
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__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       =["""image_processor""", """tokenizer"""]
	A__    :   Optional[int]				       ="""LayoutLMv3ImageProcessor"""
	A__    :   List[str]				       =("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
	def __init__(			self							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]=None       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'       ,   UpperCAmelCase_       ,   )
											SCREAMING_SNAKE_CASE__													= kwargs.pop('feature_extractor'							)

						SCREAMING_SNAKE_CASE__													= 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__(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def __call__(			self							:   List[str]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,   UpperCAmelCase_							:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,   UpperCAmelCase_							:   Union[List[List[int]], List[List[List[int]]]] = None       ,   UpperCAmelCase_							:   Optional[Union[List[int], List[List[int]]]] = None       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   Union[bool, str, PaddingStrategy] = False       ,   UpperCAmelCase_							:   Union[bool, str, TruncationStrategy] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   int = 0       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   Optional[Union[str, TensorType]] = None       ,   **UpperCAmelCase_							:   Any       ,   ):
						# 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.'							)

						# first, apply the image processor
						SCREAMING_SNAKE_CASE__													= self.image_processor(images=UpperCAmelCase_       ,   return_tensors=UpperCAmelCase_							)

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

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

						# add pixel values
						SCREAMING_SNAKE_CASE__													= features.pop('pixel_values'							)
						if return_overflowing_tokens is True:
											SCREAMING_SNAKE_CASE__													= self.get_overflowing_images(UpperCAmelCase_       ,   encoded_inputs['overflow_to_sample_mapping']							)
						SCREAMING_SNAKE_CASE__													= images

						return encoded_inputs
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]							):
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						SCREAMING_SNAKE_CASE__													= []
						for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]							)

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

						return images_with_overflow
	def      A_				(			self							:   Optional[int]       ,   *UpperCAmelCase_							:   Optional[Any]       ,   **UpperCAmelCase_							:   Dict							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Dict       ,   *UpperCAmelCase_							:   Optional[int]       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	@property
	def      A_				(			self							:   List[Any]							):
						return ["input_ids", "bbox", "attention_mask", "pixel_values"]
	@property
	def      A_				(			self							:   Optional[Any]							):
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,   UpperCAmelCase_       ,   )
						return self.image_processor_class





	@property
	def      A_				(			self							:   List[str]							):
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,   UpperCAmelCase_       ,   )
						return self.image_processor







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__snake_case								=  logging.get_logger(__name__)


__snake_case								=  TypeVar("""DatasetType""", Dataset, IterableDataset)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = "first_exhausted"	,	)		->		DatasetType:
					'''simple docstring'''


					from .arrow_dataset import Dataset
					from .iterable_dataset import IterableDataset

					if not datasets:
										raise ValueError('Unable to interleave an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
										raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'		)
					if dataset_type is Dataset:
										return _interleave_map_style_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)
					else:
										return _interleave_iterable_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = 0	,	)		->		DatasetType:
					'''simple docstring'''


					if not dsets:
										raise ValueError('Unable to concatenate an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if dataset_type is Dataset:
										return _concatenate_map_style_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)
					else:
										return _concatenate_iterable_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)





