





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
       from test_module.custom_tokenization_fast import CustomTokenizerFast

class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						mock.Mock()
            _UpperCamelCase              =						500
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						HTTPError
            _UpperCamelCase              =						{}

            # Download this model to make sure it's in the cache.
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

            # Under the mock environment we get a 500 error when trying to reach the tokenizer.
            with mock.patch('''requests.Session.request''',       return_value=A      ) as mock_head:
                   _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
                   # This check we did call the fake head request
                   mock_head.assert_called()

     @require_tokenizers
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						mock.Mock()
            _UpperCamelCase              =						500
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						HTTPError
            _UpperCamelCase              =						{}

            # Download this model to make sure it's in the cache.
            _UpperCamelCase              =						GPTaTokenizerFast.from_pretrained('''gpt2'''      )

            # Under the mock environment we get a 500 error when trying to reach the tokenizer.
            with mock.patch('''requests.Session.request''',       return_value=A      ) as mock_head:
                   _UpperCamelCase              =						GPTaTokenizerFast.from_pretrained('''gpt2'''      )
                   # This check we did call the fake head request
                   mock_head.assert_called()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            try:
                   _UpperCamelCase              =						tempfile.mktemp()
                   with open(A,       '''wb'''      ) as f:
                          http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',       A      )

                   _UpperCamelCase              =						AlbertTokenizer.from_pretrained(A      )
            finally:
                   os.remove(A      )

            # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
            # the current folder and have the right name.
            if os.path.isfile('''tokenizer.json'''      ):
                   # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
                   return
            try:
                   with open('''tokenizer.json''',       '''wb'''      ) as f:
                          http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json''',       A      )
                   _UpperCamelCase              =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
                   # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
                   self.assertEqual(tokenizer.vocab_size,       1000      )
                   # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

            finally:
                   os.remove('''tokenizer.json'''      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''      )






@is_staging_test
class  __A(						unittest.TestCase ):
     __A	  =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

     @classmethod
     def 	_UpperCamelCase     (      cls      ):





            """simple docstring"""

            _UpperCamelCase              =						TOKEN
            HfFolder.save_token(A      )

     @classmethod
     def 	_UpperCamelCase     (      cls      ):





            """simple docstring"""

            try:
                   delete_repo(token=cls._token,       repo_id='''test-tokenizer'''      )
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token,       repo_id='''valid_org/test-tokenizer-org'''      )
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token,       repo_id='''test-dynamic-tokenizer'''      )
            except HTTPError:
                   pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						BertTokenizer(A      )

            tokenizer.push_to_hub('''test-tokenizer''',       use_auth_token=self._token      )
            _UpperCamelCase              =						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

            # Reset repo
            delete_repo(token=self._token,       repo_id='''test-tokenizer'''      )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(A,       repo_id='''test-tokenizer''',       push_to_hub=A,       use_auth_token=self._token      )

            _UpperCamelCase              =						BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						BertTokenizer(A      )

            tokenizer.push_to_hub('''valid_org/test-tokenizer-org''',       use_auth_token=self._token      )
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )

            # Reset repo
            delete_repo(token=self._token,       repo_id='''valid_org/test-tokenizer-org'''      )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(
                       A,       repo_id='''valid_org/test-tokenizer-org''',       push_to_hub=A,       use_auth_token=self._token      )

            _UpperCamelCase              =						BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
            self.assertDictEqual(new_tokenizer.vocab,       tokenizer.vocab      )



     @require_tokenizers
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            CustomTokenizer.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   _UpperCamelCase              =						CustomTokenizer(A      )

            # No fast custom tokenizer
            tokenizer.push_to_hub('''test-dynamic-tokenizer''',       use_auth_token=self._token      )

            _UpperCamelCase              =						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''',       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizer'''      )

            # Fast and slow custom tokenizer
            CustomTokenizerFast.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _UpperCamelCase              =						os.path.join(A,       '''vocab.txt'''      )
                   with open(A,       '''w''',       encoding='''utf-8'''      ) as vocab_writer:
                          vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )

                   _UpperCamelCase              =						BertTokenizerFast.from_pretrained(A      )
                   bert_tokenizer.save_pretrained(A      )
                   _UpperCamelCase              =						CustomTokenizerFast.from_pretrained(A      )

            tokenizer.push_to_hub('''test-dynamic-tokenizer''',       use_auth_token=self._token      )

            _UpperCamelCase              =						AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''',       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizerFast'''      )
            _UpperCamelCase              =						AutoTokenizer.from_pretrained(
                F'''{USER}/test-dynamic-tokenizer''',       use_fast=A,       trust_remote_code=A      )
            # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
            self.assertEqual(tokenizer.__class__.__name__,       '''CustomTokenizer'''      )






class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''Hello 友達'''      )
            self.assertEqual(trie.data,       {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )
            trie.add('''Hello'''      )
            trie.data
            self.assertEqual(trie.data,       {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      ),       ['''[CLS] This is a extra_id_100''']      )
            trie.add('''[CLS]'''      )
            trie.add('''extra_id_1'''      )
            trie.add('''extra_id_100'''      )
            self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      ),       ['''[CLS]''', ''' This is a ''', '''extra_id_100''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''A'''      )
            self.assertEqual(trie.split('''ABC'''      ),       ['''A''', '''BC''']      )
            self.assertEqual(trie.split('''BCA'''      ),       ['''BC''', '''A''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''TOKEN]'''      )
            trie.add('''[SPECIAL_TOKEN]'''      )
            self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      ),       ['''This is something ''', '''[SPECIAL_TOKEN]''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''A'''      )
            trie.add('''P'''      )
            trie.add('''[SPECIAL_TOKEN]'''      )
            self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      ),       ['''This is something ''', '''[SPECIAL_TOKEN]''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''AB'''      )
            trie.add('''B'''      )
            trie.add('''C'''      )
            self.assertEqual(trie.split('''ABC'''      ),       ['''AB''', '''C''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            trie.add('''ABC'''      )
            trie.add('''B'''      )
            trie.add('''CD'''      )
            self.assertEqual(trie.split('''ABCD'''      ),       ['''ABC''', '''D''']      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Trie()
            _UpperCamelCase              =						trie.cut_text('''ABC''',       [0, 0, 2, 1, 2, 3]      )
            self.assertEqual(A,       ['''AB''', '''C''']      )













import datasets

from .evaluate import evaluate


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

lowercase    : Tuple    	=  """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

lowercase    : List[str]    	=  """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> 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'}]
    >>> 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'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""

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

     def 	_UpperCamelCase     (      self      ):





            """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 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
            _UpperCamelCase              =						[
                {
                    '''paragraphs''': [
                        {
                            '''qas''': [
                                {
                                    '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                    '''id''': ref['''id'''],
                                }
                                for ref in references
                            ]
                        }
                    ]
                }
            ]
            _UpperCamelCase              =						evaluate(dataset=A,       predictions=A      )
            return score






