





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 GLPNImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       List[Any]    ,  a    :       Tuple    ,  a    :       Tuple=7    ,  a    :       List[str]=3    ,  a    :       Dict=18    ,  a    :       int=30    ,  a    :       List[Any]=400    ,  a    :       Any=True    ,  a    :       Any=32    ,  a    :       str=True    ,  )->     int:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size_divisor
     lowercase__			    =						do_rescale


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size_divisor": self.size_divisor,
         "do_rescale": self.do_rescale,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[int]				      = GLPNImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						GLPNImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size_divisor' ) )
     self.assertTrue(hasattr(a    ,  'resample' ) )
     self.assertTrue(hasattr(a    ,  'do_rescale' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Optional[int]:





     """simple docstring"""







     pass


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





     """simple docstring"""







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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )


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





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						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 (GLPNImageProcessor doesn't support batching)
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )


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





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						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 (GLPNImageProcessor doesn't support batching)
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )









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        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Dict				      = VOCAB_FILES_NAMES
   _UpperCamelCase					:	str				      = PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase					:	Optional[int]				      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase					:	Optional[Any]				      = ['input_ids', 'attention_mask']
   _UpperCamelCase					:	int				      = BarthezTokenizer


   def __init__(					self    :       List[Any]    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[Any]=None    ,  a    :       Dict="<s>"    ,  a    :       Union[str, Any]="</s>"    ,  a    :       List[str]="</s>"    ,  a    :       Optional[Any]="<s>"    ,  a    :       int="<unk>"    ,  a    :       str="<pad>"    ,  a    :       Optional[int]="<mask>"    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						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    ,  )

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  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]
     lowercase__			    =						[self.cls_token_id]
     lowercase__			    =						[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[self.sep_token_id]
     lowercase__			    =						[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  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       str    ,  a    :       Optional[str] = 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
     lowercase__			    =						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,)


