


"""simple docstring"""





import datasets

from .evaluate import evaluate


snake_case__						:   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'''

snake_case__						:   str			 = '''\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'''

snake_case__						:   Dict			 = '''\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 						snake_case_(				datasets.Metric	):






     def    lowerCamelCase__   (		self     :    Tuple ):
           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     :    List[Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
           lowerCAmelCase    :		Optional[int]				=      [
               {
                   "paragraphs": [
                       {
                           "qas": [
                               {
                                   "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                   "id": ref["id"],
                               }
                               for ref in references
                           ]
                       }
                   ]
               }
           ]
           lowerCAmelCase    :		List[Any]				=      evaluate(dataset=UpperCamelCase__    , predictions=UpperCamelCase__ )
           return score





"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A						   =			logging.get_logger(__name__)

__A						   =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__A						   =			{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  RobertaTokenizer

				def __init__(	self							:				Optional[int]     ,  UpperCamelCase__							:				int=None     ,  UpperCamelCase__							:				Union[str, Any]=None     ,  UpperCamelCase__							:				Optional[Any]=None     ,  UpperCamelCase__							:				int="replace"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				List[Any]="</s>"     ,  UpperCamelCase__							:				Any="</s>"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				str="<unk>"     ,  UpperCamelCase__							:				Optional[int]="<pad>"     ,  UpperCamelCase__							:				int="<mask>"     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=True     ,  **UpperCamelCase__							:				Tuple     ,  )->   Optional[int]:



						'''simple docstring'''


						super().__init__(
						    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  errors=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Tuple          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       Optional[int]          =			add_prefix_space
								__lowerCAmelCase:       Dict          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       Any          =			add_prefix_space

						__lowerCAmelCase:       int          =			"post_processor"
						__lowerCAmelCase:       Optional[Any]          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       Dict          =			json.loads(tokenizer_component_instance.__getstate__())

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
										__lowerCAmelCase:       List[Any]          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       str          =			tuple(state["cls"])

								__lowerCAmelCase:       str          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Optional[Any]          =			add_prefix_space
										__lowerCAmelCase:       List[str]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Any          =			trim_offsets
										__lowerCAmelCase:       List[str]          =			True

								if changes_to_apply:
										__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       List[str]          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

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



						'''simple docstring'''


						if self._mask_token is None:
								if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
								return None
						return str(self._mask_token)

				@mask_token.setter
				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       int          =			value

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				str     ,  **UpperCamelCase__							:				List[Any])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				int     ,  *UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				List[str])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Union[str, Any]=None)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
								return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			[self.sep_token_id]
						__lowerCAmelCase:       str          =			[self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


