixel_values': pixel_values}
					return config, inputs_dict







@require_tf
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Dict					=	(
				    (
				        TFDeiTModel,
				        TFDeiTForImageClassification,
				        TFDeiTForImageClassificationWithTeacher,
				        TFDeiTForMaskedImageModeling,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCAmelCase       :   str					=	(
				    {
				        """feature-extraction""": TFDeiTModel,
				        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
				    }
				    if is_tf_available()
				    else {}
				)

				UpperCAmelCase       :   Any					=	False
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   Optional[Any]					=	False
				UpperCAmelCase       :   int					=	False



				def       _lowercase  (self       :   Dict)					->			str:
					__snake_case    :							int   	=				TFDeiTModelTester(self)
					__snake_case    :							int   	=				ConfigTester(self			,						config_class=_A			,						has_text_modality=_A			,						hidden_size=37)



				def       _lowercase  (self       :   List[str])					->			int:
					self.config_tester.run_common_tests()



				@unittest.skip(reason='DeiT does not use inputs_embeds')
				def       _lowercase  (self       :   str)					->			Optional[int]:
					pass



				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					__snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case    :							Tuple   	=				model_class(_A)
						self.assertIsInstance(model.get_input_embeddings()			,						(tf.keras.layers.Layer))
						__snake_case    :							Dict   	=				model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(_A			,						tf.keras.layers.Dense))



				def       _lowercase  (self       :   Optional[int])					->			Any:
					__snake_case	,       __snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case    :							Union[str, Any]   	=				model_class(_A)
						__snake_case    :							Optional[int]   	=				inspect.signature(model.call)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__snake_case    :							int   	=				[*signature.parameters.keys()]

						__snake_case    :							Dict   	=				['pixel_values']
						self.assertListEqual(arg_names[:1]			,						_A)



				def       _lowercase  (self       :   Any)					->			int:
					__snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					__snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*_A)



				def       _lowercase  (self       :   Optional[int])					->			Any:
					__snake_case    :							Tuple   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_A)



				def       _lowercase  (self       :   Dict			,						_A       :   Any			,						_A       :   Optional[Any]			,						_A       :   Any=False)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				super()._prepare_for_class(_A			,						_A			,						return_labels=_A)

					if return_labels:
						if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters:
							del inputs_dict["labels"]

					return inputs_dict






				@slow
				def       _lowercase  (self       :   str)					->			Any:
					for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							Tuple   	=				TFDeiTModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







def 						__UpperCAmelCase       (					)							->    Union[str, Any]:

	'''simple docstring'''





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

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



				@cached_property
				def       _lowercase  (self       :   Union[str, Any])					->			int:
					return (
					    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
					    if is_vision_available()
					    else None
					)






				@slow
				def       _lowercase  (self       :   str)					->			Dict:
					__snake_case    :							List[str]   	=				TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')

					__snake_case    :							Tuple   	=				self.default_image_processor
					__snake_case    :							str   	=				prepare_img()
					__snake_case    :							Optional[int]   	=				image_processor(images=_A			,						return_tensors='tf')

					# forward pass
					__snake_case    :							int   	=				model(**_A)

					# verify the logits
					__snake_case    :							Optional[Any]   	=				tf.TensorShape((1, 10_00))
					self.assertEqual(outputs.logits.shape			,						_A)

					__snake_case    :							Optional[Any]   	=				tf.constant([-1.0_266, 0.1_912, -1.2_861])

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






"""simple docstring"""







from typing import Any

class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   Any)					->			int:
					__snake_case    :							Any   	=				data
					__snake_case    :							Dict   	=				None






				def __repr__(self       :   Tuple)					->			str:
					return f"Node({self.data})"







class 	UpperCamelCase    :



				def __init__(self       :   Union[str, Any])					->			Union[str, Any]:
					__snake_case    :							Any   	=				None



				def __iter__(self       :   Tuple)					->			Any:
					__snake_case    :							List[str]   	=				self.head
					while node:
						yield node.data
						__snake_case    :							Any   	=				node.next



				def __len__(self       :   str)					->			int:
					return sum(1 for _ in self)



				def __repr__(self       :   int)					->			str:
					return "->".join([str(_A) for item in self])



				def __getitem__(self       :   List[Any]			,						_A       :   int)					->			Any:
					if not 0 <= index < len(self):
						raise ValueError('list index out of range.')
					for i, node in enumerate(self):
						if i == index:
							return node
					return None



				def __setitem__(self       :   int			,						_A       :   int			,						_A       :   Any)					->			None:
					if not 0 <= index < len(self):
						raise ValueError('list index out of range.')
					__snake_case    :							Optional[int]   	=				self.head
					for _ in range(_A):
						__snake_case    :							Any   	=				current.next
					__snake_case    :							Dict   	=				data



				def       _lowercase  (self       :   List[Any]			,						_A       :   Any)					->			None:
					self.insert_nth(len(self)			,						_A)



				def       _lowercase  (self       :   List[str]			,						_A       :   Any)					->			None:
					self.insert_nth(0			,						_A)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   int			,						_A       :   Any)					->			None:
					if not 0 <= index <= len(self):
						raise IndexError('list index out of range')
					__snake_case    :							str   	=				Node(_A)
					if self.head is None:
						__snake_case    :							str   	=				new_node
					elif index == 0:
						__snake_case    :							Union[str, Any]   	=				self.head  # link new_node to head
						__snake_case    :							int   	=				new_node
					else:
						__snake_case    :							Any   	=				self.head
						for _ in range(index - 1):
							__snake_case    :							Any   	=				temp.next
						__snake_case    :							Dict   	=				temp.next
						__snake_case    :							str   	=				new_node



				def       _lowercase  (self       :   Optional[int])					->			None:  # print every node data
					print(self)



				def       _lowercase  (self       :   Optional[Any])					->			Any:
					return self.delete_nth(0)



				def       _lowercase  (self       :   List[str])					->			Any:  # delete from tail
					return self.delete_nth(len(self) - 1)



				def       _lowercase  (self       :   int			,						_A       :   int = 0)					->			Any:
					if not 0 <= index <= len(self) - 1:  # test if index is valid
						raise IndexError('List index out of range.')
					__snake_case    :							int   	=				self.head  # default first node
					if index == 0:
						__snake_case    :							Any   	=				self.head.next
					else:
						__snake_case    :							List[Any]   	=				self.head
						for _ in range(index - 1):
							__snake_case    :							List[str]   	=				temp.next
						__snake_case    :							Union[str, Any]   	=				temp.next
						__snake_case    :							str   	=				temp.next.next
					return delete_node.data



				def       _lowercase  (self       :   str)					->			bool:
					return self.head is None






				def       _lowercase  (self       :   Tuple)					->			None:
					__snake_case    :							List[Any]   	=				None
					__snake_case    :							Optional[Any]   	=				self.head

					while current:
						# Store the current node's next node.
						__snake_case    :							List[str]   	=				current.next
						# Make the current node's next point backwards
						__snake_case    :							Optional[Any]   	=				prev
						# Make the previous node be the current node
						__snake_case    :							Optional[Any]   	=				current
						# Make the current node the next node (to progress iteration)
						__snake_case    :							Any   	=				next_node
					# Return prev in order to put the head at the end
					__snake_case    :							Optional[Any]   	=				prev







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				LinkedList()
	assert linked_list.is_empty() is True
	assert str(UpperCAmelCase_						) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(10						):
		assert len(UpperCAmelCase_						) == i
		linked_list.insert_nth(UpperCAmelCase_    ,							i + 1						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1    ,							11						)						)

	linked_list.insert_head(0						)
	linked_list.insert_tail(11						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(0    ,							12						)						)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_nth(9						) == 10
	assert linked_list.delete_tail() == 11
	assert len(UpperCAmelCase_						) == 9
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1    ,							10						)						)

	assert all(linked_list[i] == i + 1 for i in range(0    ,							9						)						) is True

	for i in range(0    ,							9						):
		__snake_case    :							Tuple   	=				-i
	assert all(linked_list[i] == -i for i in range(0    ,							9						)						) is True

	linked_list.reverse()
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(-8    ,							1						)						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							str   	=				[
	    -9,
	    1_00,
	    Node(77_34_51_12						),
	    'dlrow olleH',
	    7,
	    55_55,
	    0,
	    -192.55_555,
	    'Hello, world!',
	    77.9,
	    Node(10						),
	    None,
	    None,
	    12.20,
	]
	__snake_case    :							Optional[Any]   	=				LinkedList()

	for i in test_input:
		linked_list.insert_tail(UpperCAmelCase_						)

	# Check if it's empty or not
	assert linked_list.is_empty() is False
	assert (
	    str(UpperCAmelCase_						) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
	    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the head
	__snake_case    :							int   	=				linked_list.delete_head()
	assert result == -9
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	__snake_case    :							Any   	=				linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None"
	)

	# Delete a node in specific location in linked list
	__snake_case    :							Tuple   	=				linked_list.delete_nth(10						)
	assert result is None
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None"
	)

	# Add a Node instance to its head
	linked_list.insert_head(Node('Hello again, world!'						)						)
	assert (
	    str(UpperCAmelCase_						)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
	)

	# Add None to its tail
	linked_list.insert_tail(UpperCAmelCase_						)
	assert (
	    str(UpperCAmelCase_						)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
	)

	# Reverse the linked list
	linked_list.reverse()
	assert (
	    str(UpperCAmelCase_						)
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)
def 						__UpperCAmelCase       (					)							->    List[Any]:

	'''simple docstring'''





	from doctest import testmod

	testmod()

	__snake_case    :							int   	=				LinkedList()
	linked_list.insert_head(input('Inserting 1st at head '						).strip()						)
	linked_list.insert_head(input('Inserting 2nd at head '						).strip()						)
	print('\nPrint list:'						)
	linked_list.print_list()
	linked_list.insert_tail(input('\nInserting 1st at tail '						).strip()						)
	linked_list.insert_tail(input('Inserting 2nd at tail '						).strip()						)
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nDelete head'						)
	linked_list.delete_head()
	print('Delete tail'						)
	linked_list.delete_tail()
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nReverse linked list'						)
	linked_list.reverse()
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nString representation of linked list:'						)
	print(UpperCAmelCase_						)
	print('\nReading/changing Node data using indexing:'						)
	print(F"Element at Position 1: {linked_list[1]}"						)
	__snake_case    :							Optional[Any]   	=				input('Enter New Value: '						).strip()
	print('New list:'						)
	print(UpperCAmelCase_						)
	print(F"length of linked_list is : {len(UpperCAmelCase_						)}"						)


if __name__ == "__main__":
	main()





