



'''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_barthez import BarthezTokenizer
else:
  lowercase_	 =						None

lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_	 =						{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_	 =						{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

lowercase_	 =						"▁"






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: List[str]    = VOCAB_FILES_NAMES
 __lowerCamelCase					: Optional[int]    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: Tuple    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: List[Any]    = ['input_ids', 'attention_mask']
 __lowerCamelCase					: List[Any]    = BarthezTokenizer



 def __init__(self				,     A=None				,     A=None				,     A="<s>"				,     A="</s>"				,     A="</s>"				,     A="<s>"				,     A="<unk>"				,     A="<pad>"				,     A="<mask>"				,     **A				,     )					->  Any:


      """simple docstring"""



      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else mask_token

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

      _a											=						vocab_file
      _a											=						False if not self.vocab_file else True



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



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



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      _a											=						[self.sep_token_id]
      _a											=						[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       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



      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(A ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
           return
      _a											=						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 ):
           copyfile(self.vocab_file				,     A )

      return (out_vocab_file,)








'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=18				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=None				,     )					->  Any:


      """simple docstring"""



      _a											=						size if size is not None else {'''shortest_edge''': 20}
      _a											=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      _a											=						parent
      _a											=						batch_size
      _a											=						num_channels
      _a											=						image_size
      _a											=						min_resolution
      _a											=						max_resolution
      _a											=						do_resize
      _a											=						size
      _a											=						do_center_crop
      _a											=						crop_size



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = MobileNetVaImageProcessor if is_vision_available() else None



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						MobileNetVaImageProcessingTester(self )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(A				,     '''do_resize''' ) )
      self.assertTrue(hasattr(A				,     '''size''' ) )
      self.assertTrue(hasattr(A				,     '''do_center_crop''' ) )
      self.assertTrue(hasattr(A				,     '''crop_size''' ) )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 20} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18} )

      _a											=						self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84 )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 42} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84} )



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


      """simple docstring"""



      pass



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     np.ndarray )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     torch.Tensor )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



