





"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					["""image_processor""", """tokenizer"""]
 _lowerCamelCase				=					"""LayoutLMv2ImageProcessor"""
 _lowerCamelCase				=					("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

 def __init__(   self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.''' ,						__lowerCamelCase ,						)
         __A   :						Tuple									=					kwargs.pop('''feature_extractor'''  )

     __A   :						Tuple									=					image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''  )
     if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''  )

     super().__init__(__lowerCamelCase ,						__lowerCamelCase  )

 def __call__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = 0 ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes '''
             '''if you initialized the image processor with apply_ocr set to True.'''  )

     if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''  )

     if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''  )

     # first, apply the image processor
     __A   :						Union[str, Any]									=					self.image_processor(images=__lowerCamelCase ,						return_tensors=__lowerCamelCase  )

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						Union[str, Any]									=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __A   :						Dict									=					features['''words''']

     __A   :						List[Any]									=					self.tokenizer(
         text=text if text is not None else features['''words'''] ,						text_pair=text_pair if text_pair is not None else None ,						boxes=boxes if boxes is not None else features['''boxes'''] ,						word_labels=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=__lowerCamelCase ,						stride=__lowerCamelCase ,						pad_to_multiple_of=__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase ,						return_attention_mask=__lowerCamelCase ,						return_overflowing_tokens=__lowerCamelCase ,						return_special_tokens_mask=__lowerCamelCase ,						return_offsets_mapping=__lowerCamelCase ,						return_length=__lowerCamelCase ,						verbose=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase ,						)

     # add pixel values
     __A   :						List[str]									=					features.pop('''pixel_values'''  )
     if return_overflowing_tokens is True:
         __A   :						Any									=					self.get_overflowing_images(__lowerCamelCase ,						encoded_inputs['''overflow_to_sample_mapping''']  )
     __A   :						List[str]									=					images

     return encoded_inputs

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					[]
     for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]  )

     if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F""" {len(__lowerCamelCase  )} and {len(__lowerCamelCase  )}"""  )

     return images_with_overflow

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase  )

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return ["input_ids", "bbox", "attention_mask", "image"]

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     warnings.warn(
         '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,						__lowerCamelCase ,						)
     return self.image_processor_class







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,						__lowerCamelCase ,						)
     return self.image_processor











"""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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					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(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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   :						Union[str, Any]									=					[
         '''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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




