


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    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'LayoutLMv3ImageProcessor'
				lowerCAmelCase 		= ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Tuple:
					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."""				,   a__				,   )
						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__(a__				,   a__   )

				def __call__(						self				,   a__				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					# 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=a__				,   return_tensors=a__   )

					# second, apply the tokenizer
					if text is not None and self.image_processor.apply_ocr and text_pair is None:
						if isinstance(a__				,   a__   ):
							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=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )

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

					return encoded_inputs

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Union[str, Any]:
					# 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(a__   ) != len(a__   ):
						raise ValueError(
						    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
						    f' {len(a__   )} and {len(a__   )}'   )

					return images_with_overflow

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				List[str]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				List[Any]:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return ["input_ids", "bbox", "attention_mask", "pixel_values"]

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor







from sklearn.metrics import recall_score

import datasets


_lowercase       :      Any	=	"\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


_lowercase       :      int	=	"\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


_lowercase       :      Dict	=	"\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






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

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Tuple:
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
					        {
					            """predictions""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					            """references""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					        }
					        if self.config_name == """multilabel"""
					        else {
					            """predictions""": datasets.Value("""int32"""   ),
					            """references""": datasets.Value("""int32"""   ),
					        }   )				,   reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]				,   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__=None				,   a__=1				,   a__="binary"				,   a__=None				,   a__="warn"				,   )	->				Any:
					A								=				recall_score(
					    a__				,   a__				,   labels=a__				,   pos_label=a__				,   average=a__				,   sample_weight=a__				,   zero_division=a__				,   )
					return {"recall": float(a__   ) if score.size == 1 else score}



