






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Any=100       ,				lowerCamelCase		:			Tuple=13       ,				lowerCamelCase		:			Dict=30       ,				lowerCamelCase		:			Dict=2       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			Optional[Any]=32       ,				lowerCamelCase		:			Dict=4       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Dict=37       ,				lowerCamelCase		:			Union[str, Any]="gelu"       ,				lowerCamelCase		:			int=0.1       ,				lowerCamelCase		:			Tuple=0.1       ,				lowerCamelCase		:			Any=10       ,				lowerCamelCase		:			List[str]=0.02       ,				lowerCamelCase		:			Optional[Any]=3       ,				lowerCamelCase		:			Optional[int]=None       ,				lowerCamelCase		:			int=[0, 1, 2, 3]       ,				)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    100
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _UpperCAmelCase							=    (image_size // patch_size) ** 2
         _UpperCAmelCase							=    num_patches + 1


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


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

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

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels, pixel_labels


  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         return BeitConfig(
             vocab_size=self.vocab_size       ,				image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_indices=self.out_indices       ,				)


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


         """simple docstring"""
         _UpperCAmelCase							=    BeitModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


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


         """simple docstring"""
         _UpperCAmelCase							=    BeitForMaskedImageModeling(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length - 1, self.vocab_size)							)


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


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

         # test greyscale images
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    BeitForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    BeitForSemanticSegmentation(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)





  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_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    =  (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': BeitModel,
          '''image-classification''': BeitForImageClassification,
          '''image-segmentation''': BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			str							)						->	Any:


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


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


         """simple docstring"""
         self.config_tester.run_common_tests()


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


         """simple docstring"""
         pass


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


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	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							)
                self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)							)
                _UpperCAmelCase							=    model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCamelCase       ,				nn.Linear							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """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		:			Union[str, Any]							)						->	List[Any]:


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


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


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


  def  lowerCamelCase       (      self		:			int							)						->	int:


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


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


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


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


         """simple docstring"""
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                # we don't test BeitForMaskedImageModeling
                if model_class in [*get_values(lowerCamelCase							), BeitForMaskedImageModeling]:
                       continue

                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


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


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase							=    False
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                # we don't test BeitForMaskedImageModeling
                if (
                    model_class in [*get_values(lowerCamelCase							), BeitForMaskedImageModeling]
                    or not model_class.supports_gradient_checkpointing
                ):
                       continue

                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.gradient_checkpointing_enable()
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


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


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

         _UpperCAmelCase							=    _config_zero_init(lowerCamelCase							)
         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(config=lowerCamelCase							)
                for name, param in model.named_parameters():
                       # we skip lambda parameters as these require special initial values
                       # determined by config.layer_scale_init_value
                       if "lambda" in name:
                              continue
                       if param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1E9).round() / 1E9).item()       ,				[0.0, 1.0]       ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,				)





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


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




def       _SCREAMING_SNAKE_CASE (							)				->						str:
       _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		:			str							)						->	List[str]:


         """simple docstring"""
         return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""							) if is_vision_available() else None


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


         """simple docstring"""
         _UpperCAmelCase							=    BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							).to(lowerCamelCase							)

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

         # prepare bool_masked_pos
         _UpperCAmelCase							=    torch.ones((1, 196)       ,				dtype=torch.bool							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(pixel_values=lowerCamelCase       ,				bool_masked_pos=lowerCamelCase							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 196, 8192)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor(
             [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]       ,				lowerCamelCase       ,				atol=1E-2							)							)


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


         """simple docstring"""
         _UpperCAmelCase							=    BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							).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							)
         _UpperCAmelCase							=    outputs.logits

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

         _UpperCAmelCase							=    torch.tensor([-1.2385, -1.0987, -1.0108]							).to(lowerCamelCase							)

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

         _UpperCAmelCase							=    281
         self.assertEqual(logits.argmax(-1							).item()       ,				lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""							).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							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 2_1841)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([1.6881, -0.2787, 0.5901]							).to(lowerCamelCase							)

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

         _UpperCAmelCase							=    2396
         self.assertEqual(logits.argmax(-1							).item()       ,				lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
         _UpperCAmelCase							=    model.to(lowerCamelCase							)

         _UpperCAmelCase							=    BeitImageProcessor(do_resize=lowerCamelCase       ,				size=640       ,				do_center_crop=lowerCamelCase							)

         _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,				split="""test"""							)
         _UpperCAmelCase							=    Image.open(ds[0]["""file"""]							)
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

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

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 150, 160, 160)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    version.parse(PIL.__version__							) < version.parse("""9.0.0"""							)

         if is_pillow_less_than_a:
                _UpperCAmelCase							=    torch.tensor(
                    [
                        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                    ]       ,				device=lowerCamelCase       ,				)
         else:
                _UpperCAmelCase							=    torch.tensor(
                    [
                        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                    ]       ,				device=lowerCamelCase       ,				)

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





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


         """simple docstring"""
         _UpperCAmelCase							=    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
         _UpperCAmelCase							=    model.to(lowerCamelCase							)

         _UpperCAmelCase							=    BeitImageProcessor(do_resize=lowerCamelCase       ,				size=640       ,				do_center_crop=lowerCamelCase							)

         _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,				split="""test"""							)
         _UpperCAmelCase							=    Image.open(ds[0]["""file"""]							)
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

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

         _UpperCAmelCase							=    outputs.logits.detach().cpu()

         _UpperCAmelCase							=    image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase       ,				target_sizes=[(500, 300)]							)
         _UpperCAmelCase							=    torch.Size((500, 300)							)
         self.assertEqual(segmentation[0].shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase							)
         _UpperCAmelCase							=    torch.Size((160, 160)							)
         self.assertEqual(segmentation[0].shape       ,				lowerCamelCase							)




'''simple docstring'''





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

import sentencepiece as spm

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


A__    :   Tuple           =					logging.get_logger(__name__)

A__    :   int           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__a      ,     eos_token=__a      ,     unk_token=__a      ,     sep_token=__a      ,     cls_token=__a      ,     pad_token=__a      ,     mask_token=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__a      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       if token_ids_a is None:
             return [1] + ([0] * len(__a  )) + [1]
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [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]
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__a  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(__a  ) + token
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Dict													=    {}

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Dict													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



