
'''simple docstring'''
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 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							BarthezTokenizer
				lowerCAmelCase_     			=							BarthezTokenizerFast
				lowerCAmelCase_     			=							True
				lowerCAmelCase_     			=							True







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											super().setUp()

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







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







											'''simple docstring'''






											UpperCamelCase   			=     '<pad>'
											UpperCamelCase   			=     1

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







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







											'''simple docstring'''






											UpperCamelCase   			=     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(A_     )						,				101122     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											self.assertEqual(self.get_tokenizer().vocab_size						,				101122     )







				@require_torch
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											UpperCamelCase   			=     [0, 57, 3018, 70307, 91, 2]

											UpperCamelCase   			=     self.tokenizer(
											    A_						,				max_length=len(A_     )						,				padding=A_						,				truncation=A_						,				return_tensors='pt'     )
											self.assertIsInstance(A_						,				A_     )

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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											if not self.test_rust_tokenizer:
																		return

											UpperCamelCase   			=     self.get_tokenizer()
											UpperCamelCase   			=     self.get_rust_tokenizer()

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

											UpperCamelCase   			=     tokenizer.tokenize(A_     )
											UpperCamelCase   			=     rust_tokenizer.tokenize(A_     )
											self.assertListEqual(A_						,				A_     )

											UpperCamelCase   			=     tokenizer.encode(A_						,				add_special_tokens=A_     )
											UpperCamelCase   			=     rust_tokenizer.encode(A_						,				add_special_tokens=A_     )
											self.assertListEqual(A_						,				A_     )

											UpperCamelCase   			=     self.get_rust_tokenizer()
											UpperCamelCase   			=     tokenizer.encode(A_     )
											UpperCamelCase   			=     rust_tokenizer.encode(A_     )
											self.assertListEqual(A_						,				A_     )







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







											'''simple docstring'''






											UpperCamelCase   			=     {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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.
											UpperCamelCase   			=     [
											    '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=A_						,				model_name='moussaKam/mbarthez'						,				revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'						,				sequences=A_						,				)






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






