






import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Tuple=[10, 20, 30, 40]       ,				lowerCamelCase		:			str=[2, 2, 3, 2]       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Union[str, Any]=["stage2", "stage3", "stage4"]       ,				lowerCamelCase		:			Any=[2, 3, 4]       ,				lowerCamelCase		:			Tuple=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    num_stages
         _UpperCAmelCase							=    hidden_sizes
         _UpperCAmelCase							=    depths
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    out_features
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    scope


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


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_labels							)

         _UpperCAmelCase							=    self.get_config()
         return config, pixel_values, labels


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


         """simple docstring"""
         return ConvNextConfig(
             num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]							)

         # verify backbone works with out_features=None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps							)       ,				1							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				1							)
         self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]							)





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


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


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


         """simple docstring"""
         return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""							)
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    inspect.signature(model.forward							)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase							=    [*signature.parameters.keys()]

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _UpperCAmelCase							=    self.model_tester.num_stages
                self.assertEqual(len(lowerCamelCase							)       ,				expected_num_stages + 1							)

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase							=    True

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ConvNextModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None





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


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0260, -0.4739, 0.1911]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ConvNextBackbone,) if is_torch_available() else ()
  _lowerCamelCase    =  ConvNextConfig

  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  LongformerTokenizer
  _lowerCamelCase    =  True
  _lowerCamelCase    =  LongformerTokenizerFast
  _lowerCamelCase    =  True


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


         """simple docstring"""
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _UpperCAmelCase							=    [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         _UpperCAmelCase							=    {"""unk_token""": """<unk>"""}

         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    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(lowerCamelCase							) + """\n"""							)
         with open(self.merges_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write("""\n""".join(lowerCamelCase							)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				**lowerCamelCase		:			str							)						->	Tuple:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int       ,				**lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    """lower newer"""
         return input_text, output_text


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.tokenizer_class(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map							)
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         _UpperCAmelCase							=    tokenizer.tokenize(lowerCamelCase							)  # , add_prefix_space=True)
         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("""Hello world!"""       ,				add_special_tokens=lowerCamelCase							)       ,				[0, 3_1414, 232, 328, 2]							)
         self.assertListEqual(
             tokenizer.encode("""Hello world! cécé herlolip 418"""       ,				add_special_tokens=lowerCamelCase							)       ,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]       ,				)


  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""							)

         _UpperCAmelCase							=    tokenizer.encode("""sequence builders"""       ,				add_special_tokens=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode("""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				"""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase       ,				lowerCamelCase							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()

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

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

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

         tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
         self.assertNotEqual(lowerCamelCase       ,				lowerCamelCase							)

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

         _UpperCAmelCase							=    """Encode <mask> sequence"""
         _UpperCAmelCase							=    """Encode <mask>sequence"""

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

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


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


         """simple docstring"""
         pass


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


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

                       # 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"""]							)       ,				)

                       _UpperCAmelCase							=    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                       _UpperCAmelCase							=    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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]       ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)

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


  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         for trim_offsets, add_prefix_space in itertools.product([True, False]       ,				repeat=2							):
                _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                    self.tmpdirname       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)

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

                self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]       ,				lowerCamelCase							)

                self.assertEqual(post_processor_state["""add_prefix_space"""]       ,				lowerCamelCase							)
                self.assertEqual(post_processor_state["""trim_offsets"""]       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                       _UpperCAmelCase							=    """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _UpperCAmelCase							=    f"""{text_of_1_token} {text_of_1_token}"""

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

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

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

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

                       _UpperCAmelCase							=    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)),
                       # )

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

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

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