


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 (							lowercase__):


       """simple docstring"""




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





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			Any=None					,      __lowerCamelCase							:			Dict=None					,      **__lowerCamelCase							:			Tuple       ):


             '''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."					,      __lowerCamelCase					,      )
                   lowerCamelCase__		:				List[Any]    =  kwargs.pop("feature_extractor"       )

             lowerCamelCase__		:				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__(__lowerCamelCase					,      __lowerCamelCase       )





       def __call__(   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,      __lowerCamelCase							:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,      __lowerCamelCase							:			Union[List[List[int]], List[List[List[int]]]] = None					,      __lowerCamelCase							:			Optional[Union[List[int], List[List[int]]]] = None					,      __lowerCamelCase							:			bool = True					,      __lowerCamelCase							:			Union[bool, str, PaddingStrategy] = False					,      __lowerCamelCase							:			Union[bool, str, TruncationStrategy] = None					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			int = 0					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			Optional[bool] = None					,      __lowerCamelCase							:			Optional[bool] = None					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = True					,      __lowerCamelCase							:			Optional[Union[str, TensorType]] = None					,      **__lowerCamelCase							:			int					,      ):


             '''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
             lowerCamelCase__		:				Tuple    =  self.image_processor(images=__lowerCamelCase					,      return_tensors=__lowerCamelCase       )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                         lowerCamelCase__		:				Dict    =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
                   lowerCamelCase__		:				Union[str, Any]    =  features["words"]

             lowerCamelCase__		:				Tuple    =  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=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      max_length=__lowerCamelCase					,      stride=__lowerCamelCase					,      pad_to_multiple_of=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      return_overflowing_tokens=__lowerCamelCase					,      return_special_tokens_mask=__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      return_length=__lowerCamelCase					,      verbose=__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase					,      )

             # add pixel values
             lowerCamelCase__		:				Tuple    =  features.pop("pixel_values"       )
             if return_overflowing_tokens is True:
                   lowerCamelCase__		:				Tuple    =  self.get_overflowing_images(__lowerCamelCase					,      encoded_inputs["overflow_to_sample_mapping"]       )
             lowerCamelCase__		:				Union[str, Any]    =  images

             return encoded_inputs





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  []
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]       )

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

             return images_with_overflow





       def 						lowerCAmelCase     (   self							:			int					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[str]					,      *__lowerCamelCase							:			List[Any]					,      **__lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



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





       @property
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



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





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,      __lowerCamelCase					,      )
             return self.image_processor_class





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



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






import datasets

from .evaluate import evaluate


A      :    Dict		=					"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A      :    Any		=					"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A      :    str		=					"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\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': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\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 CUAD 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\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"







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


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
                     {
                         "predictions": {
                             "id": datasets.Value("string"       ),
                             "prediction_text": datasets.features.Sequence(datasets.Value("string"       )       ),
                         },
                         "references": {
                             "id": datasets.Value("string"       ),
                             "answers": datasets.features.Sequence(
                                 {
                                     "text": datasets.Value("string"       ),
                                     "answer_start": datasets.Value("int32"       ),
                                 }       ),
                         },
                     }       )					,      codebase_urls=["https://www.atticusprojectai.org/cuad"]					,      reference_urls=["https://www.atticusprojectai.org/cuad"]					,      )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
             lowerCamelCase__		:				Any    =  [
                 {
                     "paragraphs": [
                         {
                             "qas": [
                                 {
                                     "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                     "id": ref["id"],
                                 }
                                 for ref in references
                             ]
                         }
                     ]
                 }
             ]
             lowerCamelCase__		:				Optional[int]    =  evaluate(dataset=__lowerCamelCase					,      predictions=__lowerCamelCase       )
             return score


