


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						CanineTokenizer
 __lowercase				:		Optional[Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()
  __A  :		List[Any]       =			CanineTokenizer()
  tokenizer.save_pretrained(self.tmpdirname						)


 @cached_property
 def  snake_case__   (      self						):

  """simple docstring"""







  return CanineTokenizer.from_pretrained('google/canine-s'						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.tokenizer_class.from_pretrained(self.tmpdirname      ,      **__lowercase						)
  __A  :		Dict       =			1_024
  return tokenizer


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.canine_tokenizer
  __A  :		List[str]       =			['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
  # fmt: off
  __A  :		Dict       =			[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
  # fmt: on
  __A  :		List[str]       =			tokenizer(__lowercase      ,      padding=__lowercase      ,      return_tensors='pt'						)
  self.assertIsInstance(__lowercase      ,      __lowercase						)

  __A  :		List[str]       =			list(batch.input_ids.numpy()[0]						)

  self.assertListEqual(__lowercase      ,      __lowercase						)

  self.assertEqual((2, 39)      ,      batch.input_ids.shape						)
  self.assertEqual((2, 39)      ,      batch.attention_mask.shape						)


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.canine_tokenizer
  __A  :		List[str]       =			['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
  __A  :		List[str]       =			tokenizer(__lowercase      ,      padding=__lowercase      ,      return_tensors='pt'						)
  # check if input_ids, attention_mask and token_type_ids are returned
  self.assertIn('input_ids'      ,      __lowercase						)
  self.assertIn('attention_mask'      ,      __lowercase						)
  self.assertIn('token_type_ids'      ,      __lowercase						)


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.canine_tokenizer
  __A  :		Tuple       =			[
      'What\'s the weater?',
      'It\'s about 25 degrees.',
  ]
  __A  :		str       =			tokenizer(
      text_target=__lowercase      ,      max_length=32      ,      padding='max_length'      ,      truncation=__lowercase      ,      return_tensors='pt'						)
  self.assertEqual(32      ,      targets['input_ids'].shape[1]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.get_tokenizers()
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    self.assertNotEqual(tokenizer.model_max_length      ,      42						)

        # Now let's start the test
  __A  :		Optional[int]       =			self.get_tokenizers()
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    # Isolate this from the other tests because we save additional tokens/etc
    __A  :		Dict       =			tempfile.mkdtemp()

    __A  :		Optional[int]       =			' He is very happy, UNwant\u00E9d,running'
    __A  :		List[Any]       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    tokenizer.save_pretrained(__lowercase						)

    __A  :		Tuple       =			tokenizer.__class__.from_pretrained(__lowercase						)
    __A  :		List[Any]       =			after_tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    self.assertListEqual(__lowercase      ,      __lowercase						)

    shutil.rmtree(__lowercase						)

  __A  :		Dict       =			self.get_tokenizers(model_max_length=42						)
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    # Isolate this from the other tests because we save additional tokens/etc
    __A  :		Optional[int]       =			tempfile.mkdtemp()

    __A  :		List[Any]       =			' He is very happy, UNwant\u00E9d,running'

    __A  :		int       =			tokenizer.additional_special_tokens

    # We can add a new special token for Canine as follows:
    __A  :		Tuple       =			chr(0xe007						)
    additional_special_tokens.append(__lowercase						)
    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}						)
    __A  :		List[str]       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    tokenizer.save_pretrained(__lowercase						)

    __A  :		List[str]       =			tokenizer.__class__.from_pretrained(__lowercase						)
    __A  :		Any       =			after_tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    self.assertListEqual(__lowercase      ,      __lowercase						)
    self.assertIn(__lowercase      ,      after_tokenizer.additional_special_tokens						)
    self.assertEqual(after_tokenizer.model_max_length      ,      42						)

    __A  :		List[str]       =			tokenizer.__class__.from_pretrained(__lowercase      ,      model_max_length=43						)
    self.assertEqual(tokenizer.model_max_length      ,      43						)

    shutil.rmtree(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.get_tokenizers(do_lower_case=__lowercase						)
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    __A     ,__A  :		Union[str, Any]       =			self.get_clean_sequence(__lowercase						)

    # a special token for Canine can be defined as follows:
    __A  :		int       =			0xe005
    __A  :		Dict       =			chr(__lowercase						)

    tokenizer.add_special_tokens({'cls_token': special_token}						)
    __A  :		Tuple       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    self.assertEqual(len(__lowercase						)      ,      1						)

    __A  :		Union[str, Any]       =			tokenizer.decode(ids + encoded_special_token      ,      clean_up_tokenization_spaces=__lowercase						)
    __A  :		List[str]       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)

    __A  :		Optional[Any]       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    __A  :		Optional[Any]       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    self.assertEqual(__lowercase      ,      input_encoded + special_token_id						)

    __A  :		List[str]       =			tokenizer.decode(__lowercase      ,      skip_special_tokens=__lowercase						)
    self.assertTrue(special_token not in decoded						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			self.get_tokenizers(do_lower_case=__lowercase						)
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    __A  :		List[str]       =			chr(0xe005						)
    __A  :		str       =			chr(0xe006						)

    # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
    tokenizer.add_tokens([SPECIAL_TOKEN_1]      ,      special_tokens=__lowercase						)
    # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
    # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
    tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}						)

    __A  :		int       =			tokenizer.tokenize(__lowercase						)
    __A  :		int       =			tokenizer.tokenize(__lowercase						)

    self.assertEqual(len(__lowercase						)      ,      1						)
    self.assertEqual(len(__lowercase						)      ,      1						)
    self.assertEqual(token_a[0]      ,      __lowercase						)
    self.assertEqual(token_a[0]      ,      __lowercase						)


 @require_tokenizers
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_tokenizers(do_lower_case=__lowercase						)
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    # a special token for Canine can be defined as follows:
    __A  :		Optional[Any]       =			0xe006
    __A  :		List[Any]       =			chr(__lowercase						)

    __A  :		List[Any]       =			AddedToken(__lowercase      ,      lstrip=__lowercase						)
    tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}						)

    with tempfile.TemporaryDirectory() as tmp_dir_name:
     tokenizer.save_pretrained(__lowercase						)
     tokenizer.from_pretrained(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			[]
  if self.test_slow_tokenizer:
   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

  if self.test_rust_tokenizer:
   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

  for tokenizer_class, tokenizer_utils in tokenizer_list:
   with tempfile.TemporaryDirectory() as tmp_dir:
    tokenizer_utils.save_pretrained(__lowercase						)

    with open(os.path.join(__lowercase      ,      'special_tokens_map.json'						)      ,      encoding='utf-8'						) as json_file:
     __A  :		Optional[int]       =			json.load(__lowercase						)

    with open(os.path.join(__lowercase      ,      'tokenizer_config.json'						)      ,      encoding='utf-8'						) as json_file:
     __A  :		Tuple       =			json.load(__lowercase						)

    # a special token for Canine can be defined as follows:
    __A  :		Any       =			0xe006
    __A  :		str       =			chr(__lowercase						)

    __A  :		Union[str, Any]       =			[new_token_a]
    __A  :		Any       =			[new_token_a]

    with open(os.path.join(__lowercase      ,      'special_tokens_map.json'						)      ,      'w'      ,      encoding='utf-8'						) as outfile:
     json.dump(__lowercase      ,      __lowercase						)
    with open(os.path.join(__lowercase      ,      'tokenizer_config.json'						)      ,      'w'      ,      encoding='utf-8'						) as outfile:
     json.dump(__lowercase      ,      __lowercase						)

    # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
    # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
    # "special_tokens_map.json" files
    __A  :		Optional[int]       =			tokenizer_class.from_pretrained(__lowercase      ,      extra_ids=0						)
    self.assertIn(__lowercase      ,      tokenizer_without_change_in_init.additional_special_tokens						)
    # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
    self.assertEqual(
        [new_token_a]      ,      tokenizer_without_change_in_init.convert_ids_to_tokens(
            tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]						)						)      ,      )

    __A  :		Any       =			0xe007
    __A  :		Optional[Any]       =			chr(__lowercase						)
    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
    __A  :		Optional[Any]       =			[AddedToken(__lowercase      ,      lstrip=__lowercase						)]
    __A  :		List[str]       =			tokenizer_class.from_pretrained(
        __lowercase      ,      additional_special_tokens=__lowercase      ,      extra_ids=0						)

    self.assertIn(__lowercase      ,      tokenizer.additional_special_tokens						)
    # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
    self.assertEqual(
        [new_token_a]      ,      tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]						)						)						)


 @require_tokenizers
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.get_tokenizers(do_lower_case=__lowercase						)
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    __A  :		List[Any]       =			'hello world'
    if self.space_between_special_tokens:
     __A  :		Optional[Any]       =			'[CLS] hello world [SEP]'
    else:
     __A  :		int       =			input
    __A  :		Dict       =			tokenizer.encode(__lowercase      ,      add_special_tokens=__lowercase						)
    __A  :		str       =			tokenizer.decode(__lowercase      ,      spaces_between_special_tokens=self.space_between_special_tokens						)
    self.assertIn(__lowercase      ,      [output, output.lower()]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.get_tokenizers()
  for tokenizer in tokenizers:
   with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
    __A  :		List[str]       =			[
        'bos_token',
        'eos_token',
        'unk_token',
        'sep_token',
        'pad_token',
        'cls_token',
        'mask_token',
    ]

  __A  :		Any       =			'a'
  __A  :		Any       =			ord(__lowercase						)

  for attr in attributes_list:
   setattr(__lowercase      ,      attr + '_id'      ,      __lowercase						)
   self.assertEqual(getattr(__lowercase      ,      __lowercase						)      ,      __lowercase						)
   self.assertEqual(getattr(__lowercase      ,      attr + '_id'						)      ,      __lowercase						)

   setattr(__lowercase      ,      attr + '_id'      ,      __lowercase						)
   self.assertEqual(getattr(__lowercase      ,      __lowercase						)      ,      __lowercase						)
   self.assertEqual(getattr(__lowercase      ,      attr + '_id'						)      ,      __lowercase						)

  setattr(__lowercase      ,      'additional_special_tokens_ids'      ,      []						)
  self.assertListEqual(getattr(__lowercase      ,      'additional_special_tokens'						)      ,      []						)
  self.assertListEqual(getattr(__lowercase      ,      'additional_special_tokens_ids'						)      ,      []						)

  __A  :		Union[str, Any]       =			0xe006
  __A  :		Optional[int]       =			chr(__lowercase						)
  setattr(__lowercase      ,      'additional_special_tokens_ids'      ,      [additional_special_token_id]						)
  self.assertListEqual(getattr(__lowercase      ,      'additional_special_tokens'						)      ,      [additional_special_token]						)
  self.assertListEqual(getattr(__lowercase      ,      'additional_special_tokens_ids'						)      ,      [additional_special_token_id]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass







 def  snake_case__   (      self						):

  """simple docstring"""







  pass










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






