




'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	lowerCamelCase_				(			lowerCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					HerbertTokenizer
					lowercase__       =					HerbertTokenizerFast
					lowercase__       =					True






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt''',    encoding="utf-8"					) as f_data:
									_snake_case      			=			f_data.read().replace("\n\n",    "\n"					).strip()

							_snake_case      			=			[
							    "<s>",
							    "</s>",
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "w</w>",
							    "r</w>",
							    "t</w>",
							    "lo",
							    "low",
							    "er</w>",
							    "low</w>",
							    "lowest</w>",
							    "newer</w>",
							    "wider</w>",
							    ",</w>",
							    "<unk>",
							]
							_snake_case      			=			dict(zip(_SCREAMING_SNAKE_CASE,    range(len(_SCREAMING_SNAKE_CASE					)					)					)					)
							_snake_case      			=			["l o 123", "lo w 1456", "e r</w> 1789", ""]

							_snake_case      			=			os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["vocab_file"]					)
							_snake_case      			=			os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["merges_file"]					)
							with open(self.vocab_file,    "w"					) as fp:
									fp.write(json.dumps(_SCREAMING_SNAKE_CASE					)					)
							with open(self.merges_file,    "w"					) as fp:
									fp.write("\n".join(_SCREAMING_SNAKE_CASE					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			"lower newer"
							_snake_case      			=			"lower newer"
							return input_text, output_text






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tokenizer_class(vocab_file=self.vocab_file,    merges_file=self.merges_file					)

							_snake_case      			=			"lower"
							_snake_case      			=			["low", "er</w>"]
							_snake_case      			=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
							self.assertListEqual(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)

							_snake_case      			=			tokens + ["<unk>"]
							_snake_case      			=			[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					),    _SCREAMING_SNAKE_CASE					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):


							if not self.test_rust_tokenizer:
									return

							_snake_case      			=			self.get_tokenizer()
							_snake_case      			=			self.get_rust_tokenizer()

							_snake_case      			=			"lower,newer"

							_snake_case      			=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
							self.assertListEqual(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)

							_snake_case      			=			tokenizer.encode(_SCREAMING_SNAKE_CASE,    add_special_tokens=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			rust_tokenizer.encode(_SCREAMING_SNAKE_CASE,    add_special_tokens=_SCREAMING_SNAKE_CASE					)
							self.assertListEqual(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)

							_snake_case      			=			self.get_rust_tokenizer()
							_snake_case      			=			tokenizer.encode(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			rust_tokenizer.encode(_SCREAMING_SNAKE_CASE					)
							self.assertListEqual(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)






					@slow
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"					)

							_snake_case      			=			tokenizer.encode("konstruowanie sekwencji",    add_special_tokens=_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			tokenizer.encode("konstruowanie wielu sekwencji",    add_special_tokens=_SCREAMING_SNAKE_CASE					)

							_snake_case      			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]












def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	word.upper()
       UpperCamelCase_ 							=	0
       UpperCamelCase_ 							=	{}
       UpperCamelCase_ 							=	[]

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


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

     _UpperCAmelCase										=					time.time()
     with open('dictionary.txt') as in_file:
          _UpperCAmelCase										=					in_file.read().splitlines()

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

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

     _UpperCAmelCase										=					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.

