


'''simple docstring'''





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

from ..utils.generic import ModelOutput







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										lowerCAmelCase_ :      Union[str, Any]    				=				dataset
										lowerCAmelCase_ :      Optional[Any]    				=				process
										lowerCAmelCase_ :      Union[str, Any]    				=				params




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




						def __getitem__(				self   ,						__lowercase							)					->    Union[str, Any]:
										lowerCAmelCase_ :      List[Any]    				=				self.dataset[i]
										lowerCAmelCase_ :      Optional[int]    				=				self.process(UpperCamelCase__   ,						**self.params							)
										return processed







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




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

										# Internal bookkeeping
										lowerCAmelCase_ :      Optional[int]    				=				None
										lowerCAmelCase_ :      int    				=				None




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




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




						def 						lowercase_ (				self							)					->    List[str]:





										if isinstance(self._loader_batch_data   ,						torch.Tensor							):
														# Batch data is simple tensor, just fetch the slice
														lowerCAmelCase_ :      Optional[Any]    				=				self._loader_batch_data[self._loader_batch_index]
										else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														lowerCAmelCase_ :      Union[str, Any]    				=				{}
														for k, element in self._loader_batch_data.items():
																		if isinstance(UpperCamelCase__   ,						UpperCamelCase__							):
																						# Convert ModelOutput to tuple first
																						lowerCAmelCase_ :      Dict    				=				element.to_tuple()
																						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_ :      Union[str, Any]    				=				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							):
																										lowerCAmelCase_ :      Union[str, Any]    				=				tuple(el[self._loader_batch_index].unsqueeze(0							) for el in element							)
																						elif isinstance(element[0]   ,						np.ndarray							):
																										lowerCAmelCase_ :      List[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
																						lowerCAmelCase_ :      int    				=				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_ :      Union[str, Any]    				=				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_ :      Tuple    				=				element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														lowerCAmelCase_ :      Any    				=				self._loader_batch_data.__class__(UpperCamelCase__							)
										self._loader_batch_index += 1
										return result




						def 						lowercase_ (				self							)					->    Tuple:





										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_ :      List[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							):
																		lowerCAmelCase_ :      Optional[int]    				=				processed
														else:
																		lowerCAmelCase_ :      int    				=				list(processed.keys()							)[0]
																		lowerCAmelCase_ :      int    				=				processed[key]
														if isinstance(UpperCamelCase__   ,						UpperCamelCase__							):
																		lowerCAmelCase_ :      List[Any]    				=				len(UpperCamelCase__							)
														else:
																		lowerCAmelCase_ :      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.
																		lowerCAmelCase_ :      List[Any]    				=				observed_batch_size
														# Setting internal index to unwrap the batch
														lowerCAmelCase_ :      List[Any]    				=				processed
														lowerCAmelCase_ :      int    				=				0
														return self.loader_batch_item()
										else:
														# We're not unrolling batches
														return processed







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase=None							)					->    Any:
										super().__init__(UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__							)




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




						def 						lowercase_ (				self							)					->    Dict:





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







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




						def __iter__(				self							)					->    Dict:
										lowerCAmelCase_ :      Dict    				=				iter(self.loader							)
										return self




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      List[Any]    				=				False
										lowerCAmelCase_ :      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:
																		lowerCAmelCase_ :      Tuple    				=				self.loader_batch_item()
																		lowerCAmelCase_ :      Any    				=				item.pop('''is_last'''							)
																		accumulator.append(UpperCamelCase__							)
																		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(UpperCamelCase__   ,						torch.Tensor							):
																						lowerCAmelCase_ :      Tuple    				=				processed
																		else:
																						lowerCAmelCase_ :      Union[str, Any]    				=				list(processed.keys()							)[0]
																						lowerCAmelCase_ :      List[str]    				=				processed[key]
																		if isinstance(UpperCamelCase__   ,						UpperCamelCase__							):
																						lowerCAmelCase_ :      List[Any]    				=				len(UpperCamelCase__							)
																		else:
																						lowerCAmelCase_ :      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.
																						lowerCAmelCase_ :      List[str]    				=				observed_batch_size
																		lowerCAmelCase_ :      List[Any]    				=				processed
																		lowerCAmelCase_ :      str    				=				0
																		while self._loader_batch_index < self.loader_batch_size:
																						lowerCAmelCase_ :      Any    				=				self.loader_batch_item()
																						lowerCAmelCase_ :      List[Any]    				=				item.pop('''is_last'''							)
																						accumulator.append(UpperCamelCase__							)
																						if is_last:
																										return accumulator
														else:
																		lowerCAmelCase_ :      int    				=				processed
																		lowerCAmelCase_ :      List[str]    				=				item.pop('''is_last'''							)
																		accumulator.append(UpperCamelCase__							)
										return accumulator







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										lowerCAmelCase_ :      Optional[Any]    				=				dataset
										lowerCAmelCase_ :      Dict    				=				key




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




						def __getitem__(				self   ,						__lowercase							)					->    Dict:
										return self.dataset[i][self.key]







class 				snake_case__(							UpperCamelCase_							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[Any]:
										lowerCAmelCase_ :      str    				=				dataset
										lowerCAmelCase_ :      List[str]    				=				keya
										lowerCAmelCase_ :      Tuple    				=				keya




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




						def __getitem__(				self   ,						__lowercase							)					->    Dict:
										return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				if (inductance, frequency, reactance).count(0 ) != 1:
								raise ValueError('''One and only one argument must be 0''' )
				if inductance < 0:
								raise ValueError('''Inductance cannot be negative''' )
				if frequency < 0:
								raise ValueError('''Frequency cannot be negative''' )
				if reactance < 0:
								raise ValueError('''Inductive reactance cannot be negative''' )
				if inductance == 0:
								return {"inductance": reactance / (2 * pi * frequency)}
				elif frequency == 0:
								return {"frequency": reactance / (2 * pi * inductance)}
				elif reactance == 0:
								return {"reactance": 2 * pi * frequency * inductance}
				else:
								raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
						import doctest

						doctest.testmod()