


"""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   lowercase__							(				A_							):
   __UpperCAmelCase       					=				['''image_processor''', '''tokenizer''']
   __UpperCAmelCase       					=				'''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase       					=				('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')





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

      _lowerCamelCase  :				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__(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)





   def __call__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = 0			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = None			,      **SCREAMING_SNAKE_CASE			,      )     ->     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=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE)

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

      _lowerCamelCase  :				Union[str, 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=SCREAMING_SNAKE_CASE			,      add_special_tokens=SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      truncation=SCREAMING_SNAKE_CASE			,      max_length=SCREAMING_SNAKE_CASE			,      stride=SCREAMING_SNAKE_CASE			,      pad_to_multiple_of=SCREAMING_SNAKE_CASE			,      return_token_type_ids=SCREAMING_SNAKE_CASE			,      return_attention_mask=SCREAMING_SNAKE_CASE			,      return_overflowing_tokens=SCREAMING_SNAKE_CASE			,      return_special_tokens_mask=SCREAMING_SNAKE_CASE			,      return_offsets_mapping=SCREAMING_SNAKE_CASE			,      return_length=SCREAMING_SNAKE_CASE			,      verbose=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )

      # add pixel values
      _lowerCamelCase  :				List[Any]      =		features.pop("""pixel_values""")
      if return_overflowing_tokens is True:
         _lowerCamelCase  :				List[Any]      =		self.get_overflowing_images(SCREAMING_SNAKE_CASE			,      encoded_inputs["""overflow_to_sample_mapping"""])
      _lowerCamelCase  :				Dict      =		images

      return encoded_inputs





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     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(SCREAMING_SNAKE_CASE) != len(SCREAMING_SNAKE_CASE):
         raise ValueError(
             """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
             F' {len(SCREAMING_SNAKE_CASE)} and {len(SCREAMING_SNAKE_CASE)}')

      return images_with_overflow





   def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     Dict:
      return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     int:
      return self.tokenizer.decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_     (       self)     ->     Dict:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]





   @property
   def 		UpperCamelCase_     (       self)     ->     Optional[int]:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,      SCREAMING_SNAKE_CASE			,      )
      return self.image_processor_class





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





"""simple docstring"""
import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase   =	2 << 20  # 2mb
UpperCAmelCase   =	get_logger(__name__)




class   lowercase__							(				A_							):





   def 		UpperCamelCase_     (       self)     ->     Dict:
      _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
      return runner in [None, "DirectRunner", "PortableRunner"]







def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):




   """simple docstring"""







   _lowerCamelCase  :				Any      =		FileSystems
   if fs.exists(__snake_case      ):
      if force_upload:
         logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
      else:
         logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
         return
   with fs.create(__snake_case      ) as remote_file:
      with open(__snake_case	,   """rb"""      ) as local_file:
         _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
         while chunk:
            remote_file.write(__snake_case      )
            _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )





def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):




   """simple docstring"""







   _lowerCamelCase  :				int      =		FileSystems
   if os.path.exists(__snake_case      ):
      if force_download:
         logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
      else:
         logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
         return
   with fs.open(__snake_case      ) as remote_file:
      with open(__snake_case	,   """wb"""      ) as local_file:
         _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
         while chunk:
            local_file.write(__snake_case      )
            _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )

