


"""simple docstring"""






def 			A__		(					_UpperCAmelCase    :      str	)							->	str:

	'''simple docstring'''


	snake_case__  :						str		=      word.upper()
	snake_case__  :						List[Any]		=      0
	snake_case__  :						Union[str, Any]		=      {}
	snake_case__  :						int		=      []

	for letter in word:
		if letter not in letter_nums:
			snake_case__  :						Tuple		=      str(_UpperCAmelCase	)
			next_num += 1
		word_pattern.append(letter_nums[letter]	)
	return ".".join(_UpperCAmelCase	)


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

					lowercase  		=			time.time()
					with open("""dictionary.txt""") as in_file:
										lowercase  		=			in_file.read().splitlines()

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

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

					lowercase  		=			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.









import torch

from transformers import AutoTokenizer







class      _lowercase					(				lowercase__):


       """simple docstring"""




       def __init__(  self				:     Optional[Any]   , __lowerCamelCase				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    AutoTokenizer.from_pretrained(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]   , __lowerCamelCase				:     str			):


             '''simple docstring'''



             if isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							List[Any]       =    None
                   for l in x:
                         lowerCamelCase__						:							str       =    self.tokenizer(
                             __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )
                         lowerCamelCase__						:							Dict       =    torch.tensor([len(__lowerCamelCase			)]			)
                         if d is not None:
                               for k in d.keys():
                                     lowerCamelCase__						:							Tuple       =    torch.cat((d[k], t[k])   , 0			)
                         else:
                               lowerCamelCase__						:							Union[str, Any]       =    t

                   lowerCamelCase__						:							List[Any]       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"			)			)
                   lowerCamelCase__						:							Dict       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"			)			)

             elif isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							Tuple       =    self.tokenizer(
                       __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )

             else:
                   raise Exception(
                       "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                       " strings` for supports are supported."			)

             return d





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[Any]=0.7_0			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    []
             for idx in range(len(W_query["input_ids"]			)			):
                   lowerCamelCase__						:							str       =    range(p_start.shape[1]			)

                   lowerCamelCase__						:							int       =    []
                   for start_id in start_indexes:
                         for end_id in end_indexes:
                               if start_id < end_id:
                                     output.append(
                                         (
                                             start_id,
                                             end_id,
                                             p_start[idx][start_id].item(),
                                             p_end[idx][end_id].item(),
                                         )			)

                   output.sort(key=lambda __lowerCamelCase			: (tup[2] * tup[3])   , reverse=__lowerCamelCase			)
                   lowerCamelCase__						:							str       =    []
                   for k in range(len(__lowerCamelCase			)			):
                         if output[k][2] * output[k][3] >= thresh:
                               lowerCamelCase__       ,  lowerCamelCase__						:							Optional[int]       =    output[k][0], output[k][1]
                               lowerCamelCase__						:							Tuple       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]			)
                               temp.append((decoded, output[k][2] * output[k][3])			)

                   final_outputs.append(__lowerCamelCase			)

             return final_outputs


