






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any]=2 ,     __SCREAMING_SNAKE_CASE      :     int=True ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=False ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=10 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=3 ,     __SCREAMING_SNAKE_CASE      :     Any=32 * 8 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=32 * 8 ,     __SCREAMING_SNAKE_CASE      :     Any=4 ,     __SCREAMING_SNAKE_CASE      :     Dict=64 ,     )      ->							Any:
           __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     				=hidden_dim
           __UpperCAmelCase     				=hidden_dim



    def _a							(				self      :     Optional[Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
               __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=torch.ones([self.batch_size, self.min_size, self.max_size] ,     device=__SCREAMING_SNAKE_CASE      )

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

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



    def _a							(				self      :     Optional[int]      )      ->							str:
           __UpperCAmelCase     				=MaskaFormerConfig(
               hidden_size=self.hidden_dim ,     )
           __UpperCAmelCase     				=self.num_queries
           __UpperCAmelCase     				=self.num_labels

           __UpperCAmelCase     				=[1, 1, 1, 1]
           __UpperCAmelCase     				=self.num_channels

           __UpperCAmelCase     				=64
           __UpperCAmelCase     				=128
           __UpperCAmelCase     				=self.hidden_dim
           __UpperCAmelCase     				=self.hidden_dim
           __UpperCAmelCase     				=self.hidden_dim
           return config



    def _a							(				self      :     List[str]      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self.prepare_config_and_inputs()
           __UpperCAmelCase     				={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
           return config, inputs_dict



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							Dict:
           __UpperCAmelCase     				=output.encoder_hidden_states
           __UpperCAmelCase     				=output.pixel_decoder_hidden_states
           __UpperCAmelCase     				=output.transformer_decoder_hidden_states

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



    def _a							(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=False      )      ->							List[str]:
           with torch.no_grad():
                  __UpperCAmelCase     				=MaskaFormerModel(config=__SCREAMING_SNAKE_CASE      )
                  model.to(__SCREAMING_SNAKE_CASE      )
                  model.eval()

                  __UpperCAmelCase     				=model(pixel_values=__SCREAMING_SNAKE_CASE ,     pixel_mask=__SCREAMING_SNAKE_CASE      )
                  __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     output_hidden_states=__SCREAMING_SNAKE_CASE      )

           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape ,     (self.batch_size, self.num_queries, self.hidden_dim) ,     )
           # 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(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							Any:
           __UpperCAmelCase     				=MaskaFormerForUniversalSegmentation(config=__SCREAMING_SNAKE_CASE      )
           model.to(__SCREAMING_SNAKE_CASE      )
           model.eval()

           def comm_check_on_output(__SCREAMING_SNAKE_CASE      :     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=__SCREAMING_SNAKE_CASE ,     pixel_mask=__SCREAMING_SNAKE_CASE      )
                  __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )

                  comm_check_on_output(__SCREAMING_SNAKE_CASE      )

                  __UpperCAmelCase     				=model(
                      pixel_values=__SCREAMING_SNAKE_CASE ,     pixel_mask=__SCREAMING_SNAKE_CASE ,     mask_labels=__SCREAMING_SNAKE_CASE ,     class_labels=__SCREAMING_SNAKE_CASE      )

           comm_check_on_output(__SCREAMING_SNAKE_CASE      )

           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				):
    """simple docstring"""

    lowerCamelCase	:							Any				= (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    lowerCamelCase	:							Optional[int]				= {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

    lowerCamelCase	:							str				= False
    lowerCamelCase	:							Any				= False
    lowerCamelCase	:							int				= False
    lowerCamelCase	:							List[str]				= False



    def _a							(				self      :     str      )      ->							str:
           __UpperCAmelCase     				=MaskaFormerModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__SCREAMING_SNAKE_CASE ,     has_text_modality=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							Tuple:
           self.config_tester.run_common_tests()



    def _a							(				self      :     Any      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     output_hidden_states=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[int]      )      ->							List[str]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__SCREAMING_SNAKE_CASE      )



    @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""      )
    def _a							(				self      :     Union[str, Any]      )      ->							List[str]:
           pass



    @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""      )
    def _a							(				self      :     str      )      ->							List[str]:
           pass



    @unittest.skip(reason="""Mask2Former is not a generative model"""      )
    def _a							(				self      :     Any      )      ->							Any:
           pass



    @unittest.skip(reason="""Mask2Former does not use token embeddings"""      )
    def _a							(				self      :     Union[str, Any]      )      ->							int:
           pass



    @require_torch_multi_gpu
    @unittest.skip(
        reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""      )
    def _a							(				self      :     List[Any]      )      ->							int:
           pass



    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""      )
    def _a							(				self      :     Union[str, Any]      )      ->							Tuple:
           pass



    def _a							(				self      :     str      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )
                  __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] ,     __SCREAMING_SNAKE_CASE      )



    @slow
    def _a							(				self      :     Dict      )      ->							str:
           for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                  __UpperCAmelCase     				=MaskaFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=(self.model_tester.min_size,) * 2
           __UpperCAmelCase     				={
               """pixel_values""": torch.randn((2, 3, *size) ,     device=__SCREAMING_SNAKE_CASE      ),
               """mask_labels""": torch.randn((2, 10, *size) ,     device=__SCREAMING_SNAKE_CASE      ),
               """class_labels""": torch.zeros(2 ,     10 ,     device=__SCREAMING_SNAKE_CASE      ).long(),
           }
           __UpperCAmelCase     				=self.model_tester.get_config()

           __UpperCAmelCase     				=MaskaFormerForUniversalSegmentation(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )
           self.assertTrue(outputs.loss is not None      )



    def _a							(				self      :     Any      )      ->							int:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     output_hidden_states=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[int]      )      ->							Union[str, Any]:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

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



    def _a							(				self      :     Optional[int]      )      ->							Optional[Any]:
           if not self.model_tester.is_training:
                  return

           __UpperCAmelCase     				=self.all_model_classes[1]
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()

           __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )
           model.to(__SCREAMING_SNAKE_CASE      )
           model.train()

           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     mask_labels=__SCREAMING_SNAKE_CASE ,     class_labels=__SCREAMING_SNAKE_CASE      ).loss
           loss.backward()



    def _a							(				self      :     List[Any]      )      ->							List[str]:
           __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(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )
           model.train()

           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     mask_labels=__SCREAMING_SNAKE_CASE ,     class_labels=__SCREAMING_SNAKE_CASE      )

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

           __UpperCAmelCase     				=outputs.pixel_decoder_hidden_states[0]
           pixel_decoder_hidden_states.retain_grad()

           __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=__SCREAMING_SNAKE_CASE      )

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




__A					=	1E-4







def       lowercase__	(					)	->				Optional[Any]:







       """simple docstring"""






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





@require_vision
@slow
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @cached_property
    def _a							(				self      :     Optional[int]      )      ->							str:
           return "facebook/mask2former-swin-small-coco-instance"



    @cached_property
    def _a							(				self      :     int      )      ->							List[str]:
           return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None



    def _a							(				self      :     Optional[Any]      )      ->							Any:
           __UpperCAmelCase     				=MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(__SCREAMING_SNAKE_CASE ,     return_tensors="""pt"""      ).to(__SCREAMING_SNAKE_CASE      )
           __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(__SCREAMING_SNAKE_CASE ,     (1, 3, 384, 384)      )

           with torch.no_grad():
                  __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=torch.tensor(
               [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]      ).to(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=torch.tensor(
               [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]      ).to(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=torch.tensor(
               [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]      ).to(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=__SCREAMING_SNAKE_CASE      )      )



    def _a							(				self      :     Union[str, Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(__SCREAMING_SNAKE_CASE      ).eval()
           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(__SCREAMING_SNAKE_CASE ,     return_tensors="""pt"""      ).to(__SCREAMING_SNAKE_CASE      )
           __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(__SCREAMING_SNAKE_CASE ,     (1, 3, 384, 384)      )

           with torch.no_grad():
                  __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )
           # masks_queries_logits
           __UpperCAmelCase     				=outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape ,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
           __UpperCAmelCase     				=[
               [-8.7_839, -9.0_056, -8.8_121],
               [-7.4_104, -7.0_313, -6.5_401],
               [-6.6_105, -6.3_427, -6.4_675],
           ]
           __UpperCAmelCase     				=torch.tensor(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=__SCREAMING_SNAKE_CASE      )      )
           # class_queries_logits
           __UpperCAmelCase     				=outputs.class_queries_logits
           self.assertEqual(class_queries_logits.shape ,     (1, model.config.num_queries, model.config.num_labels + 1)      )
           __UpperCAmelCase     				=torch.tensor(
               [
                   [1.8_324, -8.0_835, -4.1_922],
                   [0.8_450, -9.0_050, -3.6_053],
                   [0.3_045, -7.7_293, -3.0_275],
               ]      ).to(__SCREAMING_SNAKE_CASE      )
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=__SCREAMING_SNAKE_CASE      )      )



    def _a							(				self      :     int      )      ->							int:
           __UpperCAmelCase     				=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(__SCREAMING_SNAKE_CASE      ).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(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=[el.to(__SCREAMING_SNAKE_CASE      ) for el in inputs["""mask_labels"""]]
           __UpperCAmelCase     				=[el.to(__SCREAMING_SNAKE_CASE      ) for el in inputs["""class_labels"""]]

           with torch.no_grad():
                  __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )

           self.assertTrue(outputs.loss is not None      )











"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




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

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   str       =[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




