



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

from ..utils.generic import ModelOutput
class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		dataset
						lowerCamelCase__ :      Optional[int]								=		process
						lowerCamelCase__ :      Optional[int]								=		params





			def __len__(self				):
						return len(self.dataset				)





			def __getitem__(self							,    __magic_name__				):
						lowerCamelCase__ :      Dict								=		self.dataset[i]
						lowerCamelCase__ :      Tuple								=		self.process(__magic_name__							,    **self.params				)
						return processed







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=None				):
						lowerCamelCase__ :      List[str]								=		loader
						lowerCamelCase__ :      Tuple								=		infer
						lowerCamelCase__ :      str								=		params
						if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									lowerCamelCase__ :      Tuple								=		None
						lowerCamelCase__ :      Tuple								=		loader_batch_size

						# Internal bookkeeping
						lowerCamelCase__ :      Union[str, Any]								=		None
						lowerCamelCase__ :      Optional[Any]								=		None





			def __len__(self				):
						return len(self.loader				)





			def __iter__(self				):
						lowerCamelCase__ :      Tuple								=		iter(self.loader				)
						return self





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

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





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







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=None				):
						super().__init__(__magic_name__							,    __magic_name__							,    __magic_name__				)





			def __iter__(self				):
						lowerCamelCase__ :      Optional[Any]								=		iter(self.loader				)
						lowerCamelCase__ :      Dict								=		None
						return self





			def        _snake_case    (self				):
						if self.subiterator is None:
									lowerCamelCase__ :      List[Any]								=		self.infer(next(self.iterator				)							,    **self.params				)
						try:
									# Try to return next item
									lowerCamelCase__ :      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
									lowerCamelCase__ :      List[Any]								=		self.infer(next(self.iterator				)							,    **self.params				)
									lowerCamelCase__ :      str								=		next(self.subiterator				)
						return processed







class 				__A					( A_      ):





			def __iter__(self				):
						lowerCamelCase__ :      int								=		iter(self.loader				)
						return self





			def        _snake_case    (self				):
						# 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.
						lowerCamelCase__ :      Optional[int]								=		False
						lowerCamelCase__ :      Union[str, 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:
												lowerCamelCase__ :      Dict								=		self.loader_batch_item()
												lowerCamelCase__ :      Union[str, Any]								=		item.pop("""is_last"""				)
												accumulator.append(__magic_name__				)
												if is_last:
															return accumulator

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







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		dataset
						lowerCamelCase__ :      Optional[Any]								=		key





			def __len__(self				):
						return len(self.dataset				)





			def __getitem__(self							,    __magic_name__				):
						return self.dataset[i][self.key]







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      int								=		dataset
						lowerCamelCase__ :      Dict								=		keya
						lowerCamelCase__ :      List[str]								=		keya





			def __len__(self				):
						return len(self.dataset				)





			def __getitem__(self							,    __magic_name__				):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_lowercase     							=						(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase     							=						[ord(letter) for letter in string.ascii_lowercase]
_lowercase     							=						{ord(char) for char in VALID_CHARS}

_lowercase     							=						["the", "be", "to", "of", "and", "in", "that", "have"]






def      _A				(UpperCamelCase		:      list[int]      ,			UpperCamelCase		:      tuple[int, ...] )  ->str | None:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		""
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int

			for keychar, cipherchar in zip(cycle(UpperCamelCase )      ,			UpperCamelCase ):
						lowerCamelCase__ :      str								=		cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(UpperCamelCase )

			return decoded






def      _A				(UpperCamelCase		:      list[int] )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      list[str]								=		[]
			for key in product(UpperCamelCase      ,			repeat=3 ):
						lowerCamelCase__ :      Union[str, Any]								=		try_key(UpperCamelCase      ,			UpperCamelCase )
						if encoded is not None:
									possibles.append(UpperCamelCase )
			return possibles






def      _A				(UpperCamelCase		:      list[str]      ,			UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			return [possible for possible in possibles if common_word in possible.lower()]






def      _A				(UpperCamelCase		:      str = "p059_cipher.txt" )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      list[int]
			lowerCamelCase__ :      list[str]
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str								=		Path(UpperCamelCase ).parent.joinpath(UpperCamelCase ).read_text(encoding="""utf-8""" )

			lowerCamelCase__ :      str								=		[int(UpperCamelCase ) for number in data.strip().split(""",""" )]

			lowerCamelCase__ :      Optional[Any]								=		filter_valid_chars(UpperCamelCase )
			for common_word in COMMON_WORDS:
						lowerCamelCase__ :      Optional[int]								=		filter_common_word(UpperCamelCase      ,			UpperCamelCase )
						if len(UpperCamelCase ) == 1:
									break

			lowerCamelCase__ :      Union[str, Any]								=		possibles[0]
			return sum(ord(UpperCamelCase ) for char in decoded_text )


if __name__ == "__main__":
				print(F'''{solution() = }''')



