
'''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 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""image_processor""", """tokenizer"""]
		lowerCamelCase     							=						"""LayoutLMv2ImageProcessor"""
		lowerCamelCase     							=						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



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






				"""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  :       str     			=						kwargs.pop('''feature_extractor'''      )

				snake_case  :       Union[str, 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__(UpperCamelCase__  ,	UpperCamelCase__      )



		def __call__(      self				:			Any  ,	UpperCamelCase__				:			Union[str, Any]  ,	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__				:			Dict  ,	)						->			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.'''      )

				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  :       List[str]     			=						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  :       Union[str, Any]     			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
						snake_case  :       Tuple     			=						features['''words''']

				snake_case  :       List[str]     			=						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  :       Optional[int]     			=						features.pop('''pixel_values'''      )
				if return_overflowing_tokens is True:
						snake_case  :       int     			=						self.get_overflowing_images(UpperCamelCase__  ,	encoded_inputs['''overflow_to_sample_mapping''']      )
				snake_case  :       Any     			=						images

				return encoded_inputs



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Any      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						[]
				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       lowerCAmelCase				(      self				:			List[Any]  ,	*UpperCamelCase__				:			int  ,	**UpperCamelCase__				:			Optional[int]      )						->			Optional[int]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Dict  ,	*UpperCamelCase__				:			List[str]  ,	**UpperCamelCase__				:			List[Any]      )						->			Dict:






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Tuple:






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			str:






				"""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       lowerCAmelCase				(      self				:			List[str]      )						->			Any:






				"""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 .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)


