



'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			RobertaTokenizer
 __lowerCamelCase       :			List[Any]		=			RobertaTokenizerFast
 __lowerCamelCase       :			Union[str, Any]		=			True
 __lowerCamelCase       :			Dict		=			{'cls_token': '<s>'}



 def    a__    (self			)       ->	int:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	Dict:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return self.tokenizer_class.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  **A			)       ->	str:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    '''lower newer'''
      _a           =    '''lower newer'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''lower newer'''
      _a           =    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _a           =    tokenizer.tokenize(A			)  # , add_prefix_space=True)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]
      _a           =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('''Hello world!'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 2]			)
      self.assertListEqual(
          tokenizer.encode('''Hello world! cécé herlolip 418'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,  )



 @slow
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class.from_pretrained('''roberta-base'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.encode(
          '''sequence builders'''					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.encode(
          '''sequence builders'''					,  '''multi-sequence build'''					,  add_special_tokens=A					,  add_prefix_space=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      _a           =    '''Encode this sequence.'''
      _a           =    tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

      # Testing encoder arguments
      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertNotEqual(A					,  A			)

      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertEqual(A					,  A			)

      tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
      _a           =    tokenizer.encode(A					,  add_special_tokens=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
      self.assertNotEqual(A					,  A			)

      # Testing spaces after special tokens
      _a           =    '''<mask>'''
      tokenizer.add_special_tokens(
          {'''mask_token''': AddedToken(A					,  lstrip=A					,  rstrip=A			)}			)  # mask token has a left space
      _a           =    tokenizer.convert_tokens_to_ids(A			)

      _a           =    '''Encode <mask> sequence'''
      _a           =    '''Encode <mask>sequence'''

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertEqual(A					,  A			)

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertNotEqual(A					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      pass



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    self.rust_tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    self.tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    '''A, <mask> AllenNLP sentence.'''
                _a           =    tokenizer_r.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)
                _a           =    tokenizer_p.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']			)					,  sum(tokens_p['''token_type_ids''']			)			)

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)					,  sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)					,  )

                _a           =    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
                _a           =    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
                self.assertSequenceEqual(tokens_r['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2			):
           _a           =    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)

           _a           =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
           _a           =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,  A			)

           self.assertEqual(post_processor_state['''add_prefix_space''']					,  A			)
           self.assertEqual(post_processor_state['''trim_offsets''']					,  A			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                _a           =    f'''{text_of_1_token} {text_of_1_token}'''

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    f''' {text}'''

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			) + 1, 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )








'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



