




from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__A        =    TypeVar("KT")
__A        =    TypeVar("VT")







class     __lowerCAmelCase (       Generic[KT, VT] ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__ = "root"			,						lowerCamelCase__ = None							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    key
							__lowerCamelCase		       =    value
							__lowerCamelCase		       =    []


			def __repr__(      self							)    ->      str:



							'''simple docstring'''
							return f"""Node({self.key}: {self.value})"""






			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							return len(self.forward							)







class     __lowerCAmelCase (       Generic[KT, VT] ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__ = 0.5			,						lowerCamelCase__ = 16							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    Node[KT, VT]()
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    p
							__lowerCamelCase		       =    max_level


			def __str__(      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    list(self							)

							if len(lowerCamelCase__							) == 0:
											return f"""SkipList(level={self.level})"""

							__lowerCamelCase		       =    max((len(str(lowerCamelCase__							)							) for item in items)			,						default=4							)
							__lowerCamelCase		       =    max(lowerCamelCase__			,						4							) + 4

							__lowerCamelCase		       =    self.head
							__lowerCamelCase		       =    []

							__lowerCamelCase		       =    node.forward.copy()
							lines.append(f"""[{node.key}]""".ljust(lowerCamelCase__			,						'-'							) + '* ' * len(lowerCamelCase__							)							)
							lines.append(' ' * label_size + '| ' * len(lowerCamelCase__							)							)

							while len(node.forward							) != 0:
											__lowerCamelCase		       =    node.forward[0]

											lines.append(
											    f"""[{node.key}]""".ljust(lowerCamelCase__			,						'-'							)
											    + ' '.join(str(n.key							) if n.key == node.key else '|' for n in forwards							)							)
											lines.append(' ' * label_size + '| ' * len(lowerCamelCase__							)							)
											__lowerCamelCase		       =    node.forward

							lines.append('None'.ljust(lowerCamelCase__							) + '* ' * len(lowerCamelCase__							)							)
							return f"""SkipList(level={self.level})\n""" + "\n".join(lowerCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.head

							while len(node.forward							) != 0:
											yield node.forward[0].key
											__lowerCamelCase		       =    node.forward[0]


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    1
							while random() < self.p and level < self.max_level:
											level += 1

							return level


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:



							'''simple docstring'''
							__lowerCamelCase		       =    []

							__lowerCamelCase		       =    self.head

							for i in reversed(range(self.level							)							):
											# i < node.level - When node level is lesser than `i` decrement `i`.
											# node.forward[i].key < key - Jumping to node with key value higher
											#                             or equal to searched key would result
											#                             in skipping searched key.
											while i < node.level and node.forward[i].key < key:
															__lowerCamelCase		       =    node.forward[i]
											# Each leftmost node (relative to searched node) will potentially have to
											# be updated.
											update_vector.append(lowerCamelCase__							)

							update_vector.reverse()  # Note that we were inserting values in reverse order.

							# len(node.forward) != 0 - If current node doesn't contain any further
							#                          references then searched key is not present.
							# node.forward[0].key == key - Next node key should be equal to search key
							#                              if key is present.
							if len(node.forward							) != 0 and node.forward[0].key == key:
											return node.forward[0], update_vector
							else:
											return None, update_vector


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self._locate_node(lowerCamelCase__							)

							if node is not None:
											for i, update_node in enumerate(lowerCamelCase__							):
															# Remove or replace all references to removed node.
															if update_node.level > i and update_node.forward[i].key == key:
																			if node.level > i:
																							__lowerCamelCase		       =    node.forward[i]
																			else:
																							__lowerCamelCase		       =    update_node.forward[:i]


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self._locate_node(lowerCamelCase__							)
							if node is not None:
											__lowerCamelCase		       =    value
							else:
											__lowerCamelCase		       =    self.random_level()

											if level > self.level:
															# After level increase we have to add additional nodes to head.
															for _ in range(self.level - 1			,						lowerCamelCase__							):
																			update_vector.append(self.head							)
															__lowerCamelCase		       =    level

											__lowerCamelCase		       =    Node(lowerCamelCase__			,						lowerCamelCase__							)

											for i, update_node in enumerate(update_vector[:level]							):
															# Change references to pass through new node.
															if update_node.level > i:
																			new_node.forward.append(update_node.forward[i]							)

															if update_node.level < i + 1:
																			update_node.forward.append(lowerCamelCase__							)
															else:
																			__lowerCamelCase		       =    new_node






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      VT | None:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self._locate_node(lowerCamelCase__							)

							if node is not None:
											return node.value

							return None







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()
				skip_list.insert('Key1' ,     3      )
				skip_list.insert('Key2' ,     12      )
				skip_list.insert('Key3' ,     41      )
				skip_list.insert('Key4' ,     -19      )

				__lowerCamelCase		       =    skip_list.head
				__lowerCamelCase		       =    {}
				while node.level != 0:
								__lowerCamelCase		       =    node.forward[0]
								__lowerCamelCase		       =    node.value

				assert len(UpperCamelCase__      ) == 4
				assert all_values["Key1"] == 3
				assert all_values["Key2"] == 12
				assert all_values["Key3"] == 41
				assert all_values["Key4"] == -19







def 			lowerCamelCase_		(       )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()
				skip_list.insert('Key1' ,     10      )
				skip_list.insert('Key1' ,     12      )

				skip_list.insert('Key5' ,     7      )
				skip_list.insert('Key7' ,     10      )
				skip_list.insert('Key10' ,     5      )

				skip_list.insert('Key7' ,     7      )
				skip_list.insert('Key5' ,     5      )
				skip_list.insert('Key10' ,     10      )

				__lowerCamelCase		       =    skip_list.head
				__lowerCamelCase		       =    {}
				while node.level != 0:
								__lowerCamelCase		       =    node.forward[0]
								__lowerCamelCase		       =    node.value

				if len(UpperCamelCase__      ) != 4:
								print()
				assert len(UpperCamelCase__      ) == 4
				assert all_values["Key1"] == 12
				assert all_values["Key7"] == 7
				assert all_values["Key5"] == 5
				assert all_values["Key10"] == 10







def 			lowerCamelCase_		(       )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()
				assert skip_list.find('Some key'      ) is None







def 			lowerCamelCase_		(       )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()

				skip_list.insert('Key2' ,     20      )
				assert skip_list.find('Key2'      ) == 20

				skip_list.insert('Some Key' ,     10      )
				skip_list.insert('Key2' ,     8      )
				skip_list.insert('V' ,     13      )

				assert skip_list.find('Y'      ) is None
				assert skip_list.find('Key2'      ) == 8
				assert skip_list.find('Some Key'      ) == 10
				assert skip_list.find('V'      ) == 13







def 			lowerCamelCase_		(       )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()
				skip_list.delete('Some key'      )

				assert len(skip_list.head.forward      ) == 0







def 			lowerCamelCase_		(       )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()

				skip_list.insert('Key1' ,     12      )
				skip_list.insert('V' ,     13      )
				skip_list.insert('X' ,     14      )
				skip_list.insert('Key2' ,     15      )

				skip_list.delete('V'      )
				skip_list.delete('Key2'      )

				assert skip_list.find('V'      ) is None
				assert skip_list.find('Key2'      ) is None







def 			lowerCamelCase_		(       )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()

				skip_list.insert('Key1' ,     12      )
				skip_list.insert('V' ,     13      )
				skip_list.insert('X' ,     14      )
				skip_list.insert('Key2' ,     15      )

				skip_list.delete('V'      )
				assert skip_list.find('V'      ) is None
				assert skip_list.find('X'      ) == 14
				assert skip_list.find('Key1'      ) == 12
				assert skip_list.find('Key2'      ) == 15

				skip_list.delete('X'      )
				assert skip_list.find('V'      ) is None
				assert skip_list.find('X'      ) is None
				assert skip_list.find('Key1'      ) == 12
				assert skip_list.find('Key2'      ) == 15

				skip_list.delete('Key1'      )
				assert skip_list.find('V'      ) is None
				assert skip_list.find('X'      ) is None
				assert skip_list.find('Key1'      ) is None
				assert skip_list.find('Key2'      ) == 15

				skip_list.delete('Key2'      )
				assert skip_list.find('V'      ) is None
				assert skip_list.find('X'      ) is None
				assert skip_list.find('Key1'      ) is None
				assert skip_list.find('Key2'      ) is None







def 			lowerCamelCase_		(       )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()

				skip_list.insert('Key1' ,     12      )
				skip_list.insert('V' ,     13      )
				skip_list.insert('X' ,     142      )
				skip_list.insert('Key2' ,     15      )

				skip_list.delete('X'      )

				def traverse_keys(UpperCamelCase__				:      int      ):
								yield node.key
								for forward_node in node.forward:
												yield from traverse_keys(UpperCamelCase__      )

				assert len(set(traverse_keys(skip_list.head      )      )      ) == 4







def 			lowerCamelCase_		(       )     ->       Optional[Any]:





				"""simple docstring"""


				def is_sorted(UpperCamelCase__				:      Tuple      ):
								return all(next_item >= item for item, next_item in zip(UpperCamelCase__ ,     lst[1:]      )      )

				__lowerCamelCase		       =    SkipList()
				for i in range(10      ):
								skip_list.insert(UpperCamelCase__ ,     UpperCamelCase__      )
				assert is_sorted(list(UpperCamelCase__      )      )
				skip_list.delete(5      )
				skip_list.delete(8      )
				skip_list.delete(2      )
				assert is_sorted(list(UpperCamelCase__      )      )
				skip_list.insert(-12 ,     -12      )
				skip_list.insert(77 ,     77      )
				assert is_sorted(list(UpperCamelCase__      )      )







def 			lowerCamelCase_		(       )     ->       str:





				"""simple docstring"""


				for _ in range(100      ):
								# Repeat test 100 times due to the probabilistic nature of skip list
								# random values == random bugs
								test_insert()
								test_insert_overrides_existing_value()

								test_searching_empty_list_returns_none()
								test_search()

								test_deleting_item_from_empty_list_do_nothing()
								test_deleted_items_are_not_founded_by_find_method()
								test_delete_removes_only_given_key()
								test_delete_doesnt_leave_dead_nodes()

								test_iter_always_yields_sorted_values()







def 			lowerCamelCase_		(       )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    SkipList()
				skip_list.insert(2 ,     '2'      )
				skip_list.insert(4 ,     '4'      )
				skip_list.insert(6 ,     '4'      )
				skip_list.insert(4 ,     '5'      )
				skip_list.insert(8 ,     '4'      )
				skip_list.insert(9 ,     '4'      )

				skip_list.delete(4      )

				print(UpperCamelCase__      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







					def __len__(       self			:				Any		):
						return len(self.dataset		)







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Tuple=None		):
						lowerCAmelCase__	:						List[Any]  				=	loader
						lowerCAmelCase__	:						int  				=	infer
						lowerCAmelCase__	:						List[str]  				=	params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							lowerCAmelCase__	:						int  				=	None
						lowerCAmelCase__	:						Dict  				=	loader_batch_size

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							lowerCAmelCase__	:						int  				=	{}
							for k, element in self._loader_batch_data.items():
								if isinstance(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						str  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[int]  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									lowerCAmelCase__	:						Dict  				=	None
								elif isinstance(element[self._loader_batch_index]   ,torch.Tensor		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									lowerCAmelCase__	:						str  				=	element[self._loader_batch_index].unsqueeze(0		)
								elif isinstance(element[self._loader_batch_index]   ,np.ndarray		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									lowerCAmelCase__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						lowerCAmelCase__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**self.params		)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[str]  				=	first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	next(self.subiterator		)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Extremely similar to PipelineIterator in its unpacking mechanism
						# BUT, we have an extra required item which is the presence of `is_last`
						# That is because everything is flattened by `PipelineChunkIterator` we
						# need to keep track of how to regroup here in the original `process`
						# boundaries so that `process` and `postprocess` see the same data.

						# This iterator accumulates items (possibly while unbatching) until it
						# its a `is_last` and then just passes it on to the caller.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								lowerCAmelCase__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						str  				=	first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									lowerCAmelCase__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







					def __len__(       self			:				List[Any]		):
						return len(self.dataset		)







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







					def __len__(       self			:				str		):
						return len(self.dataset		)







					def __getitem__(       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




