


"""simple docstring"""






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

from ..utils.generic import ModelOutput





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : List[Any] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					dataset
												__UpperCAmelCase   :						str      							=					process
												__UpperCAmelCase   :						List[str]      							=					params






					def __len__(       self    : int  ):
												'''simple docstring'''






												return len(self.dataset  )






					def __getitem__(       self    : Optional[Any] ,       UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.dataset[i]
												__UpperCAmelCase   :						Tuple      							=					self.process(UpperCamelCase ,       **self.params  )
												return processed






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Tuple ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : List[str]=None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					loader
												__UpperCAmelCase   :						Optional[Any]      							=					infer
												__UpperCAmelCase   :						str      							=					params
												if loader_batch_size == 1:
																			# Let's spare some time by deactivating altogether
																			__UpperCAmelCase   :						List[str]      							=					None
												__UpperCAmelCase   :						Tuple      							=					loader_batch_size

												# Internal bookkeeping
												__UpperCAmelCase   :						str      							=					None
												__UpperCAmelCase   :						Dict      							=					None






					def __len__(       self    : Any  ):
												'''simple docstring'''






												return len(self.loader  )






					def __iter__(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					iter(self.loader  )
												return self






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												if isinstance(self._loader_batch_data ,       torch.Tensor  ):
																			# Batch data is simple tensor, just fetch the slice
																			__UpperCAmelCase   :						Optional[int]      							=					self._loader_batch_data[self._loader_batch_index]
												else:
																			# Batch data is assumed to be BaseModelOutput (or dict)
																			__UpperCAmelCase   :						List[str]      							=					{}
																			for k, element in self._loader_batch_data.items():
																										if isinstance(UpperCamelCase ,       UpperCamelCase  ):
																																	# Convert ModelOutput to tuple first
																																	__UpperCAmelCase   :						List[str]      							=					element.to_tuple()
																																	if isinstance(element[0] ,       torch.Tensor  ):
																																								__UpperCAmelCase   :						str      							=					tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																	elif isinstance(element[0] ,       np.ndarray  ):
																																								__UpperCAmelCase   :						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(UpperCamelCase ,       UpperCamelCase  ):
																																	# Those are stored as lists of tensors so need specific unbatching.
																																	if isinstance(element[0] ,       torch.Tensor  ):
																																								__UpperCAmelCase   :						int      							=					tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																	elif isinstance(element[0] ,       np.ndarray  ):
																																								__UpperCAmelCase   :						Union[str, Any]      							=					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
																																	__UpperCAmelCase   :						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

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






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






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






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Tuple ,       UpperCamelCase    : Any ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : str ,       UpperCamelCase    : List[str]=None  ):
												'''simple docstring'''






												super().__init__(UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )






					def __iter__(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					iter(self.loader  )
												__UpperCAmelCase   :						Optional[Any]      							=					None
												return self






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												if self.subiterator is None:
																			__UpperCAmelCase   :						Any      							=					self.infer(next(self.iterator  ) ,       **self.params  )
												try:
																			# Try to return next item
																			__UpperCAmelCase   :						List[Any]      							=					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
																			__UpperCAmelCase   :						Optional[Any]      							=					self.infer(next(self.iterator  ) ,       **self.params  )
																			__UpperCAmelCase   :						Dict      							=					next(self.subiterator  )
												return processed






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __iter__(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					iter(self.loader  )
												return self






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					False
												__UpperCAmelCase   :						Optional[int]      							=					[]
												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:
																										__UpperCAmelCase   :						Optional[int]      							=					self.loader_batch_item()
																										__UpperCAmelCase   :						Dict      							=					item.pop("""is_last"""  )
																										accumulator.append(UpperCamelCase  )
																										if is_last:
																																	return accumulator

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






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Dataset ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					dataset
												__UpperCAmelCase   :						str      							=					key






					def __len__(       self    : Optional[int]  ):
												'''simple docstring'''






												return len(self.dataset  )






					def __getitem__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												return self.dataset[i][self.key]






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : List[Any] ,       UpperCamelCase    : Dataset ,       UpperCamelCase    : str ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					dataset
												__UpperCAmelCase   :						str      							=					keya
												__UpperCAmelCase   :						Tuple      							=					keya






					def __len__(       self    : str  ):
												'''simple docstring'''






												return len(self.dataset  )






					def __getitem__(       self    : Union[str, Any] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




"""simple docstring"""






from collections.abc import Callable
def      lowerCamelCase       (     _UpperCamelCase		:      Callable[[float], float]       ,			_UpperCamelCase		:      float       ,			_UpperCamelCase		:      float							)		->       float:

							'''simple docstring'''



							__UpperCAmelCase   :						float      							=					a
							__UpperCAmelCase   :						float      							=					b
							if function(_UpperCamelCase							) == 0:  # one of the a or b is a root for the function
														return a
							elif function(_UpperCamelCase							) == 0:
														return b
							elif (
							    function(_UpperCamelCase							) * function(_UpperCamelCase							) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval."""							)
							else:
														__UpperCAmelCase   :						float      							=					start + (end - start) / 2.0
														while abs(start - mid							) > 1_0**-7:  # until precisely equals to 10^-7
																					if function(_UpperCamelCase							) == 0:
																												return mid
																					elif function(_UpperCamelCase							) * function(_UpperCamelCase							) < 0:
																												__UpperCAmelCase   :						Union[str, Any]      							=					mid
																					else:
																												__UpperCAmelCase   :						List[Any]      							=					mid
																					__UpperCAmelCase   :						Optional[Any]      							=					start + (end - start) / 2.0
														return mid
def      lowerCamelCase       (     _UpperCamelCase		:      float							)		->       float:

							'''simple docstring'''



							return x**3 - 2 * x - 5


if __name__ == "__main__":
							print(bisection(f, 1, 1000))

							import doctest

							doctest.testmod()
