






"""simple docstring"""



from typing import Any





class 					_UpperCAmelCase       :




					def __init__(				self     :						Optional[Any]    ,						A     :						Any   )  ->  Tuple:
							lowercase_ : List[Any]							       =   data
							lowercase_ : Union[str, Any]							       =   None






					def __repr__(				self     :						Union[str, Any]   )  ->  str:
							return F'''Node({self.data})'''





class 					_UpperCAmelCase       :




					def __init__(				self     :						Dict   )  ->  str:
							lowercase_ : Dict							       =   None




					def __iter__(				self     :						Optional[int]   )  ->  Any:
							lowercase_ : Optional[Any]							       =   self.head
							while node:
									yield node.data
									lowercase_ : Tuple							       =   node.next




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




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




					def __getitem__(				self     :						List[str]    ,						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     :						List[Any]    ,						A     :						int    ,						A     :						Any   )  ->  None:
							if not 0 <= index < len(self   ):
									raise ValueError('''list index out of range.'''   )
							lowercase_ : List[Any]							       =   self.head
							for _ in range(A   ):
									lowercase_ : Union[str, Any]							       =   current.next
							lowercase_ : str							       =   data




					def   A  (				self     :						Optional[int]    ,						A     :						Any   )  ->  None:
							self.insert_nth(len(self   )    ,						A   )




					def   A  (				self     :						Any    ,						A     :						Any   )  ->  None:
							self.insert_nth(0    ,						A   )




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




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




					def   A  (				self     :						Dict   )  ->  Any:
							return self.delete_nth(0   )




					def   A  (				self     :						Any   )  ->  Any:  # delete from tail
							return self.delete_nth(len(self   ) - 1   )




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




					def   A  (				self     :						List[Any]   )  ->  bool:
							return self.head is None






					def   A  (				self     :						Union[str, Any]   )  ->  None:
							lowercase_ : str							       =   None
							lowercase_ : Optional[Any]							       =   self.head

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



def 						lowercase    (				):
		lowercase_ : str							       =   LinkedList()
		assert linked_list.is_empty() is True
		assert str(__snake_case	) == ""

		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(1_0	):
				assert len(__snake_case	) == i
				linked_list.insert_nth(__snake_case		,					i + 1	)
		assert str(__snake_case	) == "->".join(str(__snake_case	) for i in range(1		,					1_1	)	)

		linked_list.insert_head(0	)
		linked_list.insert_tail(1_1	)
		assert str(__snake_case	) == "->".join(str(__snake_case	) for i in range(0		,					1_2	)	)

		assert linked_list.delete_head() == 0
		assert linked_list.delete_nth(9	) == 1_0
		assert linked_list.delete_tail() == 1_1
		assert len(__snake_case	) == 9
		assert str(__snake_case	) == "->".join(str(__snake_case	) for i in range(1		,					1_0	)	)

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

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

		linked_list.reverse()
		assert str(__snake_case	) == "->".join(str(__snake_case	) for i in range(-8		,					1	)	)



def 						lowercase    (				):
		lowercase_ : Optional[int]							       =   [
		    -9,
		    1_0_0,
		    Node(7_7_3_4_5_1_1_2	),
		    '''dlrow olleH''',
		    7,
		    5_5_5_5,
		    0,
		    -192.55555,
		    '''Hello, world!''',
		    77.9,
		    Node(1_0	),
		    None,
		    None,
		    12.20,
		]
		lowercase_ : Any							       =   LinkedList()

		for i in test_input:
				linked_list.insert_tail(__snake_case	)

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

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

		# Delete the tail
		lowercase_ : int							       =   linked_list.delete_tail()
		assert result == 12.2
		assert (
		    str(__snake_case	) == "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
		lowercase_ : int							       =   linked_list.delete_nth(1_0	)
		assert result is None
		assert (
		    str(__snake_case	) == "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(__snake_case	)
		    == "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(__snake_case	)
		assert (
		    str(__snake_case	)
		    == "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(__snake_case	)
		    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
		    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
		)



def 						lowercase    (				):
		from doctest import testmod

		testmod()

		lowercase_ : List[Any]							       =   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(__snake_case	)
		print('''\nReading/changing Node data using indexing:'''	)
		print(F'''Element at Position 1: {linked_list[1]}'''	)
		lowercase_ : int							       =   input('''Enter New Value: '''	).strip()
		print('''New list:'''	)
		print(__snake_case	)
		print(F'''length of linked_list is : {len(__snake_case	)}'''	)


if __name__ == "__main__":
						main()











"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class 					_UpperCAmelCase       :




					def __init__(				self     :						int    ,						A     :						Tuple    ,						A     :						int=3    ,						A     :						List[str]=32    ,						A     :						Dict=3    ,						A     :						Any=10    ,						A     :						Dict=[10, 20, 30, 40]    ,						A     :						Optional[Any]=[1, 1, 2, 1]    ,						A     :						Union[str, Any]=True    ,						A     :						Optional[Any]=True    ,						A     :						Any="relu"    ,						A     :						Optional[Any]=3    ,						A     :						Tuple=None    ,						)  ->  Dict:
							lowercase_ : str							       =   parent
							lowercase_ : List[Any]							       =   batch_size
							lowercase_ : Optional[int]							       =   image_size
							lowercase_ : int							       =   num_channels
							lowercase_ : int							       =   embeddings_size
							lowercase_ : str							       =   hidden_sizes
							lowercase_ : List[str]							       =   depths
							lowercase_ : Dict							       =   is_training
							lowercase_ : int							       =   use_labels
							lowercase_ : Any							       =   hidden_act
							lowercase_ : List[Any]							       =   num_labels
							lowercase_ : Tuple							       =   scope
							lowercase_ : Optional[Any]							       =   len(A   )




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							lowercase_ : Union[str, Any]							       =   None
							if self.use_labels:
									lowercase_ : List[str]							       =   ids_tensor([self.batch_size]    ,						self.num_labels   )

							lowercase_ : Optional[int]							       =   self.get_config()

							return config, pixel_values, labels




					def   A  (				self     :						Dict   )  ->  int:
							return ResNetConfig(
							    num_channels=self.num_channels    ,						embeddings_size=self.embeddings_size    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						hidden_act=self.hidden_act    ,						num_labels=self.num_labels    ,						image_size=self.image_size    ,						)




					def   A  (				self     :						str    ,						A     :						Tuple    ,						A     :						str    ,						A     :						str   )  ->  str:
							lowercase_ : str							       =   TFResNetModel(config=A   )
							lowercase_ : Union[str, Any]							       =   model(A   )
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,						)




					def   A  (				self     :						Any    ,						A     :						int    ,						A     :						List[Any]    ,						A     :						Optional[Any]   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   self.num_labels
							lowercase_ : Union[str, Any]							       =   TFResNetForImageClassification(A   )
							lowercase_ : Tuple							       =   model(A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )






					def   A  (				self     :						Union[str, Any]   )  ->  Tuple:
							lowercase_ : Tuple							       =   self.prepare_config_and_inputs()
							lowercase_       ,					lowercase_       ,					lowercase_ : Dict							       =   config_and_inputs
							lowercase_ : int							       =   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				str							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Any							    =   False




					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : int							       =   TFResNetModelTester(self   )
							lowercase_ : str							       =   ConfigTester(self    ,						config_class=A    ,						has_text_modality=A   )




					def   A  (				self     :						Dict   )  ->  Optional[Any]:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




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




					@unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
					def   A  (				self     :						Any   )  ->  Any:
							pass




					@unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							pass




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_       ,					lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_ : str							       =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,						A   )




					def   A  (				self     :						List[str]   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						List[Any]   )  ->  List[str]:
							def check_hidden_states_output(A     :						Union[str, Any]    ,						A     :						int    ,						A     :						List[Any]   ):
									lowercase_ : int							       =   model_class(A   )
									lowercase_ : Optional[Any]							       =   model(**self._prepare_for_class(A    ,						A   )   )

									lowercase_ : Union[str, Any]							       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase_ : Any							       =   self.model_tester.num_stages
									self.assertEqual(len(A   )    ,						expected_num_stages + 1   )

									# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]   )    ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,						)

							lowercase_       ,					lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_ : Union[str, Any]							       =   ['''basic''', '''bottleneck''']
							for model_class in self.all_model_classes:
									for layer_type in layers_type:
											lowercase_ : List[str]							       =   layer_type
											lowercase_ : Tuple							       =   True
											check_hidden_states_output(A    ,						A    ,						A   )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowercase_ : Optional[Any]							       =   True

											check_hidden_states_output(A    ,						A    ,						A   )




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A   )






					@slow
					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Tuple							       =   TFResNetModel.from_pretrained(A   )
									self.assertIsNotNone(A   )



def 						lowercase    (				):
		lowercase_ : List[str]							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






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




					@cached_property
					def   A  (				self     :						Any   )  ->  Optional[int]:
							return (
							    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
							    if is_vision_available()
							    else None
							)






					@slow
					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Optional[int]							       =   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

							lowercase_ : List[Any]							       =   self.default_image_processor
							lowercase_ : Dict							       =   prepare_img()
							lowercase_ : List[str]							       =   image_processor(images=A    ,						return_tensors='''tf'''   )

							# forward pass
							lowercase_ : Tuple							       =   model(**A   )

							# verify the logits
							lowercase_ : Optional[int]							       =   tf.TensorShape((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						A   )

							lowercase_ : Optional[Any]							       =   tf.constant([-11.1069, -9.7877, -8.3777]   )

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



