






"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_   : Optional[Any]   			= "pt"
elif is_tf_available():
  UpperCAmelCase_   : Tuple   			= "tf"
else:
  UpperCAmelCase_   : Dict   			= "jax"




class 							lowerCAmelCase__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  ByTaTokenizer
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	**lowercase_     :  Optional[int]):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**_SCREAMING_SNAKE_CASE)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[Any]=False						,	lowercase_     :  Dict=20						,	lowercase_     :  Union[str, Any]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]
          for i in range(len(_SCREAMING_SNAKE_CASE)):
              try:
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tokenizer.decode([i]						,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_      :  int    						=		list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''						,	t[1])						,	_SCREAMING_SNAKE_CASE))
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(filter(lambda lowercase_: [t[0]] == tokenizer.encode(t[1]						,	add_special_tokens=_SCREAMING_SNAKE_CASE)						,	_SCREAMING_SNAKE_CASE))
          if max_length is not None and len(_SCREAMING_SNAKE_CASE) > max_length:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		toks[:max_length]
          if min_length is not None and len(_SCREAMING_SNAKE_CASE) < min_length and len(_SCREAMING_SNAKE_CASE) > 0:
              while len(_SCREAMING_SNAKE_CASE) < min_length:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		toks + toks
                    # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_      :  str    						=		[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_      :  int    						=		tokenizer.decode(_SCREAMING_SNAKE_CASE						,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE)
          if " " not in output_txt and len(_SCREAMING_SNAKE_CASE) > 1:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
                  tokenizer.decode([toks_ids[0]]						,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE)
                  + """ """
                  + tokenizer.decode(toks_ids[1:]						,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		""" """ + output_txt
          SCREAMING_SNAKE_CASE_      :  int    						=		tokenizer.encode(_SCREAMING_SNAKE_CASE						,	add_special_tokens=_SCREAMING_SNAKE_CASE)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_      :  Any    						=		tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']						,	batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		"""Unicode €."""
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tokenizer(_SCREAMING_SNAKE_CASE)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']						,	_SCREAMING_SNAKE_CASE)

          # decoding
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		tokenizer.decode(_SCREAMING_SNAKE_CASE)
          self.assertEqual(_SCREAMING_SNAKE_CASE						,	'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_      :  int    						=		[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']						,	_SCREAMING_SNAKE_CASE)
          # decoding
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer.decode(_SCREAMING_SNAKE_CASE)
          self.assertEqual(_SCREAMING_SNAKE_CASE						,	'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))						,	'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  str    						=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          # fmt: off
          SCREAMING_SNAKE_CASE_      :  int    						=		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer(_SCREAMING_SNAKE_CASE						,	padding=_SCREAMING_SNAKE_CASE						,	return_tensors=_SCREAMING_SNAKE_CASE)
          self.assertIsInstance(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_      :  int    						=		list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_      :  str    						=		list(batch.input_ids.tolist()[0])

          self.assertListEqual(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)

          self.assertEqual((2, 37)						,	batch.input_ids.shape)
          self.assertEqual((2, 37)						,	batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer(_SCREAMING_SNAKE_CASE						,	padding=_SCREAMING_SNAKE_CASE						,	return_tensors=_SCREAMING_SNAKE_CASE)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('''input_ids'''						,	_SCREAMING_SNAKE_CASE)
          self.assertIn('''attention_mask'''						,	_SCREAMING_SNAKE_CASE)
          self.assertNotIn('''decoder_input_ids'''						,	_SCREAMING_SNAKE_CASE)
          self.assertNotIn('''decoder_attention_mask'''						,	_SCREAMING_SNAKE_CASE)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[
              """Summary of the text.""",
              """Another summary.""",
          ]
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		tokenizer(
              text_target=_SCREAMING_SNAKE_CASE						,	max_length=32						,	padding='''max_length'''						,	truncation=_SCREAMING_SNAKE_CASE						,	return_tensors=_SCREAMING_SNAKE_CASE)
          self.assertEqual(32						,	targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_      :  str    						=		["""A long paragraph for summarization. </s>"""]
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		["""Summary of the text. </s>"""]
          # fmt: off
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_      :  int    						=		[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_      :  Any    						=		tokenizer(_SCREAMING_SNAKE_CASE						,	text_target=_SCREAMING_SNAKE_CASE)

          self.assertEqual(_SCREAMING_SNAKE_CASE						,	batch['''input_ids'''][0])
          self.assertEqual(_SCREAMING_SNAKE_CASE						,	batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  self.assertNotEqual(tokenizer.model_max_length						,	42)

                    # Now let's start the test
          SCREAMING_SNAKE_CASE_      :  Any    						=		self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		""" He is very happy, UNwant\u00E9d,running"""
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer.encode(_SCREAMING_SNAKE_CASE						,	add_special_tokens=_SCREAMING_SNAKE_CASE)
                  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)

                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		after_tokenizer.encode(_SCREAMING_SNAKE_CASE						,	add_special_tokens=_SCREAMING_SNAKE_CASE)
                  self.assertListEqual(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)

                  shutil.rmtree(_SCREAMING_SNAKE_CASE)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_tokenizers(model_max_length=42)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_      :  Any    						=		tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		""" He is very happy, UNwant\u00E9d,running"""
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer.encode(_SCREAMING_SNAKE_CASE						,	add_special_tokens=_SCREAMING_SNAKE_CASE)
                  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)

                  SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE)
                  SCREAMING_SNAKE_CASE_      :  str    						=		after_tokenizer.encode(_SCREAMING_SNAKE_CASE						,	add_special_tokens=_SCREAMING_SNAKE_CASE)
                  self.assertListEqual(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)
                  self.assertIn('''new_additional_special_token'''						,	after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length						,	42)

                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE						,	model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length						,	43)

                  shutil.rmtree(_SCREAMING_SNAKE_CASE)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(_SCREAMING_SNAKE_CASE)

                  with open(os.path.join(_SCREAMING_SNAKE_CASE						,	'''special_tokens_map.json''')						,	encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_      :  int    						=		json.load(_SCREAMING_SNAKE_CASE)

                  with open(os.path.join(_SCREAMING_SNAKE_CASE						,	'''tokenizer_config.json''')						,	encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_      :  List[Any]    						=		json.load(_SCREAMING_SNAKE_CASE)

                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		added_tokens_extra_ids + [
                      """an_additional_special_token"""
                  ]
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		added_tokens_extra_ids + [
                      """an_additional_special_token"""
                  ]

                  with open(os.path.join(_SCREAMING_SNAKE_CASE						,	'''special_tokens_map.json''')						,	'''w'''						,	encoding='''utf-8''') as outfile:
                      json.dump(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)
                  with open(os.path.join(_SCREAMING_SNAKE_CASE						,	'''tokenizer_config.json''')						,	'''w'''						,	encoding='''utf-8''') as outfile:
                      json.dump(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer_class.from_pretrained(
                      _SCREAMING_SNAKE_CASE						,	)
                  self.assertIn(
                      '''an_additional_special_token'''						,	tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ['''an_additional_special_token''']						,	tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))						,	)

                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''						,	lstrip=_SCREAMING_SNAKE_CASE)]
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer_class.from_pretrained(
                      _SCREAMING_SNAKE_CASE						,	additional_special_tokens=_SCREAMING_SNAKE_CASE						,	)

                  self.assertIn('''a_new_additional_special_token'''						,	tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ['''a_new_additional_special_token''']						,	tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(_SCREAMING_SNAKE_CASE)

                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE						,	do_lower_case=_SCREAMING_SNAKE_CASE)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE)

                  self.assertIsInstance(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)



      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		[
                      """bos_token""",
                      """eos_token""",
                      """unk_token""",
                      """sep_token""",
                      """pad_token""",
                      """cls_token""",
                      """mask_token""",
                  ]

                  SCREAMING_SNAKE_CASE_      :  Dict    						=		0
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tokenizer.convert_ids_to_tokens(
                      _SCREAMING_SNAKE_CASE						,	skip_special_tokens=_SCREAMING_SNAKE_CASE)

                  for attr in attributes_list:
                      setattr(_SCREAMING_SNAKE_CASE						,	attr + '''_id'''						,	_SCREAMING_SNAKE_CASE)
                      self.assertEqual(getattr(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)						,	_SCREAMING_SNAKE_CASE)
                      self.assertEqual(getattr(_SCREAMING_SNAKE_CASE						,	attr + '''_id''')						,	_SCREAMING_SNAKE_CASE)

                      setattr(_SCREAMING_SNAKE_CASE						,	attr + '''_id'''						,	_SCREAMING_SNAKE_CASE)
                      self.assertEqual(getattr(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE)						,	_SCREAMING_SNAKE_CASE)
                      self.assertEqual(getattr(_SCREAMING_SNAKE_CASE						,	attr + '''_id''')						,	_SCREAMING_SNAKE_CASE)

                  setattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens_ids'''						,	[])
                  self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens''')						,	[])
                  self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens_ids''')						,	[])

                  setattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens_ids'''						,	[token_id_to_test_setters])
                  self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens''')						,	[token_to_test_setters])
                  self.assertListEqual(getattr(_SCREAMING_SNAKE_CASE						,	'''additional_special_tokens_ids''')						,	[token_id_to_test_setters])







"""simple docstring"""
import datasets

from .evaluate import evaluate


A:   Optional[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"

A:   Optional[int]								=		"\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:   int								=		"\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       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

   def 	SCREAMING_SNAKE_CASE     (   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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




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