





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer



class 							_UpperCAmelCase       :

  """simple docstring"""


  def 							lowercase						(							self					:						Dict   ,    lowerCAmelCase_					:						Tuple	)				->		str:
     raise NotImplementedError()






  def 							lowercase						(							self					:						Optional[int]	)				->		Any:
     raise NotImplementedError()





class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  def __init__(							self					:						List[str]   ,    lowerCAmelCase_					:						"AutoTokenizer"   ,    lowerCAmelCase_					:						bool = False   ,    **lowerCAmelCase_					:						Tuple	)				->		List[str]:
     __lowerCAmelCase				      =      tokenizer
     __lowerCAmelCase				      =      skip_prompt
     __lowerCAmelCase				      =      decode_kwargs

     # variables used in the streaming process
     __lowerCAmelCase				      =      []
     __lowerCAmelCase				      =      0
     __lowerCAmelCase				      =      True






  def 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						Any	)				->		List[str]:
     if len(value.shape	) > 1 and value.shape[0] > 1:
        raise ValueError('TextStreamer only supports batch size 1'	)
     elif len(value.shape	) > 1:
        __lowerCAmelCase				      =      value[0]

     if self.skip_prompt and self.next_tokens_are_prompt:
        __lowerCAmelCase				      =      False
        return

     # Add the new token to the cache and decodes the entire thing.
     self.token_cache.extend(value.tolist()	)
     __lowerCAmelCase				      =      self.tokenizer.decode(self.token_cache   ,    **self.decode_kwargs	)

     # After the symbol for a new line, we flush the cache.
     if text.endswith('\n'	):
        __lowerCAmelCase				      =      text[self.print_len :]
        __lowerCAmelCase				      =      []
        __lowerCAmelCase				      =      0
     # If the last token is a CJK character, we print the characters.
     elif len(lowerCAmelCase_	) > 0 and self._is_chinese_char(ord(text[-1]	)	):
        __lowerCAmelCase				      =      text[self.print_len :]
        self.print_len += len(lowerCAmelCase_	)
     # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
     # which may change with the subsequent token -- there are probably smarter ways to do this!)
     else:
        __lowerCAmelCase				      =      text[self.print_len : text.rfind(' '	) + 1]
        self.print_len += len(lowerCAmelCase_	)

     self.on_finalized_text(lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[int]	)				->		Optional[int]:
     # Flush the cache, if it exists
     if len(self.token_cache	) > 0:
        __lowerCAmelCase				      =      self.tokenizer.decode(self.token_cache   ,    **self.decode_kwargs	)
        __lowerCAmelCase				      =      text[self.print_len :]
        __lowerCAmelCase				      =      []
        __lowerCAmelCase				      =      0
     else:
        __lowerCAmelCase				      =      ''

     __lowerCAmelCase				      =      True
     self.on_finalized_text(lowerCAmelCase_   ,    stream_end=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						bool = False	)				->		List[str]:
     print(lowerCAmelCase_   ,    flush=lowerCAmelCase_   ,    end='' if not stream_end else None	)






  def 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						List[Any]	)				->		List[str]:
     # This defines a "chinese character" as anything in the CJK Unicode block:
     #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
     #
     # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
     # despite its name. The modern Korean Hangul alphabet is a different block,
     # as is Japanese Hiragana and Katakana. Those alphabets are used to write
     # space-separated words, so they are not treated specially and handled
     # like the all of the other languages.
     if (
         (cp >= 0X4e_00 and cp <= 0X9f_ff)
         or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
         or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
         or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
         or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
         or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
         or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
         or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
     ):  #
        return True

     return False





class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  def __init__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						"AutoTokenizer"   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						Optional[float] = None   ,    **lowerCAmelCase_					:						Optional[Any]	)				->		Optional[Any]:
     super().__init__(lowerCAmelCase_   ,    lowerCAmelCase_   ,    **lowerCAmelCase_	)
     __lowerCAmelCase				      =      Queue()
     __lowerCAmelCase				      =      None
     __lowerCAmelCase				      =      timeout






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						bool = False	)				->		Tuple:
     self.text_queue.put(lowerCAmelCase_   ,    timeout=self.timeout	)
     if stream_end:
        self.text_queue.put(self.stop_signal   ,    timeout=self.timeout	)






  def __iter__(							self					:						Tuple	)				->		str:
     return self






  def 							lowercase						(							self					:						Any	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.text_queue.get(timeout=self.timeout	)
     if value == self.stop_signal:
        raise StopIteration()
     else:
        return value





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   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["image_processor", "tokenizer"]
       _a   							=							"LayoutLMv2ImageProcessor"
       _a   							=							("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



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

          lowerCamelCase_						:   List[Any]		= 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				:				List[str]		,				__a				:				List[Any]		,				__a				:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				__a				:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				__a				:				Union[List[List[int]], List[List[List[int]]]] = None		,				__a				:				Optional[Union[List[int], List[List[int]]]] = None		,				__a				:				bool = True		,				__a				:				Union[bool, str, PaddingStrategy] = False		,				__a				:				Union[bool, str, TruncationStrategy] = None		,				__a				:				Optional[int] = None		,				__a				:				int = 0		,				__a				:				Optional[int] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = True		,				__a				:				Optional[Union[str, TensorType]] = None		,				**__a				:				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
          lowerCamelCase_						:   Tuple		= 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							):
                lowerCamelCase_						:   Optional[Any]		= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowerCamelCase_						:   Dict		= features["""words"""]

          lowerCamelCase_						:   int		= 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
          lowerCamelCase_						:   Any		= features.pop("""pixel_values"""							)
          if return_overflowing_tokens is True:
             lowerCamelCase_						:   Dict		= self.get_overflowing_images(__a		,				encoded_inputs["""overflow_to_sample_mapping"""]							)
          lowerCamelCase_						:   Optional[int]		= images

          return encoded_inputs



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



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				*__a				:				List[str]		,				**__a				:				int							)					->List[Any]:
          return self.tokenizer.decode(*__a		,				**__a							)



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



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





