"""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   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    int			  =						["""image_processor""", """tokenizer"""]
				_lowerCAmelCase     :    Union[str, Any]			  =						"""LayoutLMv2ImageProcessor"""
				_lowerCAmelCase     :    Optional[Any]			  =						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

				def __init__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    **lowerCAmelCase    ):






										"""simple docstring"""

										if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'		,    __UpperCamelCase		,    )
																snake_case								=			kwargs.pop('feature_extractor'    )

										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		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = True		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = 0		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""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.'    )

										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
										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    ):
																						snake_case								=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																snake_case								=			features['words']

										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
										snake_case								=			features.pop('pixel_values'    )
										if return_overflowing_tokens is True:
																snake_case								=			self.get_overflowing_images(__UpperCamelCase		,    encoded_inputs['overflow_to_sample_mapping']    )
										snake_case								=			images

										return encoded_inputs

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										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 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

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

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

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



"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


