





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

from ..utils.generic import ModelOutput



class    _a     (				lowercase__							):






							"""simple docstring"""

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

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






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







class    _a     (				lowercase__							):






							"""simple docstring"""

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

													# Internal bookkeeping
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					None
													SCREAMING_SNAKE_CASE__     :				Tuple   =					None

							def __len__(      self				:     Optional[int]    )	->Optional[Any]:
													return len(self.loader    )

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

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

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






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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     Tuple							,	a				:     Union[str, Any]							,	a				:     Union[str, Any]							,	a				:     List[str]							,	a				:     Any=None    )	->List[Any]:
													super().__init__(a							,	a							,	a    )

							def __iter__(      self				:     Optional[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					iter(self.loader    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					None
													return self






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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __iter__(      self				:     str    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					iter(self.loader    )
													return self






							def      A_  (      self				:     List[Any]    )	->Optional[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.
													SCREAMING_SNAKE_CASE__     :				List[str]   =					False
													SCREAMING_SNAKE_CASE__     :				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:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					self.loader_batch_item()
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					item.pop("is_last"    )
																									accumulator.append(a    )
																									if is_last:
																															return accumulator

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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     List[str]							,	a				:     Dataset							,	a				:     str    )	->Any:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					dataset
													SCREAMING_SNAKE_CASE__     :				Any   =					key

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






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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     Tuple							,	a				:     Dataset							,	a				:     str							,	a				:     str    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					dataset
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					keya
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					keya

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






							def __getitem__(      self				:     Any							,	a				:     Union[str, Any]    )	->Any:
													return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  bool = False     ):





						'''simple docstring'''


						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
												raise ValueError(
												    "Warning: upper bound of deterministic test is exceeded. "
												    "Pass allow_probable=True to allow probabilistic test. "
												    "A return value of True indicates a probable prime."     )
						# array bounds provided by analysis
						SCREAMING_SNAKE_CASE__     :				List[str]   =					[
						    2_047,
						    1_373_653,
						    25_326_001,
						    3_215_031_751,
						    2_152_302_898_747,
						    3_474_749_660_383,
						    341_550_071_728_321,
						    1,
						    3_825_123_056_546_413_051,
						    1,
						    1,
						    318_665_857_834_031_151_167_461,
						    3_317_044_064_679_887_385_961_981,
						]

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
						for idx, _p in enumerate(_lowerCamelCase			,   1     ):
												if n < _p:
																		# then we have our last prime to check
																		SCREAMING_SNAKE_CASE__     :				Dict   =					primes[:idx]
																		break
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												SCREAMING_SNAKE_CASE__     :				str   =					False
												for r in range(_lowerCamelCase     ):
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pow(_lowerCamelCase			,   d * 2**r			,   _lowerCamelCase     )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								SCREAMING_SNAKE_CASE__     :				str   =					True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						assert not miller_rabin(561     )
						assert miller_rabin(563     )
						# 2047

						assert not miller_rabin(838_201     )
						assert miller_rabin(838_207     )
						# 1_373_653

						assert not miller_rabin(17_316_001     )
						assert miller_rabin(17_316_017     )
						# 25_326_001

						assert not miller_rabin(3_078_386_641     )
						assert miller_rabin(3_078_386_653     )
						# 3_215_031_751

						assert not miller_rabin(1_713_045_574_801     )
						assert miller_rabin(1_713_045_574_819     )
						# 2_152_302_898_747

						assert not miller_rabin(2_779_799_728_307     )
						assert miller_rabin(2_779_799_728_327     )
						# 3_474_749_660_383

						assert not miller_rabin(113_850_023_909_441     )
						assert miller_rabin(113_850_023_909_527     )
						# 341_550_071_728_321

						assert not miller_rabin(1_275_041_018_848_804_351     )
						assert miller_rabin(1_275_041_018_848_804_391     )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(79_666_464_458_507_787_791_867     )
						assert miller_rabin(79_666_464_458_507_787_791_951     )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(552_840_677_446_647_897_660_333     )
						assert miller_rabin(552_840_677_446_647_897_660_359     )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
				test_miller_rabin()