






"""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  :      Tuple     ,		__snake_case  :      Any     ,		__snake_case  :      Any     ,		__snake_case  :      Optional[Any]					)			->							List[Any]:
											__magic_name__:	List[Any]							=       dataset
											__magic_name__:	List[Any]							=       process
											__magic_name__:	Optional[int]							=       params

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






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



class       __A		(   SCREAMING_SNAKE_CASE_			):

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

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

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

					def __iter__(			self  :      Union[str, Any]					)			->							Tuple:
											__magic_name__:	Optional[Any]							=       iter(self.loader					)
											return self

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

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






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



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      str     ,		__snake_case  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Dict     ,		__snake_case  :      List[Any]=None					)			->							str:
											super().__init__(__snake_case     ,		__snake_case     ,		__snake_case					)

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






					def   lowerCamelCase__   (			self  :      Tuple					)			->							str:
											if self.subiterator is None:
																	__magic_name__:	Any							=       self.infer(next(self.iterator					)     ,		**self.params					)
											try:
																	# Try to return next item
																	__magic_name__:	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
																	__magic_name__:	Optional[Any]							=       self.infer(next(self.iterator					)     ,		**self.params					)
																	__magic_name__:	int							=       next(self.subiterator					)
											return processed



class       __A		(   SCREAMING_SNAKE_CASE_			):

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






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[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.
											__magic_name__:	int							=       False
											__magic_name__:	List[Any]							=       []
											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:
																							__magic_name__:	Any							=       self.loader_batch_item()
																							__magic_name__:	List[str]							=       item.pop("""is_last"""					)
																							accumulator.append(__snake_case					)
																							if is_last:
																													return accumulator

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



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Dataset     ,		__snake_case  :      str					)			->							Tuple:
											__magic_name__:	int							=       dataset
											__magic_name__:	Dict							=       key

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






					def __getitem__(			self  :      int     ,		__snake_case  :      Optional[Any]					)			->							Dict:
											return self.dataset[i][self.key]



class       __A		(   SCREAMING_SNAKE_CASE_			):

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

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






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











"""simple docstring"""





from typing import List

from .keymap import KEYMAP, get_character



def   a    (     __UpperCAmelCase      :    str  )    ->							Optional[int]:

						def decorator(__UpperCAmelCase      :    List[str]  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += [key]
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



def   a    (     *__UpperCAmelCase      :    List[str]  )    ->							Union[str, Any]:

						def decorator(__UpperCAmelCase      :    Tuple  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += keys
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __new__(			cls  :      Optional[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any					)			->							List[Any]:
											__magic_name__:	str							=       super().__new__(cls     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
											if not hasattr(__snake_case     ,		"""key_handler"""					):
																	setattr(__snake_case     ,		"""key_handler"""     ,		{}					)
											setattr(__snake_case     ,		"""handle_input"""     ,		KeyHandler.handle_input					)

											for value in attrs.values():
																	__magic_name__:	List[str]							=       getattr(__snake_case     ,		"""handle_key"""     ,		[]					)
																	for key in handled_keys:
																							__magic_name__:	Any							=       value
											return new_cls






					@staticmethod
					def   lowerCamelCase__   (			cls  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	List[str]							=       get_character()
											if char != KEYMAP["undefined"]:
																	__magic_name__:	Union[str, Any]							=       ord(__snake_case					)
											__magic_name__:	Dict							=       cls.key_handler.get(__snake_case					)
											if handler:
																	__magic_name__:	List[str]							=       char
																	return handler(cls					)
											else:
																	return None



def   a    (     cls      :    List[Any]  )    ->							int:
						return KeyHandler(cls.__name__      ,   cls.__bases__      ,   cls.__dict__.copy()  )



