

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[str]       				=  ["image_processor", "tokenizer"]
	_lowercase  :   Optional[Any]       				=  "CLIPImageProcessor"
	_lowercase  :   int       				=  ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")







	def __init__(   self: str  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'  , __UpperCamelCase  , )
											__magic_name__							=    kwargs.pop('feature_extractor'      )

						__magic_name__							=    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__(__UpperCamelCase  , __UpperCamelCase      )







	def __call__(   self: Any  , __UpperCamelCase: List[str]=None  , __UpperCamelCase: Tuple=None  , __UpperCamelCase: Union[str, Any]=None  , **__UpperCamelCase: int      ):

						'''simple docstring'''





						if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'      )

						if text is not None:
											__magic_name__							=    self.tokenizer(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if images is not None:
											__magic_name__							=    self.image_processor(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if text is not None and images is not None:
											__magic_name__							=    image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**__UpperCamelCase      )  , tensor_type=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: str      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: int      ):

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer.model_input_names
						__magic_name__							=    self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						with open(self.vocab_file  , 'w'  , encoding='utf-8'      ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




