

"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_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 torch import nn

    from transformers import MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase=13							,       _lowerCamelCase=32							,       _lowerCamelCase=2							,       _lowerCamelCase=3							,       _lowerCamelCase=16							,       _lowerCamelCase=[1, 2, 1]							,       _lowerCamelCase=[2, 2, 4]							,       _lowerCamelCase=2							,       _lowerCamelCase=2.0							,       _lowerCamelCase=True							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.1							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=False							,       _lowerCamelCase=True							,       _lowerCamelCase=0.02							,       _lowerCamelCase=1e-5							,       _lowerCamelCase=True							,       _lowerCamelCase=None							,       _lowerCamelCase=True							,       _lowerCamelCase=10							,       _lowerCamelCase=8							,       _lowerCamelCase=["stage1", "stage2", "stage3"]							,       _lowerCamelCase=[1, 2, 3]							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    parent
        UpperCAmelCase__			:							Union[str, Any]							=    batch_size
        UpperCAmelCase__			:							Any							=    image_size
        UpperCAmelCase__			:							Optional[Any]							=    patch_size
        UpperCAmelCase__			:							int							=    num_channels
        UpperCAmelCase__			:							str							=    embed_dim
        UpperCAmelCase__			:							Any							=    depths
        UpperCAmelCase__			:							Optional[Any]							=    num_heads
        UpperCAmelCase__			:							List[Any]							=    window_size
        UpperCAmelCase__			:							Any							=    mlp_ratio
        UpperCAmelCase__			:							Union[str, Any]							=    qkv_bias
        UpperCAmelCase__			:							Tuple							=    hidden_dropout_prob
        UpperCAmelCase__			:							Any							=    attention_probs_dropout_prob
        UpperCAmelCase__			:							Optional[int]							=    drop_path_rate
        UpperCAmelCase__			:							List[str]							=    hidden_act
        UpperCAmelCase__			:							Any							=    use_absolute_embeddings
        UpperCAmelCase__			:							List[Any]							=    patch_norm
        UpperCAmelCase__			:							Optional[int]							=    layer_norm_eps
        UpperCAmelCase__			:							int							=    initializer_range
        UpperCAmelCase__			:							str							=    is_training
        UpperCAmelCase__			:							Optional[int]							=    scope
        UpperCAmelCase__			:							Dict							=    use_labels
        UpperCAmelCase__			:							Optional[int]							=    type_sequence_label_size
        UpperCAmelCase__			:							List[str]							=    encoder_stride
        UpperCAmelCase__			:							Optional[Any]							=    out_features
        UpperCAmelCase__			:							Any							=    out_indices



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        UpperCAmelCase__			:							int							=    None
        if self.use_labels:
              UpperCAmelCase__			:							Dict							=    ids_tensor([self.batch_size]							,       self.type_sequence_label_size	)

        UpperCAmelCase__			:							Any							=    self.get_config()

        return config, pixel_values, labels



  def  _a      (self	):


        """simple docstring"""

        return MaskFormerSwinConfig(
            image_size=self.image_size							,       patch_size=self.patch_size							,       num_channels=self.num_channels							,       embed_dim=self.embed_dim							,       depths=self.depths							,       num_heads=self.num_heads							,       window_size=self.window_size							,       mlp_ratio=self.mlp_ratio							,       qkv_bias=self.qkv_bias							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       drop_path_rate=self.drop_path_rate							,       hidden_act=self.hidden_act							,       use_absolute_embeddings=self.use_absolute_embeddings							,       path_norm=self.patch_norm							,       layer_norm_eps=self.layer_norm_eps							,       initializer_range=self.initializer_range							,       encoder_stride=self.encoder_stride							,       out_features=self.out_features							,       out_indices=self.out_indices							,       )



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    MaskFormerSwinModel(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							Tuple							=    model(_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
        UpperCAmelCase__			:							List[Any]							=    int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

        self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, expected_seq_len, expected_dim)	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    MaskFormerSwinBackbone(config=_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()
        UpperCAmelCase__			:							str							=    model(_lowerCamelCase	)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps	)							,       len(config.out_features	)	)
        self.parent.assertListEqual(list(result.feature_maps[0].shape	)							,       [13, 16, 16, 16]	)

        # verify channels
        self.parent.assertEqual(len(model.channels	)							,       len(config.out_features	)	)
        self.parent.assertListEqual(model.channels							,       [16, 32, 64]	)

        # verify ValueError
        with self.parent.assertRaises(_lowerCamelCase	):
              UpperCAmelCase__			:							Any							=    ["""stem"""]
              UpperCAmelCase__			:							int							=    MaskFormerSwinBackbone(config=_lowerCamelCase	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.prepare_config_and_inputs()
        UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    config_and_inputs
        UpperCAmelCase__			:							Any							=    {"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class       lowerCamelCase	(       lowerCAmelCase__					,					lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  (
      (
          MaskFormerSwinModel,
          MaskFormerSwinBackbone,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					      =  {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False
  SCREAMING_SNAKE_CASE					      =  False



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    MaskFormerSwinModelTester(self	)
        UpperCAmelCase__			:							Union[str, Any]							=    ConfigTester(self							,       config_class=_lowerCamelCase							,       embed_dim=37	)



  @require_torch_multi_gpu
  @unittest.skip(
      reason=(
          """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
          """ `nn.DataParallel`"""
      )	)
  def  _a      (self	):


        """simple docstring"""

        pass



  def  _a      (self	):


        """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  _a      (self	):


        """simple docstring"""

        return



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*_lowerCamelCase	)



  @unittest.skip("""Swin does not use inputs_embeds"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  @unittest.skip("""Swin does not support feedforward chunking"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							List[Any]							=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase__			:							int							=    model_class(_lowerCamelCase	)
              self.assertIsInstance(model.get_input_embeddings()							,       (nn.Module)	)
              UpperCAmelCase__			:							Any							=    model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_lowerCamelCase							,       nn.Linear	)	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase__			:							List[Any]							=    model_class(_lowerCamelCase	)
              UpperCAmelCase__			:							Optional[Any]							=    inspect.signature(model.forward	)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase__			:							Optional[int]							=    [*signature.parameters.keys()]

              UpperCAmelCase__			:							str							=    ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]							,       _lowerCamelCase	)



  @unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  @unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    model_class(_lowerCamelCase	)
        model.to(_lowerCamelCase	)
        model.eval()

        with torch.no_grad():
              UpperCAmelCase__			:							Any							=    model(**self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase	)	)

        UpperCAmelCase__			:							Any							=    outputs.hidden_states

        UpperCAmelCase__			:							List[Any]							=    getattr(
            self.model_tester							,       """expected_num_hidden_layers"""							,       len(self.model_tester.depths	) + 1	)
        self.assertEqual(len(_lowerCamelCase	)							,       _lowerCamelCase	)

        # Swin has a different seq_length
        UpperCAmelCase__			:							Optional[Any]							=    (
            config.patch_size
            if isinstance(config.patch_size							,       collections.abc.Iterable	)
            else (config.patch_size, config.patch_size)
        )

        UpperCAmelCase__			:							str							=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]	)							,       [num_patches, self.model_tester.embed_dim]							,       )



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase__			:							List[Any]							=    (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size							,       collections.abc.Iterable	)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
              UpperCAmelCase__			:							Dict							=    True
              self.check_hidden_states_output(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase__			:							List[str]							=    True

              self.check_hidden_states_output(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__			:							int							=    3

        UpperCAmelCase__			:							int							=    (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size							,       collections.abc.Iterable	)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        UpperCAmelCase__			:							Union[str, Any]							=    (
            config.patch_size
            if isinstance(config.patch_size							,       collections.abc.Iterable	)
            else (config.patch_size, config.patch_size)
        )

        UpperCAmelCase__			:							Optional[Any]							=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        UpperCAmelCase__			:							Any							=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
              UpperCAmelCase__			:							int							=    True
              self.check_hidden_states_output(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       (padded_height, padded_width)	)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase__			:							Tuple							=    True
              self.check_hidden_states_output(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       (padded_height, padded_width)	)



  @unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""	)
  def  _a      (self	):


        """simple docstring"""

        pass




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(_lowerCamelCase	):
              UpperCAmelCase__			:							List[Any]							=    0
              return t

        def check_equivalence(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase={}	):
              with torch.no_grad():
                    UpperCAmelCase__			:							Union[str, Any]							=    model(**_lowerCamelCase							,       return_dict=_lowerCamelCase							,       **_lowerCamelCase	)
                    UpperCAmelCase__			:							List[str]							=    model(**_lowerCamelCase							,       return_dict=_lowerCamelCase							,       **_lowerCamelCase	).to_tuple()

                    def recursive_check(_lowerCamelCase							,       _lowerCamelCase	):
                          if isinstance(_lowerCamelCase							,       (List, Tuple)	):
                                for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase							,       _lowerCamelCase	):
                                      recursive_check(_lowerCamelCase							,       _lowerCamelCase	)
                          elif isinstance(_lowerCamelCase							,       _lowerCamelCase	):
                                for tuple_iterable_value, dict_iterable_value in zip(
                                    tuple_object.values()							,       dict_object.values()	):
                                      recursive_check(_lowerCamelCase							,       _lowerCamelCase	)
                          elif tuple_object is None:
                                return
                          else:
                                self.assertTrue(
                                    torch.allclose(
                                        set_nan_tensor_to_zero(_lowerCamelCase	)							,       set_nan_tensor_to_zero(_lowerCamelCase	)							,       atol=1e-5	)							,       msg=(
                                        """Tuple and dict output are not equal. Difference:"""
                                        F""" {torch.max(torch.abs(tuple_object - dict_object	)	)}. Tuple has `nan`:"""
                                        F""" {torch.isnan(_lowerCamelCase	).any()} and `inf`: {torch.isinf(_lowerCamelCase	)}. Dict has"""
                                        F""" `nan`: {torch.isnan(_lowerCamelCase	).any()} and `inf`: {torch.isinf(_lowerCamelCase	)}."""
                                    )							,       )

                    recursive_check(_lowerCamelCase							,       _lowerCamelCase	)

        for model_class in self.all_model_classes:
              UpperCAmelCase__			:							Optional[int]							=    model_class(_lowerCamelCase	)
              model.to(_lowerCamelCase	)
              model.eval()

              UpperCAmelCase__			:							List[str]							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase	)
              check_equivalence(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

              UpperCAmelCase__			:							str							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase							,       return_labels=_lowerCamelCase	)
              UpperCAmelCase__			:							Any							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase							,       return_labels=_lowerCamelCase	)
              check_equivalence(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

              UpperCAmelCase__			:							str							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							Optional[Any]							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase	)
              check_equivalence(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       {"""output_hidden_states""": True}	)

              UpperCAmelCase__			:							Tuple							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase							,       return_labels=_lowerCamelCase	)
              UpperCAmelCase__			:							Any							=    self._prepare_for_class(_lowerCamelCase							,       _lowerCamelCase							,       return_labels=_lowerCamelCase	)
              check_equivalence(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       {"""output_hidden_states""": True}	)




@require_torch
class       lowerCamelCase	(       unittest.TestCase					,					lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  (MaskFormerSwinBackbone,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					      =  MaskFormerSwinConfig



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    MaskFormerSwinModelTester(self	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__			:							Union[str, Any]							=    inputs_dict["""pixel_values"""].shape[0]

        for backbone_class in self.all_model_classes:
              UpperCAmelCase__			:							Any							=    backbone_class(_lowerCamelCase	)
              backbone.to(_lowerCamelCase	)
              backbone.eval()

              UpperCAmelCase__			:							List[str]							=    backbone(**_lowerCamelCase	)

              # Test default outputs and verify feature maps
              self.assertIsInstance(outputs.feature_maps							,       _lowerCamelCase	)
              self.assertTrue(len(outputs.feature_maps	) == len(backbone.channels	)	)
              for feature_map, n_channels in zip(outputs.feature_maps							,       backbone.channels	):
                    self.assertTrue(feature_map.shape[:2]							,       (batch_size, n_channels)	)
              self.assertIsNone(outputs.hidden_states	)
              self.assertIsNone(outputs.attentions	)

              # Test output_hidden_states=True
              UpperCAmelCase__			:							int							=    backbone(**_lowerCamelCase							,       output_hidden_states=_lowerCamelCase	)
              self.assertIsNotNone(outputs.hidden_states	)
              self.assertTrue(len(outputs.hidden_states	)							,       len(backbone.stage_names	)	)
              # We skip the stem layer
              for hidden_states, n_channels in zip(outputs.hidden_states[1:]							,       backbone.channels	):
                    for hidden_state in hidden_states:
                          # Hidden states are in the format (batch_size, (height * width), n_channels)
                          UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    hidden_state.shape
                          self.assertTrue((h_batch_size, h_n_channels)							,       (batch_size, n_channels)	)

            # Test output_attentions=True
              if self.has_attentions:
                    UpperCAmelCase__			:							Any							=    backbone(**_lowerCamelCase							,       output_attentions=_lowerCamelCase	)
                    self.assertIsNotNone(outputs.attentions	)




"""simple docstring"""
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A							=  """▁"""

_A							=  get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizer
  SCREAMING_SNAKE_CASE					      =  BigBirdTokenizerFast
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  True



  def  _a      (self	):


        """simple docstring"""

        super().setUp()

        UpperCAmelCase__			:							Any							=    self.tokenizer_class(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """<s>"""
        UpperCAmelCase__			:							Optional[int]							=    1

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    list(self.get_tokenizer().get_vocab().keys()	)

        self.assertEqual(vocab_keys[0]							,       """<unk>"""	)
        self.assertEqual(vocab_keys[1]							,       """<s>"""	)
        self.assertEqual(vocab_keys[-1]							,       """[MASK]"""	)
        self.assertEqual(len(_lowerCamelCase	)							,       1004	)



  def  _a      (self	):


        """simple docstring"""

        self.assertEqual(self.get_tokenizer().vocab_size							,       1000	)



  def  _a      (self	):


        """simple docstring"""

        if not self.test_rust_tokenizer:
              return

        UpperCAmelCase__			:							List[Any]							=    self.get_tokenizer()
        UpperCAmelCase__			:							Optional[Any]							=    self.get_rust_tokenizer()

        UpperCAmelCase__			:							Dict							=    """I was born in 92000, and this is falsé."""

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    rust_tokenizer.tokenize(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							int							=    tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    rust_tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    self.get_rust_tokenizer()
        UpperCAmelCase__			:							List[Any]							=    tokenizer.encode(_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    rust_tokenizer.encode(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    BigBirdTokenizer(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    tokenizer.tokenize("""This is a test"""	)
        self.assertListEqual(_lowerCamelCase							,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

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

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                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""",
                """.""",
            ]							,       )
        UpperCAmelCase__			:							Tuple							=    tokenizer.convert_tokens_to_ids(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,       )

        UpperCAmelCase__			:							Tuple							=    tokenizer.convert_ids_to_tokens(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                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  _a      (self	):


        """simple docstring"""

        return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    """Hello World!"""
        UpperCAmelCase__			:							Union[str, Any]							=    [65, 18536, 2260, 101, 66]

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



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, 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"""
        )
        # fmt: off
        UpperCAmelCase__			:							Dict							=    [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
        # fmt: on
        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @require_torch
  @slow
  def  _a      (self	):


        """simple docstring"""

        import torch

        from transformers import BigBirdConfig, BigBirdModel

        # Build sequence
        UpperCAmelCase__			:							int							=    list(self.big_tokenizer.get_vocab().keys()	)[:10]
        UpperCAmelCase__			:							str							=    """ """.join(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    self.big_tokenizer.encode_plus(_lowerCamelCase							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)
        UpperCAmelCase__			:							str							=    self.big_tokenizer.batch_encode_plus(
            [sequence + """ """ + sequence]							,       return_tensors="""pt"""							,       return_token_type_ids=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    BigBirdConfig(attention_type="""original_full"""	)
        UpperCAmelCase__			:							Optional[int]							=    BigBirdModel(_lowerCamelCase	)

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

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



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""	)
        UpperCAmelCase__			:							int							=    tokenizer.decode(tokenizer("""Paris is the [MASK]."""	).input_ids	)

        self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    {"""input_ids""": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 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], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 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, 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, 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=_lowerCamelCase							,       model_name="""google/bigbird-roberta-base"""							,       revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""							,       )

