



"""simple docstring"""

import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Any	      =	BioGptTokenizer
 _lowerCAmelCase     :    str	      =	False

 def 		_snake_case      (  self	: str       ):
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     snake_case_				:  Optional[Any]       =			[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     snake_case_				:  Optional[int]       =			dict(zip(lowercase_						,  range(len(lowercase_       )       )       )       )
     snake_case_				:  str       =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     snake_case_				:  int       =			os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES['''vocab_file''']       )
     snake_case_				:  Tuple       =			os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES['''merges_file''']       )
     with open(self.vocab_file						,  '''w'''       ) as fp:
         fp.write(json.dumps(lowercase_       )       )
     with open(self.merges_file						,  '''w'''       ) as fp:
         fp.write('''\n'''.join(lowercase_       )       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: List[str]       ):
     snake_case_				:  Union[str, Any]       =			'''lower newer'''
     snake_case_				:  Any       =			'''lower newer'''
     return input_text, output_text

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Tuple       =			BioGptTokenizer(self.vocab_file						,  self.merges_file       )

     snake_case_				:  Union[str, Any]       =			'''lower'''
     snake_case_				:  int       =			['''low''', '''er</w>''']
     snake_case_				:  Union[str, Any]       =			tokenizer.tokenize(lowercase_       )
     self.assertListEqual(lowercase_						,  lowercase_       )

     snake_case_				:  Optional[int]       =			tokens + ['''<unk>''']
     snake_case_				:  List[Any]       =			[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_       )						,  lowercase_       )






 @slow
 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Any       =			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

     snake_case_				:  str       =			tokenizer.encode('''sequence builders'''						,  add_special_tokens=lowercase_       )
     snake_case_				:  Union[str, Any]       =			tokenizer.encode('''multi-sequence build'''						,  add_special_tokens=lowercase_       )

     snake_case_				:  Optional[Any]       =			tokenizer.build_inputs_with_special_tokens(lowercase_       )
     snake_case_				:  int       =			tokenizer.build_inputs_with_special_tokens(lowercase_						,  lowercase_       )

     self.assertTrue(encoded_sentence == [2] + text       )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a       )










"""simple docstring"""

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

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

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowercase__					:						Any												=			get_tests_dir('''fixtures''')




class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple       ):
     # A mock response for an HTTP head request to emulate server down
     snake_case_				:  str       =			mock.Mock()
     snake_case_				:  Optional[Any]       =			500
     snake_case_				:  str       =			{}
     snake_case_				:  Optional[int]       =			HTTPError
     snake_case_				:  Tuple       =			{}

     # Download this model to make sure it's in the cache.
     snake_case_				:  List[str]       =			WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''       )
     # Under the mock environment we get a 500 error when trying to reach the model.
     with mock.patch('''requests.Session.request'''						,  return_value=lowercase_       ) as mock_head:
         snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''       )
         # This check we did call the fake head request
         mock_head.assert_called()






 def 		_snake_case      (  self	: Union[str, Any]       ):
     # This test is for deprecated behavior and can be removed in v5
     snake_case_				:  str       =			WavaVecaFeatureExtractor.from_pretrained(
         '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''       )





@is_staging_test
class  _UpperCAmelCase (    unittest.TestCase):

 @classmethod
 def 		_snake_case      (  cls	: int       ):
     snake_case_				:  Dict       =			TOKEN
     HfFolder.save_token(lowercase_       )

 @classmethod
 def 		_snake_case      (  cls	: str       ):
     try:
         delete_repo(token=cls._token						,  repo_id='''test-feature-extractor'''       )
     except HTTPError:
         pass

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

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

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained(lowercase_       )
     feature_extractor.push_to_hub('''test-feature-extractor'''						,  use_auth_token=self._token       )

     snake_case_				:  Optional[Any]       =			WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )

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

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

     snake_case_				:  Optional[int]       =			WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			WavaVecaFeatureExtractor.from_pretrained(lowercase_       )
     feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''						,  use_auth_token=self._token       )

     snake_case_				:  Optional[int]       =			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor'''       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )

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

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

     snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org'''       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )






 def 		_snake_case      (  self	: str       ):
     CustomFeatureExtractor.register_for_auto_class()
     snake_case_				:  Optional[Any]       =			CustomFeatureExtractor.from_pretrained(lowercase_       )

     feature_extractor.push_to_hub('''test-dynamic-feature-extractor'''						,  use_auth_token=self._token       )

     # This has added the proper auto_map field to the config
     self.assertDictEqual(
         feature_extractor.auto_map						,  {'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''}						,  )

     snake_case_				:  Any       =			AutoFeatureExtractor.from_pretrained(
         f"{USER}/test-dynamic-feature-extractor"						,  trust_remote_code=lowercase_       )
     # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
     self.assertEqual(new_feature_extractor.__class__.__name__						,  '''CustomFeatureExtractor'''       )





