






'''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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							str					     =					["image_processor", "tokenizer"]
			__a							:							Optional[int]					     =					"LayoutLMv3ImageProcessor"
			__a							:							Union[str, Any]					     =					("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






			def __init__(   self							:						Union[str, Any] ,      lowercase							:						Optional[int]=None ,      lowercase							:						List[str]=None ,      **lowercase							:						Optional[Any]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = 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 ,      )
															UpperCamelCase__         = kwargs.pop("""feature_extractor"""			)

									UpperCamelCase__         = 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							:						Dict ,      lowercase							:						int ,      lowercase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,      lowercase							:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,      lowercase							:						Union[List[List[int]], List[List[List[int]]]] = None ,      lowercase							:						Optional[Union[List[int], List[List[int]]]] = None ,      lowercase							:						bool = True ,      lowercase							:						Union[bool, str, PaddingStrategy] = False ,      lowercase							:						Union[bool, str, TruncationStrategy] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						int = 0 ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						bool = False ,      lowercase							:						bool = False ,      lowercase							:						bool = False ,      lowercase							:						bool = False ,      lowercase							:						bool = True ,      lowercase							:						Optional[Union[str, TensorType]] = None ,      **lowercase							:						int ,      )							->    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."""			)

									# first, apply the image processor
									UpperCamelCase__         = 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			):
																					UpperCamelCase__         = [text]  # add batch dimension (as the image processor always adds a batch dimension)
															UpperCamelCase__         = features["""words"""]

									UpperCamelCase__         = 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
									UpperCamelCase__         = features.pop("""pixel_values"""			)
									if return_overflowing_tokens is True:
															UpperCamelCase__         = self.get_overflowing_images(lowercase ,      encoded_inputs["""overflow_to_sample_mapping"""]			)
									UpperCamelCase__         = images

									return encoded_inputs






			def 				A      (   self							:						int ,      lowercase							:						Any ,      lowercase							:						Optional[Any]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = []
									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 				A      (   self							:						Union[str, Any] ,      *lowercase							:						int ,      **lowercase							:						List[str]			)							->    Any:

									'''simple docstring'''







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






			def 				A      (   self							:						List[Any] ,      *lowercase							:						List[str] ,      **lowercase							:						Tuple			)							->    Union[str, Any]:

									'''simple docstring'''







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






			@property
			def 				A      (   self							:						int			)							->    List[Any]:

									'''simple docstring'''







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






			@property
			def 				A      (   self							:						Union[str, Any]			)							->    Dict:

									'''simple docstring'''







									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 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







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








'''simple docstring'''


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase_				: Any    	=							{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase_				: Optional[int]    	=							logging.WARNING







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = os.getenv("""DATASETS_VERBOSITY"""						,      _A					)
						if env_level_str:
												if env_level_str in log_levels:
																		return log_levels[env_level_str]
												else:
																		logging.getLogger().warning(
																		    f"Unknown option DATASETS_VERBOSITY={env_level_str}, "
																		    f"has to be one of: { ', '.join(log_levels.keys()					) }"					)
						return _default_log_level







def 	__magic_name__( ):
						'''simple docstring'''





						return __name__.split("""."""					)[0]







def 	__magic_name__( ):
						'''simple docstring'''





						return logging.getLogger(_get_library_name()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(_get_default_logging_level()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(logging.NOTSET					)







def 	__magic_name__( _A = None					):
						'''simple docstring'''





						if name is None:
												UpperCamelCase__         = _get_library_name()
						return logging.getLogger(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return _get_library_root_logger().getEffectiveLevel()







def 	__magic_name__( _A					):
						'''simple docstring'''





						_get_library_root_logger().setLevel(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = False







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						int ,      *lowercase							:						Any ,      **lowercase							:						int			)							->    List[Any]:  # pylint: disable=unused-argument

									'''simple docstring'''







									UpperCamelCase__         = args[0] if args else None






			def __iter__(   self							:						str			)							->    List[str]:

									'''simple docstring'''







									return iter(self._iterator			)






			def __getattr__(   self							:						Optional[int] ,      lowercase							:						Tuple			)							->    int:

									'''simple docstring'''







									def empty_fn(*lowercase							:						List[Any] ,      **lowercase							:						Dict			):  # pylint: disable=unused-argument
															return

									return empty_fn






			def __enter__(   self							:						Dict			)							->    Tuple:

									'''simple docstring'''







									return self







			def __exit__(   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						int			)							->    Any:

									'''simple docstring'''







									return
lowerCamelCase_				: str    	=							True
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __call__(   self							:						List[Any] ,      *lowercase							:						str ,      lowercase							:						Optional[int]=False ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									if _tqdm_active and not disable:
															return tqdm_lib.tqdm(*lowercase ,      **lowercase			)
									else:
															return EmptyTqdm(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[Any] ,      *lowercase							:						Tuple ,      **lowercase							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = None
									if _tqdm_active:
															return tqdm_lib.tqdm.set_lock(*lowercase ,      **lowercase			)







			def 				A      (   self							:						Optional[Any]			)							->    Optional[Any]:

									'''simple docstring'''







									if _tqdm_active:
															return tqdm_lib.tqdm.get_lock()
lowerCamelCase_				: int    	=							_tqdm_cls()







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						return bool(_tqdm_active					)







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = True







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = False
