


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

from ..utils.generic import ModelOutput




class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Union[str, Any]:
											a__      =dataset
											a__      =process
											a__      =params





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





				def __getitem__(				self       ,  lowercase_)  -> str:
											a__      =self.dataset[i]
											a__      =self.process(lowercase_       ,  **self.params)
											return processed







class       lowercase_					(lowercase__   ):





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

											# Internal bookkeeping
											a__      =None
											a__      =None





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





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





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

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





				def 						__UpperCamelCase    (				self)  -> List[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
											a__      =next(self.iterator)
											a__      =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):
																									a__      =processed
																		else:
																									a__      =list(processed.keys())[0]
																									a__      =processed[key]
																		if isinstance(lowercase_       ,  lowercase_):
																									a__      =len(lowercase_)
																		else:
																									a__      =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.
																									a__      =observed_batch_size
																		# Setting internal index to unwrap the batch
																		a__      =processed
																		a__      =0
																		return self.loader_batch_item()
											else:
																		# We're not unrolling batches
																		return processed







class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None)  -> Optional[Any]:
											super().__init__(lowercase_       ,  lowercase_       ,  lowercase_)





				def __iter__(				self)  -> str:
											a__      =iter(self.loader)
											a__      =None
											return self





				def 						__UpperCamelCase    (				self)  -> Tuple:
											if self.subiterator is None:
																		a__      =self.infer(next(self.iterator)       ,  **self.params)
											try:
																		# Try to return next item
																		a__      =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
																		a__      =self.infer(next(self.iterator)       ,  **self.params)
																		a__      =next(self.subiterator)
											return processed







class       lowercase_					(lowercase__   ):





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





				def 						__UpperCamelCase    (				self)  -> 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.
											a__      =False
											a__      =[]
											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:
																									a__      =self.loader_batch_item()
																									a__      =item.pop('is_last')
																									accumulator.append(lowercase_)
																									if is_last:
																																return accumulator

											while not is_last:
																		a__      =self.infer(next(self.iterator)       ,  **self.params)
																		if self.loader_batch_size is not None:
																									if isinstance(lowercase_       ,  torch.Tensor):
																																a__      =processed
																									else:
																																a__      =list(processed.keys())[0]
																																a__      =processed[key]
																									if isinstance(lowercase_       ,  lowercase_):
																																a__      =len(lowercase_)
																									else:
																																a__      =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.
																																a__      =observed_batch_size
																									a__      =processed
																									a__      =0
																									while self._loader_batch_index < self.loader_batch_size:
																																a__      =self.loader_batch_item()
																																a__      =item.pop('is_last')
																																accumulator.append(lowercase_)
																																if is_last:
																																							return accumulator
																		else:
																									a__      =processed
																									a__      =item.pop('is_last')
																									accumulator.append(lowercase_)
											return accumulator







class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Union[str, Any]:
											a__      =dataset
											a__      =key





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





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







class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =dataset
											a__      =keya
											a__      =keya





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





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





from __future__ import annotations

_lowerCAmelCase:						str   				=       '#'




class       lowercase_					:





				def __init__(				self)  -> None:
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											a__      =self._trie
											for char in text:
																		if char not in trie:
																									a__      ={}
																		a__      =trie[char]
											a__      =True





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple | list:
											a__      =self._trie
											for char in prefix:
																		if char in trie:
																									a__      =trie[char]
																		else:
																									return []
											return self._elements(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> tuple:
											a__      =[]
											for c, v in d.items():
																		a__      =[' '] if c == END else [(c + s) for s in self._elements(lowercase_)]
																		result.extend(lowercase_)
											return tuple(lowercase_)







_lowerCAmelCase:						Optional[int]   				=       Trie()
_lowerCAmelCase:						List[str]   				=       ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)






def   _lowercase(					__a				:						str     ):
							a__      =trie.find_word(__a     )
							return tuple(string + word for word in suffixes     )






def   _lowercase(					):
							print(autocomplete_using_trie('de'     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

