


'''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 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						['''image_processor''', '''tokenizer''']
 __lowercase				:		int    							=						'''LayoutLMv3ImageProcessor'''
 __lowercase				:		int    							=						('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')


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

  """simple docstring"""







  __A  :		Dict       =			None
  if "feature_extractor" in kwargs:
   warnings.warn(
       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
       ' instead.'      ,      __lowercase      ,      )
   __A  :		List[Any]       =			kwargs.pop('feature_extractor'						)

  __A  :		List[str]       =			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__(__lowercase      ,      __lowercase						)


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

  """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.'						)

  # first, apply the image processor
  __A  :		Tuple       =			self.image_processor(images=__lowercase      ,      return_tensors=__lowercase						)

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

  __A  :		List[str]       =			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=__lowercase      ,      add_special_tokens=__lowercase      ,      padding=__lowercase      ,      truncation=__lowercase      ,      max_length=__lowercase      ,      stride=__lowercase      ,      pad_to_multiple_of=__lowercase      ,      return_token_type_ids=__lowercase      ,      return_attention_mask=__lowercase      ,      return_overflowing_tokens=__lowercase      ,      return_special_tokens_mask=__lowercase      ,      return_offsets_mapping=__lowercase      ,      return_length=__lowercase      ,      verbose=__lowercase      ,      return_tensors=__lowercase      ,      **__lowercase      ,      )

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

  return encoded_inputs


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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

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

  return images_with_overflow


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







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







 @property
 def  snake_case__   (      self						):

  """simple docstring"""







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










'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






