



'''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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			['image_processor', 'tokenizer']
 __lowerCamelCase       :			Union[str, Any]		=			'LayoutLMv3ImageProcessor'
 __lowerCamelCase       :			List[str]		=			('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')



 def __init__(self					,  A=None					,  A=None					,  **A			)       ->	Tuple:


      """simple docstring"""



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

      _a           =    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__(A					,  A			)



 def __call__(self					,  A					,  A = None					,  A = None					,  A = None					,  A = None					,  A = True					,  A = False					,  A = None					,  A = None					,  A = 0					,  A = None					,  A = None					,  A = None					,  A = False					,  A = False					,  A = False					,  A = False					,  A = True					,  A = None					,  **A					,  )       ->	BatchEncoding:


      """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           =    self.image_processor(images=A					,  return_tensors=A			)

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

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

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

      return encoded_inputs



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


      """simple docstring"""



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

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

      return images_with_overflow



 def    a__    (self					,  *A					,  **A			)       ->	int:


      """simple docstring"""



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



 def    a__    (self					,  *A					,  **A			)       ->	List[str]:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	str:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	str:


      """simple docstring"""



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











import os
import sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)