


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

from ..utils.generic import ModelOutput





class      __A     (snake_case__):



							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		process
														snake_case_           =		params



							def __len__(				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      List[str]		,       UpperCAmelCase_							:      Any       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.dataset[i]
														snake_case_           =		self.process(UpperCAmelCase_		,       **self.params       )
														return processed





class      __A     (snake_case__):



							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      str=None       )					->Dict:



														"""simple docstring"""



														snake_case_           =		loader
														snake_case_           =		infer
														snake_case_           =		params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					snake_case_           =		None
														snake_case_           =		loader_batch_size

														# Internal bookkeeping
														snake_case_           =		None
														snake_case_           =		None



							def __len__(				self							:      int       )					->Optional[int]:



														"""simple docstring"""



														return len(self.loader       )



							def __iter__(				self							:      Dict       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														return self



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Dict:



														"""simple docstring"""



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

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



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





class      __A     (snake_case__):



							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]=None       )					->str:



														"""simple docstring"""



														super().__init__(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



							def __iter__(				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														snake_case_           =		None
														return self



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[Any]:



														"""simple docstring"""



														if self.subiterator is None:
																					snake_case_           =		self.infer(next(self.iterator       )		,       **self.params       )
														try:
																					# Try to return next item
																					snake_case_           =		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
																					snake_case_           =		self.infer(next(self.iterator       )		,       **self.params       )
																					snake_case_           =		next(self.subiterator       )
														return processed





class      __A     (snake_case__):



							def __iter__(				self							:      List[Any]       )					->int:



														"""simple docstring"""



														snake_case_           =		iter(self.loader       )
														return self



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		False
														snake_case_           =		[]
														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:
																												snake_case_           =		self.loader_batch_item()
																												snake_case_           =		item.pop("""is_last"""       )
																												accumulator.append(UpperCAmelCase_       )
																												if is_last:
																																			return accumulator

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





class      __A     (snake_case__):



							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      Dataset		,       UpperCAmelCase_							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		key



							def __len__(				self							:      Union[str, Any]       )					->int:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



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





class      __A     (snake_case__):



							def __init__(				self							:      Any		,       UpperCAmelCase_							:      Dataset		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str       )					->Dict:



														"""simple docstring"""



														snake_case_           =		dataset
														snake_case_           =		keya
														snake_case_           =		keya



							def __len__(				self							:      Dict       )					->List[str]:



														"""simple docstring"""



														return len(self.dataset       )



							def __getitem__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



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







"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		i // 3
							snake_case_           =		i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =		(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE				, quotient + remainder				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							)
							snake_case_           =		list(_SCREAMING_SNAKE_CASE				)
							shuffle(_SCREAMING_SNAKE_CASE				)
							return "".join(_SCREAMING_SNAKE_CASE				)

							# random is a generalised function for letters, characters and numbers







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

							if len(_SCREAMING_SNAKE_CASE				) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		any(char in punctuation for char in password				)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



