


'''simple docstring'''
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_ :     Tuple								=      '▁'

UpperCAmelCase_ :     Union[str, Any]								=      get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
class 						_lowerCamelCase					(    UpperCamelCase__    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]   							=						BertGenerationTokenizer
 __lowercase				:		Any   							=						False
 __lowercase				:		Optional[int]   							=						True


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  __A  :		int       =			BertGenerationTokenizer(_a      ,      keep_accents=_a						)
  tokenizer.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			"""<s>"""
  __A  :		Any       =			1

  self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a						)      ,      _a						)
  self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a						)      ,      _a						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			list(self.get_tokenizer().get_vocab().keys()						)

  self.assertEqual(vocab_keys[0]      ,      '<unk>'						)
  self.assertEqual(vocab_keys[1]      ,      '<s>'						)
  self.assertEqual(vocab_keys[-1]      ,      '<pad>'						)
  self.assertEqual(len(_a						)      ,      1_002						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.assertEqual(self.get_tokenizer().vocab_size      ,      1_000						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			BertGenerationTokenizer(_a      ,      keep_accents=_a						)

  __A  :		List[Any]       =			tokenizer.tokenize('This is a test'						)
  self.assertListEqual(_a      ,      ['▁This', '▁is', '▁a', '▁t', 'est']						)

  self.assertListEqual(
      tokenizer.convert_tokens_to_ids(_a						)      ,      [285, 46, 10, 170, 382]      ,      )

  __A  :		Tuple       =			tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
  self.assertListEqual(
      _a      ,      [
          SPIECE_UNDERLINE + 'I',
          SPIECE_UNDERLINE + 'was',
          SPIECE_UNDERLINE + 'b',
          'or',
          'n',
          SPIECE_UNDERLINE + 'in',
          SPIECE_UNDERLINE + '',
          '9',
          '2',
          '0',
          '0',
          '0',
          ',',
          SPIECE_UNDERLINE + 'and',
          SPIECE_UNDERLINE + 'this',
          SPIECE_UNDERLINE + 'is',
          SPIECE_UNDERLINE + 'f',
          'al',
          's',
          'é',
          '.',
      ]      ,      )
  __A  :		str       =			tokenizer.convert_tokens_to_ids(_a						)
  self.assertListEqual(
      _a      ,      [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,      )

  __A  :		Dict       =			tokenizer.convert_ids_to_tokens(_a						)
  self.assertListEqual(
      _a      ,      [
          SPIECE_UNDERLINE + 'I',
          SPIECE_UNDERLINE + 'was',
          SPIECE_UNDERLINE + 'b',
          'or',
          'n',
          SPIECE_UNDERLINE + 'in',
          SPIECE_UNDERLINE + '',
          '<unk>',
          '2',
          '0',
          '0',
          '0',
          ',',
          SPIECE_UNDERLINE + 'and',
          SPIECE_UNDERLINE + 'this',
          SPIECE_UNDERLINE + 'is',
          SPIECE_UNDERLINE + 'f',
          'al',
          's',
          '<unk>',
          '.',
      ]      ,      )


 @cached_property
 def  snake_case__   (      self						):

  """simple docstring"""







  return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'						)


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			"""Hello World!"""
  __A  :		Optional[Any]       =			[18_536, 2_260, 101]

  self.assertListEqual(_a      ,      self.big_tokenizer.encode(_a						)						)


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			(
      """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
      """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
  )
  __A  :		int       =			[
      871,
      419,
      358,
      946,
      991,
      2_521,
      452,
      358,
      1_357,
      387,
      7_751,
      3_536,
      112,
      985,
      456,
      126,
      865,
      938,
      5_400,
      5_734,
      458,
      1_368,
      467,
      786,
      2_462,
      5_246,
      1_159,
      633,
      865,
      4_519,
      457,
      582,
      852,
      2_557,
      427,
      916,
      508,
      405,
      34_324,
      497,
      391,
      408,
      11_342,
      1_244,
      385,
      100,
      938,
      985,
      456,
      574,
      362,
      12_597,
      3_200,
      3_129,
      1_172,
  ]

  self.assertListEqual(_a      ,      self.big_tokenizer.encode(_a						)						)


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  import torch

  from transformers import BertGenerationConfig, BertGenerationEncoder

  # Build sequence
  __A  :		str       =			list(self.big_tokenizer.get_vocab().keys()						)[:10]
  __A  :		Tuple       =			""" """.join(_a						)
  __A  :		Optional[int]       =			self.big_tokenizer.encode_plus(_a      ,      return_tensors='pt'      ,      return_token_type_ids=_a						)
  __A  :		Dict       =			self.big_tokenizer.batch_encode_plus(
      [sequence + ' ' + sequence]      ,      return_tensors='pt'      ,      return_token_type_ids=_a						)

  __A  :		List[str]       =			BertGenerationConfig()
  __A  :		Optional[int]       =			BertGenerationEncoder(_a						)

  assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

  with torch.no_grad():
   model(**_a						)
   model(**_a						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{"""input_ids""": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
  # fmt: on

  self.tokenizer_integration_test_util(
      expected_encoding=_a      ,      model_name='google/bert_for_seq_generation_L-24_bbc_encoder'      ,      revision='c817d1fd1be2ffa69431227a1fe320544943d4db'      ,      )










'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase_ :     int								=      ''
UpperCAmelCase_ :     Union[str, Any]								=      ''
UpperCAmelCase_ :     Any								=      ''
UpperCAmelCase_ :     int								=      1  # (0 is vertical, 1 is horizontal)


def 			_lowercase       (				):
 __A     ,__A  :		Optional[int]       =			get_dataset(UpperCamelCase__,				UpperCamelCase__			)
 print('Processing...'			)
 __A     ,__A     ,__A  :		Any       =			update_image_and_anno(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 for index, image in enumerate(UpperCamelCase__			):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __A  :		Dict       =			random_chars(32			)
  __A  :		List[Any]       =			paths[index].split(os.sep			)[-1].rsplit('.',				1			)[0]
  __A  :		Dict       =			f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
  cva.imwrite(f"""/{file_root}.jpg""",				UpperCamelCase__,				[cva.IMWRITE_JPEG_QUALITY, 85]			)
  print(f"""Success {index+1}/{len(UpperCamelCase__			)} with {file_name}"""			)
  __A  :		Tuple       =			[]
  for anno in new_annos[index]:
   __A  :		Any       =			f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
   annos_list.append(UpperCamelCase__			)
  with open(f"""/{file_root}.txt""",				'w'			) as outfile:
   outfile.write('\n'.join(line for line in annos_list			)			)


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str			):
 __A  :		Tuple       =			[]
 __A  :		int       =			[]
 for label_file in glob.glob(os.path.join(UpperCamelCase__,				'*.txt'			)			):
  __A  :		Optional[int]       =			label_file.split(os.sep			)[-1].rsplit('.',				1			)[0]
  with open(UpperCamelCase__			) as in_file:
   __A  :		Optional[Any]       =			in_file.readlines()
  __A  :		int       =			os.path.join(UpperCamelCase__,				f"""{label_name}.jpg"""			)

  __A  :		Optional[int]       =			[]
  for obj_list in obj_lists:
   __A  :		str       =			obj_list.rstrip('\n'			).split(' '			)
   boxes.append(
       [
           int(obj[0]			),
           float(obj[1]			),
           float(obj[2]			),
           float(obj[3]			),
           float(obj[4]			),
       ]			)
  if not boxes:
   continue
  img_paths.append(UpperCamelCase__			)
  labels.append(UpperCamelCase__			)
 return img_paths, labels


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					list,				UpperCamelCase__       :					int = 1			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			[]
 for idx in range(len(UpperCamelCase__			)			):
  __A  :		List[Any]       =			[]
  __A  :		List[str]       =			img_list[idx]
  path_list.append(UpperCamelCase__			)
  __A  :		Optional[Any]       =			anno_list[idx]
  __A  :		Union[str, Any]       =			cva.imread(UpperCamelCase__			)
  if flip_type == 1:
   __A  :		int       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Union[str, Any]       =			1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
  elif flip_type == 0:
   __A  :		Tuple       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Dict       =			1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
  new_annos_lists.append(UpperCamelCase__			)
  new_imgs_list.append(UpperCamelCase__			)
 return new_imgs_list, new_annos_lists, path_list


def 			_lowercase       (				UpperCamelCase__       :					int = 32			):
 assert number_char > 1, "The number of character should greater than 1"
 __A  :		int       =			ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase__			) for _ in range(UpperCamelCase__			)			)


if __name__ == "__main__":
 main()
 print('DONE ✅')






