




import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			float
      lowerCAmelCase__      :			Optional[float]     =       None
      lowerCAmelCase__      :			Optional[float]     =       None







class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    ModelOutputTest(a=30					)
          self.assertEqual(x.a			,							30					)
          self.assertIsNone(x.b					)
          self.assertIsNone(x.c					)
          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x.d







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    ModelOutputTest(a=30			,							b=10					)
          self.assertEqual(x[0]			,							30					)
          self.assertEqual(x[1]			,							10					)
          self.assertEqual(x[:2]			,							(30, 10)					)
          self.assertEqual(x[:]			,							(30, 10)					)

          __UpperCamelCase			=    ModelOutputTest(a=30			,							c=10					)
          self.assertEqual(x[0]			,							30					)
          self.assertEqual(x[1]			,							10					)
          self.assertEqual(x[:2]			,							(30, 10)					)
          self.assertEqual(x[:]			,							(30, 10)					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    ModelOutputTest(a=30			,							b=10					)
          self.assertEqual(x['''a''']			,							30					)
          self.assertEqual(x['''b''']			,							10					)
          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x['''c''']

          __UpperCamelCase			=    ModelOutputTest(a=30			,							c=10					)
          self.assertEqual(x['''a''']			,							30					)
          self.assertEqual(x['''c''']			,							10					)
          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x['''b''']







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    ModelOutputTest(a=30					)
          self.assertEqual(list(x.keys()					)			,							['''a''']					)
          self.assertEqual(list(x.values()					)			,							[30]					)
          self.assertEqual(list(x.items()					)			,							[('''a''', 30)]					)
          self.assertEqual(list(snake_case					)			,							['''a''']					)

          __UpperCamelCase			=    ModelOutputTest(a=30			,							b=10					)
          self.assertEqual(list(x.keys()					)			,							['''a''', '''b''']					)
          self.assertEqual(list(x.values()					)			,							[30, 10]					)
          self.assertEqual(list(x.items()					)			,							[('''a''', 30), ('''b''', 10)]					)
          self.assertEqual(list(snake_case					)			,							['''a''', '''b''']					)

          __UpperCamelCase			=    ModelOutputTest(a=30			,							c=10					)
          self.assertEqual(list(x.keys()					)			,							['''a''', '''c''']					)
          self.assertEqual(list(x.values()					)			,							[30, 10]					)
          self.assertEqual(list(x.items()					)			,							[('''a''', 30), ('''c''', 10)]					)
          self.assertEqual(list(snake_case					)			,							['''a''', '''c''']					)

          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x.update({'''d''': 20}					)
          with self.assertRaises(snake_case					):
              del x["a"]
          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x.pop('''a'''					)
          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    x.setdefault('''d'''			,							32					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    ModelOutputTest(a=30					)
          __UpperCamelCase			=    10
          self.assertEqual(x.a			,							10					)
          self.assertEqual(x['''a''']			,							10					)







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    ModelOutputTest(a=30					)
          __UpperCamelCase			=    10
          self.assertEqual(x.a			,							10					)
          self.assertEqual(x['''a''']			,							10					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    ModelOutputTest({'''a''': 30, '''b''': 10}					)
          self.assertEqual(list(x.keys()					)			,							['''a''', '''b''']					)
          self.assertEqual(x.a			,							30					)
          self.assertEqual(x.b			,							10					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    ModelOutputTest([('''a''', 30), ('''b''', 10)]					)
          self.assertEqual(list(x.keys()					)			,							['''a''', '''b''']					)
          self.assertEqual(x.a			,							30					)
          self.assertEqual(x.b			,							10					)

          with self.assertRaises(snake_case					):
              __UpperCamelCase			=    ModelOutputTest([('''a''', 30), (10, 10)]					)

          __UpperCamelCase			=    ModelOutputTest(a=(30, 30)					)
          self.assertEqual(list(x.keys()					)			,							['''a''']					)
          self.assertEqual(x.a			,							(30, 30)					)









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     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase				     =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase				     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    **lowerCAmelCase__		:			int     )     ->   Union[str, Any]:
   UpperCAmelCase          =	None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."      ,    lowerCAmelCase__      ,    )
     UpperCAmelCase          =	kwargs.pop("feature_extractor"     )

   UpperCAmelCase          =	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		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,    lowerCAmelCase__		:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,    lowerCAmelCase__		:			Union[List[List[int]], List[List[List[int]]]] = None      ,    lowerCAmelCase__		:			Optional[Union[List[int], List[List[int]]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[bool, str, PaddingStrategy] = False      ,    lowerCAmelCase__		:			Union[bool, str, TruncationStrategy] = False      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			int = 0      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   BatchEncoding:
   # verify input
   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
   UpperCAmelCase          =	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__     ):
       UpperCAmelCase          =	[text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCAmelCase          =	features["words"]

   UpperCAmelCase          =	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
   UpperCAmelCase          =	features.pop("pixel_values"     )
   if return_overflowing_tokens is True:
     UpperCAmelCase          =	self.get_overflowing_images(lowerCAmelCase__      ,    encoded_inputs["overflow_to_sample_mapping"]     )
   UpperCAmelCase          =	images

   return encoded_inputs


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   int:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase          =	[]
   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		:			Optional[Any]      ,    *lowerCAmelCase__		:			int      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[str]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			Tuple      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   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		:			List[Any]     )     ->   int:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor



