'''simple docstring'''





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

    if is_vision_available():
        from transformers import MaskFormerImageProcessor

if is_vision_available():
    from PIL import Image




class 			__A :



    def __init__(self       :       Tuple  ,     __a       :       Dict  ,     __a       :       Union[str, Any]=2  ,     __a       :       List[Any]=True  ,     __a       :       str=False  ,     __a       :       List[Any]=10  ,     __a       :       Optional[int]=3  ,     __a       :       Any=32 * 4  ,     __a       :       Any=32 * 6  ,     __a       :       Optional[int]=4  ,     __a       :       Optional[Any]=32  ,     ):
         UpperCAmelCase_			       =  parent
         UpperCAmelCase_			       =  batch_size
         UpperCAmelCase_			       =  is_training
         UpperCAmelCase_			       =  use_auxiliary_loss
         UpperCAmelCase_			       =  num_queries
         UpperCAmelCase_			       =  num_channels
         UpperCAmelCase_			       =  min_size
         UpperCAmelCase_			       =  max_size
         UpperCAmelCase_			       =  num_labels
         UpperCAmelCase_			       =  mask_feature_size



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
             __a					)

         UpperCAmelCase_			       =  torch.ones([self.batch_size, self.min_size, self.max_size]  ,     device=__a					)

         UpperCAmelCase_			       =  (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,     device=__a					) > 0.5
         ).float()
         UpperCAmelCase_			       =  (torch.rand((self.batch_size, self.num_labels)  ,     device=__a					) > 0.5).long()

         UpperCAmelCase_			       =  self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels



    def    _lowercase		(self       :       Union[str, Any]					):
         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]  ,     )  ,     decoder_config=DetrConfig(
                 decoder_ffn_dim=128  ,     num_queries=self.num_queries  ,     decoder_attention_heads=2  ,     d_model=self.mask_feature_size  ,     )  ,     mask_feature_size=self.mask_feature_size  ,     fpn_feature_size=self.mask_feature_size  ,     num_channels=self.num_channels  ,     num_labels=self.num_labels  ,     )



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.prepare_config_and_inputs()
         UpperCAmelCase_			       =  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
         return config, inputs_dict



    def    _lowercase		(self       :       int  ,     __a       :       int  ,     __a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  output.encoder_hidden_states
         UpperCAmelCase_			       =  output.pixel_decoder_hidden_states
         UpperCAmelCase_			       =  output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__a					)  ,     len(config.backbone_config.depths					)					)
         self.parent.assertTrue(len(__a					)  ,     len(config.backbone_config.depths					)					)
         self.parent.assertTrue(len(__a					)  ,     config.decoder_config.decoder_layers					)



    def    _lowercase		(self       :       Tuple  ,     __a       :       Union[str, Any]  ,     __a       :       Any  ,     __a       :       Union[str, Any]  ,     __a       :       Any=False					):
         with torch.no_grad():
              UpperCAmelCase_			       =  MaskFormerModel(config=__a					)
              model.to(__a					)
              model.eval()

              UpperCAmelCase_			       =  model(pixel_values=__a  ,     pixel_mask=__a					)
              UpperCAmelCase_			       =  model(__a  ,     output_hidden_states=__a					)
         # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
         # encoder and pixel decoder
         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape  ,     (self.batch_size, self.num_queries, self.mask_feature_size)  ,     )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
         self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

         if output_hidden_states:
              self.check_output_hidden_state(__a  ,     __a					)






    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       int  ,     __a       :       List[str]  ,     __a       :       Dict  ,     __a       :       Any  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(config=__a					)
         model.to(__a					)
         model.eval()

         def comm_check_on_output(__a       :       Any					):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
              self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,     )
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.num_labels + 1)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(pixel_values=__a  ,     pixel_mask=__a					)
              UpperCAmelCase_			       =  model(__a					)

              comm_check_on_output(__a					)

              UpperCAmelCase_			       =  model(
                  pixel_values=__a  ,     pixel_mask=__a  ,     mask_labels=__a  ,     class_labels=__a					)

         comm_check_on_output(__a					)

         self.parent.assertTrue(result.loss is not None					)
         self.parent.assertEqual(result.loss.shape  ,     torch.Size([1]					)					)




@require_torch
class 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		List[str]			  =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    a__			:		List[Any]			  =    (
        {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    a__			:		Optional[int]			  =    False
    a__			:		str			  =    False
    a__			:		Dict			  =    False
    a__			:		List[Any]			  =    False



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  MaskFormerModelTester(self					)
         UpperCAmelCase_			       =  ConfigTester(self  ,     config_class=__a  ,     has_text_modality=__a					)



    def    _lowercase		(self       :       Optional[Any]					):
         self.config_tester.run_common_tests()



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__a  ,     **__a  ,     output_hidden_states=__a					)



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__a					)



    @unittest.skip(reason="MaskFormer does not use inputs_embeds"					)
    def    _lowercase		(self       :       str					):
         pass



    @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"					)
    def    _lowercase		(self       :       Any					):
         pass



    @unittest.skip(reason="MaskFormer is not a generative model"					)
    def    _lowercase		(self       :       str					):
         pass



    @unittest.skip(reason="MaskFormer does not use token embeddings"					)
    def    _lowercase		(self       :       Any					):
         pass



    @require_torch_multi_gpu
    @unittest.skip(
        reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"					)
    def    _lowercase		(self       :       List[Any]					):
         pass



    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
    def    _lowercase		(self       :       str					):
         pass



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_			       =  model_class(__a					)
              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]  ,     __a					)



    @slow
    def    _lowercase		(self       :       List[str]					):
         for model_name in ["facebook/maskformer-swin-small-coco"]:
              UpperCAmelCase_			       =  MaskFormerModel.from_pretrained(__a					)
              self.assertIsNotNone(__a					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  (self.model_tester.min_size,) * 2
         UpperCAmelCase_			       =  {
             "pixel_values": torch.randn((2, 3, *size)  ,     device=__a					),
             "mask_labels": torch.randn((2, 10, *size)  ,     device=__a					),
             "class_labels": torch.zeros(2  ,     10  ,     device=__a					).long(),
         }

         UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(__a					)
         UpperCAmelCase_			       =  model(**__a					)
         self.assertTrue(outputs.loss is not None					)



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__a  ,     **__a  ,     output_hidden_states=__a					)



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_			       =  model_class(__a					).to(__a					)
              UpperCAmelCase_			       =  model(**__a  ,     output_attentions=__a					)
              self.assertTrue(outputs.attentions is not None					)



    def    _lowercase		(self       :       List[Any]					):
         if not self.model_tester.is_training:
              return
         # only MaskFormerForInstanceSegmentation has the loss
         UpperCAmelCase_			       =  self.all_model_classes[1]
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()

         UpperCAmelCase_			       =  model_class(__a					)
         model.to(__a					)
         model.train()

         UpperCAmelCase_			       =  model(__a  ,     mask_labels=__a  ,     class_labels=__a					).loss
         loss.backward()






    def    _lowercase		(self       :       Optional[Any]					):
         # only MaskFormerForInstanceSegmentation has the loss
         UpperCAmelCase_			       =  self.all_model_classes[1]
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase_			       =  True
         UpperCAmelCase_			       =  True

         UpperCAmelCase_			       =  model_class(__a					)
         model.to(__a					)
         model.train()

         UpperCAmelCase_			       =  model(__a  ,     mask_labels=__a  ,     class_labels=__a					)

         UpperCAmelCase_			       =  outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         UpperCAmelCase_			       =  outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()
         # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
         UpperCAmelCase_			       =  outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         UpperCAmelCase_			       =  outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__a					)

         self.assertIsNotNone(encoder_hidden_states.grad					)
         self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
         self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
         self.assertIsNotNone(attentions.grad					)




SCREAMING_SNAKE_CASE_:	int    =1E-4





def       lowerCAmelCase_							(   )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     return image






@require_vision
@slow
class 			__A (					unittest.TestCase      ):



    @cached_property
    def    _lowercase		(self       :       str					):
         return (
             MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"					)
             if is_vision_available()
             else None
         )



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"					).to(__a					)
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)

         UpperCAmelCase_			       =  torch.tensor(
             [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)

         UpperCAmelCase_			       =  torch.tensor(
             [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)

         UpperCAmelCase_			       =  torch.tensor(
             [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,     __a  ,     atol=__a					)					)



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)
         # masks_queries_logits
         UpperCAmelCase_			       =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
         UpperCAmelCase_			       =  [
             [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
             [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
             [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
         ]
         UpperCAmelCase_			       =  torch.tensor(__a					).to(__a					)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)
         # class_queries_logits
         UpperCAmelCase_			       =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
         UpperCAmelCase_			       =  torch.tensor(
             [
                 [1.6512E00, -5.2572E00, -3.3519E00],
                 [3.6169E-02, -5.9025E00, -2.9313E00],
                 [1.0766E-04, -7.7630E00, -5.1263E00],
             ]					).to(__a					)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     __a  ,     atol=__a					)					)



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)
         # masks_queries_logits
         UpperCAmelCase_			       =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
         UpperCAmelCase_			       =  [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
         UpperCAmelCase_			       =  torch.tensor(__a					).to(__a					)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)
         # class_queries_logits
         UpperCAmelCase_			       =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
         UpperCAmelCase_			       =  torch.tensor(
             [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]					).to(__a					)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     __a  ,     atol=__a					)					)






    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor

         UpperCAmelCase_			       =  image_processor(
             [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]  ,     segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]  ,     return_tensors="pt"  ,     )

         UpperCAmelCase_			       =  inputs["pixel_values"].to(__a					)
         UpperCAmelCase_			       =  [el.to(__a					) for el in inputs["mask_labels"]]
         UpperCAmelCase_			       =  [el.to(__a					) for el in inputs["class_labels"]]

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)

         self.assertTrue(outputs.loss is not None					)






"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							=     {
    'nielsr/canine-s': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__SCREAMING_SNAKE_CASE							=     1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__SCREAMING_SNAKE_CASE							=     0

__SCREAMING_SNAKE_CASE							=     0XE_0_0_0
__SCREAMING_SNAKE_CASE							=     0XE_0_0_1
__SCREAMING_SNAKE_CASE							=     0XE_0_0_2
__SCREAMING_SNAKE_CASE							=     0XE_0_0_3
__SCREAMING_SNAKE_CASE							=     0XE_0_0_4

# Maps special codepoints to human-readable names.
__SCREAMING_SNAKE_CASE							=     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__SCREAMING_SNAKE_CASE							=     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(							self :List[str]      ,  _lowerCamelCase :Optional[Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :List[str]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Any=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Optional[int]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Tuple=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Union[str, Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Union[str, Any]=2_048      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else bos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else eos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else sep_token
      UpperCamelCase_     :					Tuple   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else cls_token
      UpperCamelCase_     :					Optional[int]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase_     :					Dict   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  add_prefix_space=_lowerCamelCase      ,  model_max_length=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      # Creates a mapping for looking up the IDs of special symbols.
      UpperCamelCase_     :					Dict[str, int]   	={}
      for codepoint, name in SPECIAL_CODEPOINTS.items():
       UpperCamelCase_     :					List[str]   	=codepoint

      # Creates a mapping for looking up the string forms of special symbol IDs.
      UpperCamelCase_     :					Dict[int, str]   	={
          codepoint: name for name, codepoint in self._special_codepoints.items()
      }

      UpperCamelCase_     :					str   	=UNICODE_VOCAB_SIZE
      UpperCamelCase_     :					Any   	=len(self._special_codepoints   )



     @property
     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self._unicode_vocab_size



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return list(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      try:
       return ord(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid token: \'{token}\''''   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      try:
       if index in SPECIAL_CODEPOINTS:
        return SPECIAL_CODEPOINTS[index]
       return chr(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid id: {index}'''   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return "".join(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=[self.sep_token_id]
      UpperCamelCase_     :					Tuple   	=[self.cls_token_id]

      UpperCamelCase_     :					List[str]   	=cls + token_ids_a + sep
      if token_ids_a is not None:
       result += token_ids_a + sep
      return result



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase      ,  token_ids_a=_lowerCamelCase      ,  already_has_special_tokens=_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[1] + ([0] * len(_lowerCamelCase   )) + [1]
      if token_ids_a is not None:
       result += ([0] * len(_lowerCamelCase   )) + [1]
      return result



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=[self.sep_token_id]
      UpperCamelCase_     :					Optional[int]   	=[self.cls_token_id]

      UpperCamelCase_     :					Dict   	=len(cls + token_ids_a + sep   ) * [0]
      if token_ids_a is not None:
       result += len(token_ids_a + sep   ) * [1]
      return result




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      return ()
