



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

from ..utils.generic import ModelOutput



class 							__snake_case		(	snake_case_		):




							def __init__(			self	:			List[str]							,							A_	:			Tuple							,							A_	:			Any							,							A_	:			Optional[int]):
									lowerCAmelCase_     :	List[str]							   =					dataset
									lowerCAmelCase_     :	Dict							   =					process
									lowerCAmelCase_     :	Any							   =					params




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







							def __getitem__(			self	:			Union[str, Any]							,							A_	:			Tuple):
									lowerCAmelCase_     :	int							   =					self.dataset[i]
									lowerCAmelCase_     :	str							   =					self.process(A_							,							**self.params)
									return processed





class 							__snake_case		(	snake_case_		):




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

									# Internal bookkeeping
									lowerCAmelCase_     :	int							   =					None
									lowerCAmelCase_     :	Tuple							   =					None




							def __len__(			self	:			Tuple):
									return len(self.loader)




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




							def   UpperCAmelCase__     (			self	:			Union[str, Any]):






									if isinstance(self._loader_batch_data							,							torch.Tensor):
											# Batch data is simple tensor, just fetch the slice
											lowerCAmelCase_     :	Union[str, Any]							   =					self._loader_batch_data[self._loader_batch_index]
									else:
											# Batch data is assumed to be BaseModelOutput (or dict)
											lowerCAmelCase_     :	List[str]							   =					{}
											for k, element in self._loader_batch_data.items():
													if isinstance(A_							,							A_):
															# Convert ModelOutput to tuple first
															lowerCAmelCase_     :	Tuple							   =					element.to_tuple()
															if isinstance(element[0]							,							torch.Tensor):
																	lowerCAmelCase_     :	Union[str, Any]							   =					tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
															elif isinstance(element[0]							,							np.ndarray):
																	lowerCAmelCase_     :	Dict							   =					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(A_							,							A_):
															# Those are stored as lists of tensors so need specific unbatching.
															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_     :	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_     :	Any							   =					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_     :	List[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_     :	List[Any]							   =					np.expand_dims(element[self._loader_batch_index]							,							0)
													else:
															# This is typically a list, so no need to `unsqueeze`.
															lowerCAmelCase_     :	Dict							   =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
											lowerCAmelCase_     :	Dict							   =					self._loader_batch_data.__class__(A_)
									self._loader_batch_index += 1
									return result







							def   UpperCAmelCase__     (			self	:			List[str]):






									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_     :	Tuple							   =					next(self.iterator)
									lowerCAmelCase_     :	Dict							   =					self.infer(A_							,							**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(A_							,							torch.Tensor):
													lowerCAmelCase_     :	Union[str, Any]							   =					processed
											else:
													lowerCAmelCase_     :	int							   =					list(processed.keys())[0]
													lowerCAmelCase_     :	Tuple							   =					processed[key]
											if isinstance(A_							,							A_):
													lowerCAmelCase_     :	str							   =					len(A_)
											else:
													lowerCAmelCase_     :	Tuple							   =					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_     :	Tuple							   =					observed_batch_size
											# Setting internal index to unwrap the batch
											lowerCAmelCase_     :	Any							   =					processed
											lowerCAmelCase_     :	Union[str, Any]							   =					0
											return self.loader_batch_item()
									else:
											# We're not unrolling batches
											return processed





class 							__snake_case		(	snake_case_		):




							def __init__(			self	:			Dict							,							A_	:			str							,							A_	:			Any							,							A_	:			List[Any]							,							A_	:			Optional[int]=None):
									super().__init__(A_							,							A_							,							A_)




							def __iter__(			self	:			str):
									lowerCAmelCase_     :	int							   =					iter(self.loader)
									lowerCAmelCase_     :	List[str]							   =					None
									return self







							def   UpperCAmelCase__     (			self	:			Optional[Any]):






									if self.subiterator is None:
											lowerCAmelCase_     :	List[str]							   =					self.infer(next(self.iterator)							,							**self.params)
									try:
											# Try to return next item
											lowerCAmelCase_     :	Tuple							   =					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_     :	str							   =					self.infer(next(self.iterator)							,							**self.params)
											lowerCAmelCase_     :	List[Any]							   =					next(self.subiterator)
									return processed





class 							__snake_case		(	snake_case_		):




							def __iter__(			self	:			int):
									lowerCAmelCase_     :	List[str]							   =					iter(self.loader)
									return self







							def   UpperCAmelCase__     (			self	:			List[Any]):
									lowerCAmelCase_     :	Union[str, Any]							   =					False
									lowerCAmelCase_     :	Dict							   =					[]
									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_     :	List[Any]							   =					self.loader_batch_item()
													lowerCAmelCase_     :	Dict							   =					item.pop('''is_last''')
													accumulator.append(A_)
													if is_last:
															return accumulator

									while not is_last:
											lowerCAmelCase_     :	Union[str, Any]							   =					self.infer(next(self.iterator)							,							**self.params)
											if self.loader_batch_size is not None:
													if isinstance(A_							,							torch.Tensor):
															lowerCAmelCase_     :	List[Any]							   =					processed
													else:
															lowerCAmelCase_     :	Tuple							   =					list(processed.keys())[0]
															lowerCAmelCase_     :	List[str]							   =					processed[key]
													if isinstance(A_							,							A_):
															lowerCAmelCase_     :	Union[str, Any]							   =					len(A_)
													else:
															lowerCAmelCase_     :	Union[str, Any]							   =					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_     :	Any							   =					observed_batch_size
													lowerCAmelCase_     :	Optional[int]							   =					processed
													lowerCAmelCase_     :	str							   =					0
													while self._loader_batch_index < self.loader_batch_size:
															lowerCAmelCase_     :	Optional[int]							   =					self.loader_batch_item()
															lowerCAmelCase_     :	List[Any]							   =					item.pop('''is_last''')
															accumulator.append(A_)
															if is_last:
																	return accumulator
											else:
													lowerCAmelCase_     :	Optional[Any]							   =					processed
													lowerCAmelCase_     :	Optional[Any]							   =					item.pop('''is_last''')
													accumulator.append(A_)
									return accumulator





class 							__snake_case		(	snake_case_		):




							def __init__(			self	:			int							,							A_	:			str							,							A_	:			Union[str, Any]):
									lowerCAmelCase_     :	Optional[Any]							   =					dataset
									lowerCAmelCase_     :	int							   =					key




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







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





class 							__snake_case		(	snake_case_		):




							def __init__(			self	:			List[Any]							,							A_	:			Any							,							A_	:			List[str]							,							A_	:			List[Any]):
									lowerCAmelCase_     :	str							   =					dataset
									lowerCAmelCase_     :	Dict							   =					keya
									lowerCAmelCase_     :	int							   =					keya




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







							def __getitem__(			self	:			int							,							A_	:			Tuple):
									return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







'''simple docstring'''
def 		A_(     A						:		list[int]):
							UpperCamelCase   			=     []
							if len(A) == 1:
														return [nums.copy()]
							for _ in range(len(A)):
														UpperCamelCase   			=     nums.pop(0)
														UpperCamelCase   			=     permute(A)
														for perm in permutations:
																					perm.append(A)
														result.extend(A)
														nums.append(A)
							return result







def 		A_(     A						:		str):

							def backtrack(A						:		str):
														if start == len(A) - 1:
																					output.append(nums[:])
														else:
																					for i in range(A		,     len(A)):
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]
																												backtrack(start + 1)
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]  # backtrack

							UpperCamelCase   			=     []
							backtrack(0)
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						lowerCAmelCase					:    Dict           = permutea([1, 2, 3])
						print(res)
						doctest.testmod()




