



"""simple docstring"""







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

from ..utils.generic import ModelOutput

class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				Optional[Any]   ,	_lowercase							:				str   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Union[str, Any]		)							->					Dict:
									snake_case   :			str						    = dataset
									snake_case   :			Dict						    = process
									snake_case   :			Optional[Any]						    = params



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



			def __getitem__(			self							:				Union[str, Any]   ,	_lowercase							:				Optional[Any]		)							->					Dict:
									snake_case   :			Optional[Any]						    = self.dataset[i]
									snake_case   :			Dict						    = self.process(_lowercase   ,	**self.params		)
									return processed



class       _a  ( SCREAMING_SNAKE_CASE__):



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

									# Internal bookkeeping
									snake_case   :			Tuple						    = None
									snake_case   :			List[Any]						    = None



			def __len__(			self							:				Dict		)							->					str:
									return len(self.loader		)



			def __iter__(			self							:				Tuple		)							->					Tuple:
									snake_case   :			List[str]						    = iter(self.loader		)
									return self



			def 				__lowercase					(			self							:				Optional[int]		)							->					List[str]:
									if isinstance(self._loader_batch_data   ,	torch.Tensor		):
															# Batch data is simple tensor, just fetch the slice
															snake_case   :			Tuple						    = self._loader_batch_data[self._loader_batch_index]
									else:
															# Batch data is assumed to be BaseModelOutput (or dict)
															snake_case   :			List[str]						    = {}
															for k, element in self._loader_batch_data.items():
																					if isinstance(_lowercase   ,	_lowercase		):
																											# Convert ModelOutput to tuple first
																											snake_case   :			Any						    = element.to_tuple()
																											if isinstance(element[0]   ,	torch.Tensor		):
																																	snake_case   :			List[Any]						    = tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																											elif isinstance(element[0]   ,	np.ndarray		):
																																	snake_case   :			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		):
																																	snake_case   :			Optional[int]						    = tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																											elif isinstance(element[0]   ,	np.ndarray		):
																																	snake_case   :			str						    = 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
																											snake_case   :			Tuple						    = 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

																											snake_case   :			Tuple						    = 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
																											snake_case   :			List[str]						    = np.expand_dims(element[self._loader_batch_index]   ,	0		)
																					else:
																											# This is typically a list, so no need to `unsqueeze`.
																											snake_case   :			Any						    = element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
															snake_case   :			Union[str, Any]						    = self._loader_batch_data.__class__(_lowercase		)
									self._loader_batch_index += 1
									return result



			def 				__lowercase					(			self							:				Dict		)							->					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
									snake_case   :			Optional[int]						    = next(self.iterator		)
									snake_case   :			str						    = 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		):
																					snake_case   :			Optional[int]						    = processed
															else:
																					snake_case   :			str						    = list(processed.keys()		)[0]
																					snake_case   :			Tuple						    = processed[key]
															if isinstance(_lowercase   ,	_lowercase		):
																					snake_case   :			List[str]						    = len(_lowercase		)
															else:
																					snake_case   :			Dict						    = 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.
																					snake_case   :			List[Any]						    = observed_batch_size
															# Setting internal index to unwrap the batch
															snake_case   :			Tuple						    = processed
															snake_case   :			Tuple						    = 0
															return self.loader_batch_item()
									else:
															# We're not unrolling batches
															return processed



class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				Tuple   ,	_lowercase							:				int   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[str]=None		)							->					Optional[Any]:
									super().__init__(_lowercase   ,	_lowercase   ,	_lowercase		)



			def __iter__(			self							:				Optional[int]		)							->					Union[str, Any]:
									snake_case   :			List[Any]						    = iter(self.loader		)
									snake_case   :			Any						    = None
									return self



			def 				__lowercase					(			self							:				Any		)							->					List[str]:
									if self.subiterator is None:
															snake_case   :			Dict						    = self.infer(next(self.iterator		)   ,	**self.params		)
									try:
															# Try to return next item
															snake_case   :			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
															snake_case   :			int						    = self.infer(next(self.iterator		)   ,	**self.params		)
															snake_case   :			List[str]						    = next(self.subiterator		)
									return processed



class       _a  ( SCREAMING_SNAKE_CASE__):



			def __iter__(			self							:				Optional[Any]		)							->					List[Any]:
									snake_case   :			Optional[int]						    = iter(self.loader		)
									return self



			def 				__lowercase					(			self							:				int		)							->					str:
									# 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.
									snake_case   :			Optional[int]						    = False
									snake_case   :			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:
																					snake_case   :			Union[str, Any]						    = self.loader_batch_item()
																					snake_case   :			Tuple						    = item.pop("is_last"		)
																					accumulator.append(_lowercase		)
																					if is_last:
																											return accumulator

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



class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				Union[str, Any]   ,	_lowercase							:				Dataset   ,	_lowercase							:				str		)							->					Optional[Any]:
									snake_case   :			Optional[int]						    = dataset
									snake_case   :			str						    = key



			def __len__(			self							:				Dict		)							->					Optional[Any]:
									return len(self.dataset		)



			def __getitem__(			self							:				int   ,	_lowercase							:				Union[str, Any]		)							->					Optional[int]:
									return self.dataset[i][self.key]



class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				Tuple   ,	_lowercase							:				Dataset   ,	_lowercase							:				str   ,	_lowercase							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = dataset
									snake_case   :			List[Any]						    = keya
									snake_case   :			List[Any]						    = keya



			def __len__(			self							:				int		)							->					Tuple:
									return len(self.dataset		)



			def __getitem__(			self							:				Tuple   ,	_lowercase							:				Optional[int]		)							->					List[Any]:
									return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}








"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



