



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 	_a			(      snake_case_  ):
			_UpperCamelCase:   Any	     =		["image_processor", "tokenizer"]
			_UpperCamelCase:   Any	     =		"LayoutLMv2ImageProcessor"
			_UpperCamelCase:   List[str]	     =		("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






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

									lowerCAmelCase	:   str					     =					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__(lowercase_    ,  lowercase_ )






			def __call__(    self    ,  lowercase_    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = True    ,  lowercase_ = False    ,  lowercase_ = False    ,  lowercase_ = None    ,  lowercase_ = 0    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = False    ,  lowercase_ = False    ,  lowercase_ = False    ,  lowercase_ = False    ,  lowercase_ = True    ,  lowercase_ = None    ,  **lowercase_    ,  )	->					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	:   Any					     =					self.image_processor(images=lowercase_    ,  return_tensors=lowercase_ )

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

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

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

									return encoded_inputs






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

									return images_with_overflow






			def 							_snake_case   (    self    ,  *lowercase_    ,  **lowercase_ )	->					Tuple:
									return self.tokenizer.batch_decode(*lowercase_    ,  **lowercase_ )






			def 							_snake_case   (    self    ,  *lowercase_    ,  **lowercase_ )	->					Any:
									return self.tokenizer.decode(*lowercase_    ,  **lowercase_ )






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






			@property
			def 							_snake_case   (    self )	->					List[Any]:
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,  lowercase_    ,  )
									return self.image_processor_class






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






import os

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

from .logging import get_logger


lowerCAmelCase					:				Union[str, Any]   	=2 << 20  # 2mb
lowerCAmelCase					:				Union[str, Any]   	=get_logger(__name__)






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					self._options.get_all_options().get("""runner""" )
									return runner in [None, "DirectRunner", "PortableRunner"]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Union[str, Any]					     =					FileSystems
						if fs.exists(SCREAMING_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(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""rb"""  ) as local_file:
																		lowerCAmelCase	:   Any					     =					local_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								remote_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   List[str]					     =					local_file.read(SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					FileSystems
						if os.path.exists(SCREAMING_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(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""wb"""  ) as local_file:
																		lowerCAmelCase	:   Optional[int]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								local_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   Union[str, Any]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )

