

'''simple docstring'''




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:int      	=					BlenderbotSmallTokenizer
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					False



 def 			snake_case__  (   self		:   Optional[int]      ):
      super().setUp()

      __magic_name__						       =    ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
      __magic_name__						       =    dict(zip(a__			,				range(len(a__      )      )      )      )

      __magic_name__						       =    ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
      __magic_name__						       =    {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']      )
      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''merges_file''']      )
      with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           fp.write(json.dumps(a__      ) + '''\n'''      )
      with open(self.merges_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           fp.write('''\n'''.join(a__      )      )



 def 			snake_case__  (   self		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      kwargs.update(self.special_tokens_map      )
      return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,				**a__      )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]      ):
      __magic_name__						       =    '''adapt act apte'''
      __magic_name__						       =    '''adapt act apte'''
      return input_text, output_text



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    BlenderbotSmallTokenizer(self.vocab_file			,				self.merges_file			,				**self.special_tokens_map      )
      __magic_name__						       =    '''adapt act apte'''
      __magic_name__						       =    ['''adapt''', '''act''', '''ap@@''', '''te''']
      __magic_name__						       =    tokenizer.tokenize(a__      )
      self.assertListEqual(a__			,				a__      )

      __magic_name__						       =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      __magic_name__						       =    [0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )			,				a__      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )
      assert tok('''sam'''      ).input_ids == [1384]
      __magic_name__						       =    '''I am a small frog.'''
      __magic_name__						       =    tok([src_text]			,				padding=a__			,				truncation=a__      )['''input_ids''']
      __magic_name__						       =    tok.batch_decode(a__			,				skip_special_tokens=a__			,				clean_up_tokenization_spaces=a__      )[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )
      __magic_name__						       =    '''I am a small frog .'''
      __magic_name__						       =    '''.'''
      __magic_name__						       =    tok(a__      )['''input_ids''']
      __magic_name__						       =    tok(a__      )['''input_ids''']

      assert encoded[-1] == encoded_dot[0]




'''simple docstring'''




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      ).to_dict()

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

           # save in new folder
           model_config.save_pretrained(a__      )
           config.save_pretrained(a__      )

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

           # make sure private variable is not incorrectly saved
           __magic_name__						       =    json.loads(config.to_json_string()      )
           self.assertTrue('''_processor_class''' not in dict_as_saved      )

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
          '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
      self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(a__      )
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				trust_remote_code=a__      )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )



 def 			snake_case__  (   self		:   int      ):
      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(a__      ):
                AutoFeatureExtractor.register(a__			,				a__      )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __magic_name__						       =    CustomFeatureExtractor.from_pretrained(a__      )
           with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(a__      )
                __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
                self.assertIsInstance(a__			,				a__      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



 def 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # If remote code is not set, the default is to use local
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote code is disabled, we load the local one.
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote is enabled, we load from the Hub
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(not hasattr(a__			,				'''is_local'''      )      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






