





import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class        _a(    _a	,   unittest.TestCase    ):
		lowerCamelCase__     :List[str]   				=		RoCBertTokenizer
		lowerCamelCase__     :List[str]   				=		None
		lowerCamelCase__     :List[str]   				=		False
		lowerCamelCase__     :Optional[int]   				=		True
		lowerCamelCase__     :int   				=		filter_non_english







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							super().setUp()

							_snake_case       :					List[str]				       =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
							_snake_case       :					Union[str, Any]				       =   {}
							_snake_case       :					List[Any]				       =   {}
							for i, value in enumerate(_A				):
												_snake_case       :					str				       =   i
												_snake_case       :					str				       =   i
							_snake_case       :					Optional[Any]				       =   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["vocab_file"]				)
							_snake_case       :					List[str]				       =   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["word_shape_file"]				)
							_snake_case       :					Union[str, Any]				       =   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["word_pronunciation_file"]				)
							with open(self.vocab_file ,						"w" ,						encoding="utf-8"				) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)
							with open(self.word_shape_file ,						"w" ,						encoding="utf-8"				) as word_shape_writer:
												json.dump(_A ,						_A ,						ensure_ascii=_A				)
							with open(self.word_pronunciation_file ,						"w" ,						encoding="utf-8"				) as word_pronunciation_writer:
												json.dump(_A ,						_A ,						ensure_ascii=_A				)







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.tokenizer_class(self.vocab_file ,						self.word_shape_file ,						self.word_pronunciation_file				)

							_snake_case       :					List[str]				       =   tokenizer.tokenize("你好[SEP]你是谁"				)
							self.assertListEqual(_A ,						["你", "好", "[SEP]", "你", "是", "谁"]				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				) ,						[5, 6, 2, 5, 7, 8]				)
							self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_A				) ,						[5, 6, 2, 5, 7, 8]				)
							self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_A				) ,						[5, 6, 2, 5, 7, 8]				)







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   RoCBertBasicTokenizer()

							self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"				) ,						["ah", "\u535A", "\u63A8", "zz"]				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   RoCBertBasicTokenizer(do_lower_case=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "				) ,						["hello", "!", "how", "are", "you", "?"]				)
							self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,						["hello"]				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   RoCBertBasicTokenizer(do_lower_case=_A ,						strip_accents=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,						["hällo", "!", "how", "are", "you", "?"]				)
							self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,						["h\u00E9llo"]				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					int				       =   RoCBertBasicTokenizer(do_lower_case=_A ,						strip_accents=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,						["hallo", "!", "how", "are", "you", "?"]				)
							self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,						["hello"]				)







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   RoCBertBasicTokenizer(do_lower_case=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,						["hallo", "!", "how", "are", "you", "?"]				)
							self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,						["hello"]				)







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   RoCBertBasicTokenizer(do_lower_case=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "				) ,						["HeLLo", "!", "how", "Are", "yoU", "?"]				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   RoCBertBasicTokenizer(do_lower_case=_A ,						strip_accents=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,						["HäLLo", "!", "how", "Are", "yoU", "?"]				)







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   RoCBertBasicTokenizer(do_lower_case=_A ,						strip_accents=_A				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,						["HaLLo", "!", "how", "Are", "yoU", "?"]				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Any				       =   RoCBertBasicTokenizer(do_lower_case=_A ,						never_split=["[UNK]"]				)

							self.assertListEqual(
							    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"				) ,						["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

							_snake_case       :					int				       =   {}
							for i, token in enumerate(_A				):
												_snake_case       :					Dict				       =   i
							_snake_case       :					Any				       =   RoCBertWordpieceTokenizer(vocab=_A ,						unk_token="[UNK]"				)

							self.assertListEqual(tokenizer.tokenize(""				) ,						[]				)

							self.assertListEqual(tokenizer.tokenize("unwanted running"				) ,						["un", "##want", "##ed", "runn", "##ing"]				)

							self.assertListEqual(tokenizer.tokenize("unwantedX running"				) ,						["[UNK]", "runn", "##ing"]				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							self.assertTrue(_is_whitespace(" "				)				)
							self.assertTrue(_is_whitespace("\t"				)				)
							self.assertTrue(_is_whitespace("\r"				)				)
							self.assertTrue(_is_whitespace("\n"				)				)
							self.assertTrue(_is_whitespace("\u00A0"				)				)

							self.assertFalse(_is_whitespace("A"				)				)
							self.assertFalse(_is_whitespace("-"				)				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							self.assertTrue(_is_control("\u0005"				)				)

							self.assertFalse(_is_control("A"				)				)
							self.assertFalse(_is_control(" "				)				)
							self.assertFalse(_is_control("\t"				)				)
							self.assertFalse(_is_control("\r"				)				)







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							self.assertTrue(_is_punctuation("-"				)				)
							self.assertTrue(_is_punctuation("$"				)				)
							self.assertTrue(_is_punctuation("`"				)				)
							self.assertTrue(_is_punctuation("."				)				)

							self.assertFalse(_is_punctuation("A"				)				)
							self.assertFalse(_is_punctuation(" "				)				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.get_tokenizer()

							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
							self.assertListEqual([tokenizer.tokenize(_A				) for t in ["Test", "\xad", "test"]] ,						[["[UNK]"], [], ["[UNK]"]]				)

							if self.test_rust_tokenizer:
												_snake_case       :					List[Any]				       =   self.get_rust_tokenizer()
												self.assertListEqual(
												    [rust_tokenizer.tokenize(_A				) for t in ["Test", "\xad", "test"]] ,						[["[UNK]"], [], ["[UNK]"]]				)







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																	_snake_case       :					List[str]				       =   self.rust_tokenizer_class.from_pretrained(_A ,						**_A				)

																	_snake_case       :					Dict				       =   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																	_snake_case       :					Any				       =   tokenizer_r.encode_plus(
																	    _A ,						return_attention_mask=_A ,						return_token_type_ids=_A ,						return_offsets_mapping=_A ,						add_special_tokens=_A ,						)

																	_snake_case       :					List[str]				       =   tokenizer_r.do_lower_case if hasattr(_A ,						"do_lower_case"				) else False
																	_snake_case       :					Dict				       =   (
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), "A"),
																	        ((1, 2), ","),
																	        ((3, 5), "na"),
																	        ((5, 6), "##ï"),
																	        ((6, 8), "##ve"),
																	        ((9, 1_5), tokenizer_r.mask_token),
																	        ((1_6, 2_1), "Allen"),
																	        ((2_1, 2_3), "##NL"),
																	        ((2_3, 2_4), "##P"),
																	        ((2_5, 3_3), "sentence"),
																	        ((3_3, 3_4), "."),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), "a"),
																	        ((1, 2), ","),
																	        ((3, 8), "naive"),
																	        ((9, 1_5), tokenizer_r.mask_token),
																	        ((1_6, 2_1), "allen"),
																	        ((2_1, 2_3), "##nl"),
																	        ((2_3, 2_4), "##p"),
																	        ((2_5, 3_3), "sentence"),
																	        ((3_3, 3_4), "."),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results] ,						tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]				)				)
																	self.assertEqual([e[0] for e in expected_results] ,						tokens["offset_mapping"]				)







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   ["的", "人", "有"]
							_snake_case       :					str				       =   "".join(_A				)
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																	_snake_case       :					int				       =   True
																	_snake_case       :					str				       =   self.tokenizer_class.from_pretrained(_A ,						**_A				)
																	_snake_case       :					List[str]				       =   self.rust_tokenizer_class.from_pretrained(_A ,						**_A				)

																	_snake_case       :					int				       =   tokenizer_p.encode(_A ,						add_special_tokens=_A				)
																	_snake_case       :					Optional[Any]				       =   tokenizer_r.encode(_A ,						add_special_tokens=_A				)

																	_snake_case       :					Optional[int]				       =   tokenizer_r.convert_ids_to_tokens(_A				)
																	_snake_case       :					Optional[Any]				       =   tokenizer_p.convert_ids_to_tokens(_A				)

																	# it is expected that each Chinese character is not preceded by "##"
																	self.assertListEqual(_A ,						_A				)
																	self.assertListEqual(_A ,						_A				)

																	_snake_case       :					Union[str, Any]				       =   False
																	_snake_case       :					int				       =   self.rust_tokenizer_class.from_pretrained(_A ,						**_A				)
																	_snake_case       :					Any				       =   self.tokenizer_class.from_pretrained(_A ,						**_A				)

																	_snake_case       :					Optional[Any]				       =   tokenizer_r.encode(_A ,						add_special_tokens=_A				)
																	_snake_case       :					str				       =   tokenizer_p.encode(_A ,						add_special_tokens=_A				)

																	_snake_case       :					Tuple				       =   tokenizer_r.convert_ids_to_tokens(_A				)
																	_snake_case       :					Dict				       =   tokenizer_p.convert_ids_to_tokens(_A				)

																	# it is expected that only the first Chinese character is not preceded by "##".
																	_snake_case       :					str				       =   [
																	    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(_A				)
																	]
																	self.assertListEqual(_A ,						_A				)
																	self.assertListEqual(_A ,						_A				)







		@slow
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.tokenizer_class(self.vocab_file ,						self.word_shape_file ,						self.word_pronunciation_file				)

							_snake_case       :					str				       =   tokenizer.encode("你好" ,						add_special_tokens=_A				)
							_snake_case       :					List[str]				       =   tokenizer.encode("你是谁" ,						add_special_tokens=_A				)

							_snake_case       :					int				       =   tokenizer.build_inputs_with_special_tokens(_A				)
							_snake_case       :					Optional[Any]				       =   tokenizer.build_inputs_with_special_tokens(_A ,						_A				)

							assert encoded_sentence == [1] + text + [2]
							assert encoded_pair == [1] + text + [2] + text_a + [2]







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.get_tokenizers(do_lower_case=_A				)
							for tokenizer in tokenizers:
												with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
																	_snake_case       :					Optional[int]				       =   "你好，你是谁"
																	_snake_case       :					Any				       =   tokenizer.tokenize(_A				)
																	_snake_case       :					Tuple				       =   tokenizer.convert_tokens_to_ids(_A				)
																	_snake_case       :					Optional[Any]				       =   tokenizer.convert_tokens_to_shape_ids(_A				)
																	_snake_case       :					Optional[Any]				       =   tokenizer.convert_tokens_to_pronunciation_ids(_A				)
																	_snake_case       :					Dict				       =   tokenizer.prepare_for_model(
																	    _A ,						_A ,						_A ,						add_special_tokens=_A				)

																	_snake_case       :					List[str]				       =   tokenizer.encode_plus(_A ,						add_special_tokens=_A				)

																	self.assertEqual(_A ,						_A				)










import datasets

from .evaluate import evaluate


__lowerCAmelCase :Any     =    '\\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'

__lowerCAmelCase :Union[str, 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'

__lowerCAmelCase :Optional[Any]     =    '\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        _a(    datasets.Metric    ):







		def        lowercase       (    self				)      ->      Optional[int]:


							'''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        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							_snake_case       :					Union[str, Any]				       =   [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							_snake_case       :					Union[str, Any]				       =   evaluate(dataset=__snake_case ,						predictions=__snake_case				)
							return score