






def    lowerCAmelCase__		(			_a				:	Optional[int]			):
		snake_case_ :							List[str]         =				word.upper()
		snake_case_ :							Optional[Any]         =				0
		snake_case_ :							List[Any]         =				{}
		snake_case_ :							List[str]         =				[]

		for letter in word:
				if letter not in letter_nums:
						snake_case_ :							Dict         =				str(_a			)
						next_num += 1
				word_pattern.append(letter_nums[letter]			)
		return ".".join(_a			)


if __name__ == "__main__":
							import pprint
							import time

							lowercase     :							Optional[int]	     =			time.time()
							with open('''dictionary.txt''') as in_file:
														lowercase     :							Optional[Any]	     =			in_file.read().splitlines()

							lowercase     :							dict	     =			{}
							for word in word_list:
														lowercase     :							Dict	     =			get_word_pattern(word)
														if pattern in all_patterns:
																					all_patterns[pattern].append(word)
														else:
																					lowercase     :							Tuple	     =			[word]

							with open('''word_patterns.txt''', '''w''') as out_file:
														out_file.write(pprint.pformat(all_patterns))

							lowercase     :							Tuple	     =			round(time.time() - start_time, 2)
							print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
							# Done!  9,581 word patterns found in 0.58 seconds.







'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask

