



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      UpperCamelCase    (						__UpperCamelCase     ):
	lowerCAmelCase	:   str				=	["""image_processor""", """tokenizer"""]
	lowerCAmelCase	:   Optional[Any]				=	"""LayoutLMv3ImageProcessor"""
	lowerCAmelCase	:   Union[str, Any]				=	("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



	def __init__(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__		):
							A__							=       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__			,	)
													A__							=       kwargs.pop("feature_extractor"		)

							A__							=       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			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = 0			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							# 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
							A__							=       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__		):
																			A__							=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
													A__							=       features['words']

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

							return encoded_inputs



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							A__							=       []
							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			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.batch_decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	@property
	def 			__A							(       self		):
							return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 			__A							(       self		):
							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		):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,	UpperCAmelCase__			,	)
							return self.image_processor










import datasets

from .evaluate import evaluate


UpperCAmelCase_							:      List[Any]      =  "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCAmelCase_							:      Any      =  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

UpperCAmelCase_							:      Tuple      =  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION     )
class      UpperCamelCase    (						datasets.Metric     ):



	def 			__A							(       self		):
							return datasets.MetricInfo(
							    description=_DESCRIPTION			,	citation=_CITATION			,	inputs_description=_KWARGS_DESCRIPTION			,	features=datasets.Features(
							        {
							            "predictions": {"id": datasets.Value("string"		), "prediction_text": datasets.Value("string"		)},
							            "references": {
							                "id": datasets.Value("string"		),
							                "answers": datasets.features.Sequence(
							                    {
							                        "text": datasets.Value("string"		),
							                        "answer_start": datasets.Value("int32"		),
							                    }		),
							            },
							        }		)			,	codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,	reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							A__							=       [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							A__							=       evaluate(dataset=UpperCAmelCase__			,	predictions=UpperCAmelCase__		)
							return score





