




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      BarthezTokenizer
    UpperCAmelCase__							  =      BarthezTokenizerFast
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      True


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Dict:





           '''simple docstring'''

           super().setUp()

           A__       =    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
           tokenizer.save_pretrained(self.tmpdirname)
           tokenizer.save_pretrained(self.tmpdirname					, legacy_format=UpperCAmelCase__)
           A__       =    tokenizer


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





           '''simple docstring'''

           A__       =    '''<pad>'''
           A__       =    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]					, '''<s>''')
           self.assertEqual(vocab_keys[1]					, '''<pad>''')
           self.assertEqual(vocab_keys[-1]					, '''<mask>''')
           self.assertEqual(len(UpperCAmelCase__)					, 101_122)


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





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size					, 101_122)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->str:





           '''simple docstring'''

           A__       =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           A__       =    [0, 57, 3_018, 70_307, 91, 2]

           A__       =    self.tokenizer(
               UpperCAmelCase__					, max_length=len(UpperCAmelCase__)					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, return_tensors='''pt''')
           self.assertIsInstance(UpperCAmelCase__					, UpperCAmelCase__)

           self.assertEqual((2, 6)					, batch.input_ids.shape)
           self.assertEqual((2, 6)					, batch.attention_mask.shape)
           A__       =    batch.input_ids.tolist()[0]
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[Any]:





           '''simple docstring'''

           if not self.test_rust_tokenizer:
                  return

           A__       =    self.get_tokenizer()
           A__       =    self.get_rust_tokenizer()

           A__       =    '''I was born in 92000, and this is falsé.'''

           A__       =    tokenizer.tokenize(UpperCAmelCase__)
           A__       =    rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    self.get_rust_tokenizer()
           A__       =    tokenizer.encode(UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    {'''input_ids''': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # moussaKam/mbarthez is a french model. So we also use french texts.
           A__       =    [
               '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
               '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
               '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
               '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
               '''telles que la traduction et la synthèse de texte.''',
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase__					, model_name='''moussaKam/mbarthez'''					, revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					, sequences=UpperCAmelCase__					, )









from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

