

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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[Any]       				=  ["image_processor", "tokenizer"]
	_lowercase  :   Dict       				=  "LayoutLMv2ImageProcessor"
	_lowercase  :   Union[str, Any]       				=  ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")







	def __init__(   self: str  , __UpperCamelCase: List[str]=None  , __UpperCamelCase: int=None  , **__UpperCamelCase: Dict      ):

						'''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.'  , __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: Union[str, Any]  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  , __UpperCamelCase: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  , __UpperCamelCase: Union[List[List[int]], List[List[List[int]]]] = None  , __UpperCamelCase: Optional[Union[List[int], List[List[int]]]] = None  , __UpperCamelCase: bool = True  , __UpperCamelCase: Union[bool, str, PaddingStrategy] = False  , __UpperCamelCase: Union[bool, str, TruncationStrategy] = None  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: int = 0  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[bool] = None  , __UpperCamelCase: Optional[bool] = None  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , **__UpperCamelCase: Dict  , ):

						'''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
						__magic_name__							=    self.image_processor(images=__UpperCamelCase  , return_tensors=__UpperCamelCase      )

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

						__magic_name__							=    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=__UpperCamelCase  , add_special_tokens=__UpperCamelCase  , padding=__UpperCamelCase  , truncation=__UpperCamelCase  , max_length=__UpperCamelCase  , stride=__UpperCamelCase  , pad_to_multiple_of=__UpperCamelCase  , return_token_type_ids=__UpperCamelCase  , return_attention_mask=__UpperCamelCase  , return_overflowing_tokens=__UpperCamelCase  , return_special_tokens_mask=__UpperCamelCase  , return_offsets_mapping=__UpperCamelCase  , return_length=__UpperCamelCase  , verbose=__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase  , )

						# add pixel values
						__magic_name__							=    features.pop('pixel_values'      )
						if return_overflowing_tokens is True:
											__magic_name__							=    self.get_overflowing_images(__UpperCamelCase  , encoded_inputs['overflow_to_sample_mapping']      )
						__magic_name__							=    images

						return encoded_inputs







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

						'''simple docstring'''





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

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

						return images_with_overflow







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

						'''simple docstring'''





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







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

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





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







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

						'''simple docstring'''





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







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




