


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   NestedDataStructureLike[PathLike]       ,   __snake_case				:   Optional[NamedSplit] = None       ,   __snake_case				:   Optional[Features] = None       ,   __snake_case				:   str = None       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   Optional[int] = None       ,   **__snake_case				:   str       ,   ):





       '''simple docstring'''
       super().__init__(
           __snake_case       ,   split=__snake_case       ,   features=__snake_case       ,   cache_dir=__snake_case       ,   keep_in_memory=__snake_case       ,   streaming=__snake_case       ,   num_proc=__snake_case       ,   **__snake_case       ,   )
       UpperCAmelCase_							:      Tuple						=   path_or_paths if isinstance(__snake_case       ,   __snake_case	) else {self.split: path_or_paths}
       UpperCAmelCase_							:      Optional[int]						=   Text(
           cache_dir=__snake_case       ,   data_files=__snake_case       ,   features=__snake_case       ,   **__snake_case       ,   )





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       # Build iterable dataset
       if self.streaming:
          UpperCAmelCase_							:      Dict						=   self.builder.as_streaming_dataset(split=self.split	)
       # Build regular (map-style) dataset
       else:
          UpperCAmelCase_							:      Union[str, Any]						=   None
          UpperCAmelCase_							:      Dict						=   None
          UpperCAmelCase_							:      Any						=   None
          UpperCAmelCase_							:      int						=   None

          self.builder.download_and_prepare(
              download_config=__snake_case       ,   download_mode=__snake_case       ,   verification_mode=__snake_case       ,   base_path=__snake_case       ,   num_proc=self.num_proc       ,   )
          UpperCAmelCase_							:      List[str]						=   self.builder.as_dataset(
              split=self.split       ,   verification_mode=__snake_case       ,   in_memory=self.keep_in_memory	)
       return dataset



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'''





    A_  :     str			= ['image_processor', 'tokenizer']
    A_  :     int			= 'LayoutLMv2ImageProcessor'
    A_  :     str			= ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   List[str]=None       ,   **__snake_case				:   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.'''       ,   __snake_case       ,   )
          UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''feature_extractor'''	)

       UpperCAmelCase_							:      Optional[int]						=   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__(__snake_case       ,   __snake_case	)





    def __call__(	self				:   List[str]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,   __snake_case				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,   __snake_case				:   Union[List[List[int]], List[List[List[int]]]] = None       ,   __snake_case				:   Optional[Union[List[int], List[List[int]]]] = None       ,   __snake_case				:   bool = True       ,   __snake_case				:   Union[bool, str, PaddingStrategy] = False       ,   __snake_case				:   Union[bool, str, TruncationStrategy] = None       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   int = 0       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = True       ,   __snake_case				:   Optional[Union[str, TensorType]] = None       ,   **__snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       # 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_							:      Tuple						=   self.image_processor(images=__snake_case       ,   return_tensors=__snake_case	)

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

       UpperCAmelCase_							:      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=__snake_case       ,   add_special_tokens=__snake_case       ,   padding=__snake_case       ,   truncation=__snake_case       ,   max_length=__snake_case       ,   stride=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_token_type_ids=__snake_case       ,   return_attention_mask=__snake_case       ,   return_overflowing_tokens=__snake_case       ,   return_special_tokens_mask=__snake_case       ,   return_offsets_mapping=__snake_case       ,   return_length=__snake_case       ,   verbose=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case       ,   )

       # add pixel values
       UpperCAmelCase_							:      List[str]						=   features.pop('''pixel_values'''	)
       if return_overflowing_tokens is True:
          UpperCAmelCase_							:      Optional[int]						=   self.get_overflowing_images(__snake_case       ,   encoded_inputs['''overflow_to_sample_mapping''']	)
       UpperCAmelCase_							:      List[Any]						=   images

       return encoded_inputs





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_							:      List[str]						=   []
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

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

       return images_with_overflow





    def 					_lowerCamelCase (	self				:   List[Any]       ,   *__snake_case				:   Optional[int]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   *__snake_case				:   Optional[Any]       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





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





       '''simple docstring'''
       return ["input_ids", "bbox", "attention_mask", "image"]





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





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





    @property
    def 					_lowerCamelCase (	self				:   Dict	):





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