






'''simple docstring'''



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			None
      UpperCamelCase_								=			None






      @property
      def 				A__      ( self			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       return self.feat_extract_tester.prepare_feat_extract_dict()






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


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.feature_extraction_class(**self.feat_extract_dict		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''feature_size'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''sampling_rate'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''padding_value'''		)		)






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


       '''simple docstring'''

       lowercase					:   int				  =self.feat_extract_tester.prepare_inputs_for_common()
       lowercase					:   List[Any]				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Dict				  =feat_extract.model_input_names[0]

       lowercase					:   Union[str, Any]				  =BatchFeature({input_name: speech_inputs}		)

       self.assertTrue(all(len(UpperCAmelCase		) == len(UpperCAmelCase		) for x, y in zip(UpperCAmelCase     ,      processed_features[input_name]		)		)		)

       lowercase					:   Optional[Any]				  =self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase		)
       lowercase					:   List[Any]				  =BatchFeature({input_name: speech_inputs}     ,      tensor_type='''np'''		)

       lowercase					:   int				  =processed_features[input_name]

       if len(batch_features_input.shape		) < 3:
        lowercase					:   Optional[Any]				  =batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)






      @require_torch
      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase		)
       lowercase					:   Dict				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Union[str, Any]				  =feat_extract.model_input_names[0]

       lowercase					:   List[str]				  =BatchFeature({input_name: speech_inputs}     ,      tensor_type='''pt'''		)

       lowercase					:   Tuple				  =processed_features[input_name]

       if len(batch_features_input.shape		) < 3:
        lowercase					:   Tuple				  =batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)






      @require_tf
      def 				A__      ( self			:		List[str]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase		)
       lowercase					:   List[Any]				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Tuple				  =feat_extract.model_input_names[0]

       lowercase					:   str				  =BatchFeature({input_name: speech_inputs}     ,      tensor_type='''tf'''		)

       lowercase					:   Dict				  =processed_features[input_name]

       if len(batch_features_input.shape		) < 3:
        lowercase					:   List[str]				  =batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[Any]=False		)  ->  str:


       '''simple docstring'''

       def _inputs_have_equal_length(UpperCAmelCase			:		List[str]		):
        lowercase					:   Tuple				  =len(input[0]		)
        for input_slice in input[1:]:
         if len(UpperCAmelCase		) != length:
          return False
        return True

       def _inputs_are_equal(UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any		):
        if len(UpperCAmelCase		) != len(UpperCAmelCase		):
         return False

        for input_slice_a, input_slice_a in zip(UpperCAmelCase     ,      UpperCAmelCase		):
         if not np.allclose(np.asarray(UpperCAmelCase		)     ,      np.asarray(UpperCAmelCase		)     ,      atol=1e-3		):
          return False
        return True

       lowercase					:   str				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Optional[int]				  =self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCAmelCase		)
       lowercase					:   List[str]				  =feat_extract.model_input_names[0]

       lowercase					:   Any				  =BatchFeature({input_name: speech_inputs}		)

       lowercase					:   List[str]				  =self.feat_extract_tester.seq_length_diff
       lowercase					:   Union[str, Any]				  =self.feat_extract_tester.max_seq_length + pad_diff
       lowercase					:   Union[str, Any]				  =self.feat_extract_tester.min_seq_length
       lowercase					:   Tuple				  =self.feat_extract_tester.batch_size
       lowercase					:   List[str]				  =self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       lowercase					:   Union[str, Any]				  =feat_extract.pad(UpperCAmelCase     ,      padding=UpperCAmelCase		)
       lowercase					:   str				  =input_a[input_name]

       lowercase					:   List[str]				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''		)
       lowercase					:   Any				  =input_a[input_name]

       lowercase					:   Tuple				  =feat_extract.pad(UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[-1]		)		)
       lowercase					:   Union[str, Any]				  =input_a[input_name]

       lowercase					:   Tuple				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''np'''		)
       lowercase					:   str				  =input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(UpperCAmelCase		):
        feat_extract.pad(UpperCAmelCase     ,      padding='''max_length'''		)[input_name]

       lowercase					:   List[Any]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=UpperCAmelCase     ,      return_tensors='''np'''		)
       lowercase					:   Union[str, Any]				  =input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(_inputs_are_equal(UpperCAmelCase     ,      UpperCAmelCase		)		)
       self.assertTrue(len(input_a[0]		) == pad_min_length		)
       self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       lowercase					:   str				  =feat_extract.pad(UpperCAmelCase     ,      pad_to_multiple_of=10		)
       lowercase					:   int				  =input_a[input_name]

       lowercase					:   Any				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      pad_to_multiple_of=10		)
       lowercase					:   List[Any]				  =input_a[input_name]

       lowercase					:   Union[str, Any]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      pad_to_multiple_of=10     ,      max_length=UpperCAmelCase		)
       lowercase					:   int				  =input_a[input_name]

       lowercase					:   Dict				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      pad_to_multiple_of=10     ,      max_length=UpperCAmelCase     ,      return_tensors='''np'''     ,      )
       lowercase					:   Any				  =input_a[input_name]

       self.assertTrue(all(len(UpperCAmelCase		) % 10 == 0 for x in input_a		)		)
       self.assertTrue(_inputs_are_equal(UpperCAmelCase     ,      UpperCAmelCase		)		)

       lowercase					:   Union[str, Any]				  =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(UpperCAmelCase		) == expected_mult_pad_length for x in input_a		)		)
       self.assertEqual(input_a.shape[:2]     ,      (batch_size, expected_mult_pad_length)		)

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size		)

       # Check padding value is correct
       lowercase					:   Union[str, Any]				  =(np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
           < 1e-3		)
       self.assertTrue(
           abs(
               np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
           < 1e-3		)
       self.assertTrue(
           abs(
               np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
           < 1e-3		)
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1e-3		)
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
           < 1e-3		)






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Optional[int]=False		)  ->  Optional[Any]:


       '''simple docstring'''

       def _inputs_have_equal_length(UpperCAmelCase			:		int		):
        lowercase					:   List[Any]				  =len(input[0]		)
        for input_slice in input[1:]:
         if len(UpperCAmelCase		) != length:
          return False
        return True

       def _inputs_are_equal(UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Any		):
        if len(UpperCAmelCase		) != len(UpperCAmelCase		):
         return False

        for input_slice_a, input_slice_a in zip(UpperCAmelCase     ,      UpperCAmelCase		):
         if not np.allclose(np.asarray(UpperCAmelCase		)     ,      np.asarray(UpperCAmelCase		)     ,      atol=1e-3		):
          return False
        return True

       lowercase					:   Dict				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Union[str, Any]				  =self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCAmelCase		)
       lowercase					:   List[str]				  =feat_extract.model_input_names[0]

       lowercase					:   Union[str, Any]				  =BatchFeature({input_name: speech_inputs}		)

       # truncate to smallest
       lowercase					:   Union[str, Any]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)     ,      truncation=UpperCAmelCase		)
       lowercase					:   List[str]				  =input_a[input_name]

       lowercase					:   str				  =feat_extract.pad(UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)		)
       lowercase					:   Tuple				  =input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertFalse(_inputs_have_equal_length(UpperCAmelCase		)		)

       # truncate to smallest with np
       lowercase					:   Any				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)     ,      return_tensors='''np'''     ,      truncation=UpperCAmelCase     ,      )
       lowercase					:   Union[str, Any]				  =input_a[input_name]

       lowercase					:   List[Any]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)     ,      return_tensors='''np'''		)
       lowercase					:   int				  =input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(UpperCAmelCase		)		)

       # truncate to middle
       lowercase					:   Optional[Any]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[1]		)     ,      truncation=UpperCAmelCase     ,      return_tensors='''np'''     ,      )
       lowercase					:   Union[str, Any]				  =input_a[input_name]

       lowercase					:   int				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[1]		)     ,      truncation=UpperCAmelCase		)
       lowercase					:   str				  =input_a[input_name]

       lowercase					:   str				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[1]		)     ,      return_tensors='''np'''		)
       lowercase					:   Tuple				  =input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(_inputs_are_equal(UpperCAmelCase     ,      UpperCAmelCase		)		)

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(UpperCAmelCase		):
        feat_extract.pad(UpperCAmelCase     ,      truncation=UpperCAmelCase		)[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(UpperCAmelCase		):
        feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      truncation=UpperCAmelCase		)[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(UpperCAmelCase		):
        feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      truncation=UpperCAmelCase		)[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(UpperCAmelCase		):
        feat_extract.pad(UpperCAmelCase     ,      padding='''max_length'''     ,      truncation=UpperCAmelCase		)[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       lowercase					:   int				  =12
       lowercase					:   List[str]				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)     ,      pad_to_multiple_of=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      )
       lowercase					:   int				  =input_a[input_name]

       lowercase					:   Tuple				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=len(speech_inputs[0]		)     ,      pad_to_multiple_of=UpperCAmelCase     ,      )
       lowercase					:   Dict				  =input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       lowercase					:   Dict				  =len(speech_inputs[0]		)
       if expected_length % pad_to_multiple_of != 0:
        lowercase					:   List[Any]				  =((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]		) == expected_length		)
       self.assertTrue(_inputs_have_equal_length(UpperCAmelCase		)		)
       self.assertFalse(_inputs_have_equal_length(UpperCAmelCase		)		)






      def 				A__      ( self			:		str		)  ->  int:


       '''simple docstring'''

       self._check_padding(numpify=UpperCAmelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       self._check_padding(numpify=UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       self._check_truncation(numpify=UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       self._check_truncation(numpify=UpperCAmelCase		)






      @require_torch
      def 				A__      ( self			:		Any		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   int				  =self.feat_extract_tester.prepare_inputs_for_common()
       lowercase					:   str				  =feat_extract.model_input_names[0]

       lowercase					:   Optional[int]				  =BatchFeature({input_name: speech_inputs}		)

       lowercase					:   Optional[Any]				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''np'''		)[input_name]
       lowercase					:   List[str]				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''pt'''		)[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1e-2		)






      @require_tf
      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.feature_extraction_class(**self.feat_extract_dict		)
       lowercase					:   Tuple				  =self.feat_extract_tester.prepare_inputs_for_common()
       lowercase					:   Optional[Any]				  =feat_extract.model_input_names[0]

       lowercase					:   int				  =BatchFeature({input_name: speech_inputs}		)

       lowercase					:   str				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''np'''		)[input_name]
       lowercase					:   List[str]				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''tf'''		)[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1e-2		)






      def 				A__      ( self			:		str		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   str				  =self.feat_extract_dict
       lowercase					:   Union[str, Any]				  =True
       lowercase					:   List[str]				  =self.feature_extraction_class(**UpperCAmelCase		)
       lowercase					:   Optional[int]				  =self.feat_extract_tester.prepare_inputs_for_common()
       lowercase					:   int				  =[len(UpperCAmelCase		) for x in speech_inputs]
       lowercase					:   Tuple				  =feat_extract.model_input_names[0]

       lowercase					:   Dict				  =BatchFeature({input_name: speech_inputs}		)

       lowercase					:   str				  =feat_extract.pad(UpperCAmelCase     ,      padding='''longest'''     ,      return_tensors='''np'''		)
       self.assertIn('''attention_mask'''     ,      UpperCAmelCase		)
       self.assertListEqual(list(processed.attention_mask.shape		)     ,      list(processed[input_name].shape[:2]		)		)
       self.assertListEqual(processed.attention_mask.sum(-1		).tolist()     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.feat_extract_dict
       lowercase					:   List[str]				  =True
       lowercase					:   Optional[Any]				  =self.feature_extraction_class(**UpperCAmelCase		)
       lowercase					:   Any				  =self.feat_extract_tester.prepare_inputs_for_common()
       lowercase					:   Optional[Any]				  =[len(UpperCAmelCase		) for x in speech_inputs]
       lowercase					:   Tuple				  =feat_extract.model_input_names[0]

       lowercase					:   Optional[Any]				  =BatchFeature({input_name: speech_inputs}		)
       lowercase					:   List[str]				  =min(UpperCAmelCase		)

       lowercase					:   Any				  =feat_extract.pad(
           UpperCAmelCase     ,      padding='''max_length'''     ,      max_length=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      return_tensors='''np'''		)
       self.assertIn('''attention_mask'''     ,      UpperCAmelCase		)
       self.assertListEqual(
           list(processed_pad.attention_mask.shape		)     ,      [processed_pad[input_name].shape[0], max_length]		)
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()     ,      [max_length for x in speech_inputs]		)











'''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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''LayoutLMv2ImageProcessor'''
      UpperCamelCase_								=			('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		Tuple=None     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''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     ,      )
        lowercase					:   Any				  =kwargs.pop('''feature_extractor'''		)

       lowercase					:   Dict				  =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			:		Optional[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			:		Any     ,      )  ->  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.'''		)

       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
       lowercase					:   Tuple				  =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		):
         lowercase					:   Optional[Any]				  =[text]  # add batch dimension (as the image processor always adds a batch dimension)
        lowercase					:   List[str]				  =features['''words''']

       lowercase					:   Optional[Any]				  =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
       lowercase					:   List[str]				  =features.pop('''pixel_values'''		)
       if return_overflowing_tokens is True:
        lowercase					:   str				  =self.get_overflowing_images(UpperCAmelCase     ,      encoded_inputs['''overflow_to_sample_mapping''']		)
       lowercase					:   Dict				  =images

       return encoded_inputs






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =[]
       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 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Dict		)  ->  str:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		int		)  ->  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 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

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



