





'''simple docstring'''






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
      )
      from transformers.models.efficientformer.modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )


if is_vision_available():
      from PIL import Image

      from transformers import EfficientFormerImageProcessor




class  UpperCAmelCase     :




      def __init__(			self			: Union[str, Any]			,			__snake_case			: Tuple			,			__snake_case			: str = 13			,			__snake_case			: Dict = 64			,			__snake_case			: str = 2			,			__snake_case			: int = 3			,			__snake_case			: Optional[Any] = 3			,			__snake_case			: List[str] = True			,			__snake_case			: List[str] = True			,			__snake_case			: List[str] = 1_28			,			__snake_case			: int=[16, 32, 64, 1_28]			,			__snake_case			: Dict = 7			,			__snake_case			: List[str] = 4			,			__snake_case			: Optional[int] = 37			,			__snake_case			: Tuple = "gelu"			,			__snake_case			: Optional[int] = 0.1			,			__snake_case			: Optional[Any] = 0.1			,			__snake_case			: List[str] = 10			,			__snake_case			: Optional[int] = 0.02			,			__snake_case			: Optional[Any] = 2			,			__snake_case			: Tuple = 1			,			__snake_case			: Optional[Any] = 1_28			,			__snake_case			: Union[str, Any] = [2, 2, 2, 2]			,			__snake_case			: Dict = 2			,			__snake_case			: Optional[int] = 2			,			)				->      Optional[Any]:
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							image_size
            _lowerCAmelCase							     =							patch_size
            _lowerCAmelCase							     =							num_channels
            _lowerCAmelCase							     =							is_training
            _lowerCAmelCase							     =							use_labels
            _lowerCAmelCase							     =							hidden_size
            _lowerCAmelCase							     =							num_hidden_layers
            _lowerCAmelCase							     =							num_attention_heads
            _lowerCAmelCase							     =							intermediate_size
            _lowerCAmelCase							     =							hidden_act
            _lowerCAmelCase							     =							hidden_dropout_prob
            _lowerCAmelCase							     =							attention_probs_dropout_prob
            _lowerCAmelCase							     =							type_sequence_label_size
            _lowerCAmelCase							     =							initializer_range
            _lowerCAmelCase							     =							encoder_stride
            _lowerCAmelCase							     =							num_attention_outputs
            _lowerCAmelCase							     =							embed_dim
            _lowerCAmelCase							     =							embed_dim + 1
            _lowerCAmelCase							     =							resolution
            _lowerCAmelCase							     =							depths
            _lowerCAmelCase							     =							hidden_sizes
            _lowerCAmelCase							     =							dim
            _lowerCAmelCase							     =							mlp_expansion_ratio




      def 				lowercase__    (			self			: int  )				->      Optional[Any]:
            _lowerCAmelCase							     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

            _lowerCAmelCase							     =							None
            if self.use_labels:
                  _lowerCAmelCase							     =							ids_tensor([self.batch_size]			,			self.type_sequence_label_size  )

            _lowerCAmelCase							     =							self.get_config()

            return config, pixel_values, labels




      def 				lowercase__    (			self			: int  )				->      Optional[int]:
            return EfficientFormerConfig(
                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=__snake_case			,			initializer_range=self.initializer_range			,			encoder_stride=self.encoder_stride			,			resolution=self.resolution			,			depths=self.depths			,			hidden_sizes=self.hidden_sizes			,			dim=self.dim			,			mlp_expansion_ratio=self.mlp_expansion_ratio			,			)




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: List[Any]			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[Any]  )				->      List[Any]:
            _lowerCAmelCase							     =							TFEfficientFormerModel(config=__snake_case  )
            _lowerCAmelCase							     =							model(__snake_case			,			training=__snake_case  )
            self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: List[str]			,			__snake_case			: Tuple			,			__snake_case			: Tuple  )				->      Any:
            _lowerCAmelCase							     =							self.type_sequence_label_size
            _lowerCAmelCase							     =							TFEfficientFormerForImageClassification(__snake_case  )
            _lowerCAmelCase							     =							model(__snake_case			,			labels=__snake_case			,			training=__snake_case  )
            self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)  )

            # test greyscale images
            _lowerCAmelCase							     =							1
            _lowerCAmelCase							     =							TFEfficientFormerForImageClassification(__snake_case  )

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




      def 				lowercase__    (			self			: str  )				->      Any:
            _lowerCAmelCase							     =							self.prepare_config_and_inputs()
            _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							config_and_inputs
            _lowerCAmelCase							     =							{"""pixel_values""": pixel_values}
            return config, inputs_dict




@require_tf
class  UpperCAmelCase     (			snake_case_							,		snake_case_							,		unittest.TestCase   ):
      _lowercase:			Optional[int]						     =   (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      _lowercase:			List[str]						     =   (
          {
              '''feature-extraction''': TFEfficientFormerModel,
              '''image-classification''': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      _lowercase:			Tuple						     =   False

      _lowercase:			Union[str, Any]						     =   False
      _lowercase:			Dict						     =   False
      _lowercase:			List[str]						     =   False
      _lowercase:			List[Any]						     =   False




      def 				lowercase__    (			self			: Union[str, Any]  )				->      List[str]:
            _lowerCAmelCase							     =							TFEfficientFormerModelTester(self  )
            _lowerCAmelCase							     =							ConfigTester(
                self			,			config_class=__snake_case			,			has_text_modality=__snake_case			,			hidden_size=37  )




      def 				lowercase__    (			self			: Any  )				->      List[str]:
            self.config_tester.run_common_tests()




      @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""  )
      def 				lowercase__    (			self			: str  )				->      int:
            pass




      @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""  )
      def 				lowercase__    (			self			: Any  )				->      Optional[int]:
            pass




      def 				lowercase__    (			self			: Any  )				->      Union[str, Any]:
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()

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

                  _lowerCAmelCase							     =							["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]			,			__snake_case  )




      def 				lowercase__    (			self			: Optional[int]  )				->      Union[str, Any]:
            def check_hidden_states_output(__snake_case			: Optional[int]			,			__snake_case			: Tuple			,			__snake_case			: Tuple  ):
                  _lowerCAmelCase							     =							model_class(__snake_case  )

                  _lowerCAmelCase							     =							model(**self._prepare_for_class(__snake_case			,			__snake_case  )			,			training=__snake_case  )
                  _lowerCAmelCase							     =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  _lowerCAmelCase							     =							getattr(
                      self.model_tester			,			"""expected_num_hidden_layers"""			,			self.model_tester.num_hidden_layers + 1  )
                  self.assertEqual(len(__snake_case  )			,			__snake_case  )

                  if hasattr(self.model_tester			,			"""encoder_seq_length"""  ):
                        _lowerCAmelCase							     =							self.model_tester.encoder_seq_length
                        if hasattr(self.model_tester			,			"""chunk_length"""  ) and self.model_tester.chunk_length > 1:
                              _lowerCAmelCase							     =							seq_length * self.model_tester.chunk_length
                  else:
                        _lowerCAmelCase							     =							self.model_tester.seq_length

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:]  )			,			[seq_length, self.model_tester.hidden_size]			,			)

                  if config.is_encoder_decoder:
                        _lowerCAmelCase							     =							outputs.decoder_hidden_states

                        self.asseretIsInstance(__snake_case			,			(list, tuple)  )
                        self.assertEqual(len(__snake_case  )			,			__snake_case  )
                        _lowerCAmelCase							     =							getattr(self.model_tester			,			"""seq_length"""			,			__snake_case  )
                        _lowerCAmelCase							     =							getattr(self.model_tester			,			"""decoder_seq_length"""			,			__snake_case  )

                        self.assertListEqual(
                            list(hidden_states[-1].shape[-2:]  )			,			[decoder_seq_length, self.model_tester.hidden_size]			,			)

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  _lowerCAmelCase							     =							True
                  check_hidden_states_output(__snake_case			,			__snake_case			,			__snake_case  )

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

                  check_hidden_states_output(__snake_case			,			__snake_case			,			__snake_case  )




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: int			,			__snake_case			: Tuple			,			__snake_case			: List[Any]=False  )				->      Tuple:
            _lowerCAmelCase							     =							super()._prepare_for_class(__snake_case			,			__snake_case			,			return_labels=__snake_case  )

            if return_labels:
                  if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                        del inputs_dict["labels"]

            return inputs_dict




      def 				lowercase__    (			self			: Optional[int]  )				->      List[str]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__snake_case  )




      @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""  )
      def 				lowercase__    (			self			: str  )				->      Dict:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case  )




      def 				lowercase__    (			self			: List[Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__snake_case  )




      @slow
      def 				lowercase__    (			self			: Dict  )				->      List[Any]:
            for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase							     =							TFEfficientFormerModel.from_pretrained(__snake_case  )
                  self.assertIsNotNone(__snake_case  )




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase							     =							True

            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""seq_length"""			,			__snake_case  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""encoder_seq_length"""			,			__snake_case  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""key_length"""			,			__snake_case  )
            _lowerCAmelCase							     =							getattr(self.model_tester			,			"""chunk_length"""			,			__snake_case  )

            if chunk_length is not None and hasattr(self.model_tester			,			"""num_hashes"""  ):
                  _lowerCAmelCase							     =							encoder_seq_length * self.model_tester.num_hashes

            for model_class in self.all_model_classes:
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							False
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							model_class(__snake_case  )

                  _lowerCAmelCase							     =							model(**self._prepare_for_class(__snake_case			,			__snake_case  )			,			training=__snake_case  )
                  _lowerCAmelCase							     =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(__snake_case  )			,			self.model_tester.num_attention_outputs  )

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  _lowerCAmelCase							     =							True
                  _lowerCAmelCase							     =							model_class(__snake_case  )
                  _lowerCAmelCase							     =							model(**self._prepare_for_class(__snake_case			,			__snake_case  )			,			training=__snake_case  )

                  _lowerCAmelCase							     =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(__snake_case  )			,			self.model_tester.num_attention_outputs  )

                  if chunk_length is not None:
                        self.assertListEqual(
                            list(attentions[0].shape[-4:]  )			,			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,			)
                  else:
                        self.assertListEqual(
                            list(attentions[0].shape[-3:]  )			,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,			)




      def 				lowercase__    (			self			: Optional[int]  )				->      Optional[int]:
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  # Prepare our model
                  _lowerCAmelCase							     =							model_class(__snake_case  )
                  # These are maximally general inputs for the model, with multiple None dimensions
                  # Hopefully this will catch any conditionals that fail for flexible shapes
                  _lowerCAmelCase							     =							{
                      key: tf.keras.Input(shape=val.shape[1:]			,			dtype=val.dtype			,			name=__snake_case  )
                      for key, val in model.input_signature.items()
                      if key in model.dummy_inputs
                  }
                  _lowerCAmelCase							     =							model(__snake_case  )
                  self.assertTrue(outputs_dict is not None  )




def    UpperCamelCase__      (		):





      """simple docstring"""


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





@require_tf
@require_vision
class  UpperCAmelCase     (			unittest.TestCase   ):




      @cached_property
      def 				lowercase__    (			self			: Optional[int]  )				->      Tuple:
            return (
                EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""  )
                if is_vision_available()
                else None
            )




      @slow
      def 				lowercase__    (			self			: List[str]  )				->      Optional[Any]:
            _lowerCAmelCase							     =							TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""  )
            _lowerCAmelCase							     =							self.default_image_processor
            _lowerCAmelCase							     =							prepare_img()
            _lowerCAmelCase							     =							image_processor(images=__snake_case			,			return_tensors="""tf"""  )
            # forward pass
            _lowerCAmelCase							     =							model(**__snake_case			,			training=__snake_case  )
            # verify the logits
            _lowerCAmelCase							     =							tf.TensorShape((1, 10_00)  )
            self.assertEqual(outputs.logits.shape			,			__snake_case  )
            _lowerCAmelCase							     =							tf.constant([-0.05_55, 0.48_25, -0.08_52]  )
            self.assertTrue(np.allclose(outputs.logits[0, :3]			,			__snake_case			,			atol=1E-4  )  )




      @slow
      def 				lowercase__    (			self			: int  )				->      int:
            _lowerCAmelCase							     =							TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
                """snap-research/efficientformer-l1-300"""  )
            _lowerCAmelCase							     =							self.default_image_processor
            _lowerCAmelCase							     =							prepare_img()
            _lowerCAmelCase							     =							image_processor(images=__snake_case			,			return_tensors="""tf"""  )
            # forward pass
            _lowerCAmelCase							     =							model(**__snake_case			,			training=__snake_case  )
            # verify the logits
            _lowerCAmelCase							     =							tf.TensorShape((1, 10_00)  )
            self.assertEqual(outputs.logits.shape			,			__snake_case  )
            _lowerCAmelCase							     =							tf.constant([-0.13_12, 0.43_53, -1.04_99]  )
            self.assertTrue(np.allclose(outputs.logits[0, :3]			,			__snake_case			,			atol=1E-4  )  )










'''simple docstring'''

import os
import unicodedata
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 SPIECE_UNDERLINE, logging


a_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {"""vocab_file""": """spiece.model"""}

a_  :				Tuple						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(UpperCamelCase     )

   try:
    import jieba
   except ModuleNotFoundError as error:
    raise error.__class__(
        "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
        "See https://pypi.org/project/jieba/ for installation."     )
   lowerCamelCase_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

   # for backward compatibility
   if not hasattr(self			,  "sp_model_kwargs"     ):
    lowerCamelCase_      =  {}

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  UpperCamelCase     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(UpperCamelCase     )
   lowerCamelCase_      =  self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(UpperCamelCase     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(UpperCamelCase     )
    else:
     new_pieces.append(UpperCamelCase     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [self.cls_token_id]
   if token_ids_a is None:
    return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

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

   if token_ids_a is not None:
    return ([0] * len(UpperCamelCase     )) + [1] + ([0] * len(UpperCamelCase     )) + [1, 1]
   return ([0] * len(UpperCamelCase     )) + [1, 1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [2]

   if token_ids_a is None:
    return len(token_ids_a + sep     ) * [0] + cls_segment_id
   return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







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

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file			,  UpperCamelCase     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(UpperCamelCase			,  "wb"     ) as fi:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text



