



"""simple docstring"""






import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowerCAmelCase__      =    get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowerCAmelCase__      =    get_tests_dir('''fixtures/vocab.json''')
lowerCAmelCase__      =    get_tests_dir('''fixtures''')






class 	__snake_case	(     unittest.TestCase):
  snake_case__    :       List[Any]        =						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			0




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''		)
         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase						:    Any         =			WavaVecaConfig()
                _lowerCamelCase						:    List[str]         =			AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''		)

                # save in new folder
                model_config.save_pretrained(_UpperCAmelCase		)
                processor.save_pretrained(_UpperCAmelCase		)

                _lowerCamelCase						:    Optional[Any]         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)

         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdirname:
                # copy relevant files
                copyfile(_UpperCAmelCase						, os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)		)
                copyfile(_UpperCAmelCase						, os.path.join(_UpperCAmelCase						, '''vocab.json'''		)		)

                _lowerCamelCase						:    int         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)

         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase						:    Dict         =			WavaVecaFeatureExtractor()
                _lowerCamelCase						:    int         =			AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''		)

                _lowerCamelCase						:    str         =			WavaVecaProcessor(_UpperCAmelCase						, _UpperCAmelCase		)

                # save in new folder
                processor.save_pretrained(_UpperCAmelCase		)

                # drop `processor_class` in tokenizer
                with open(os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)						, '''r'''		) as f:
                       _lowerCamelCase						:    str         =			json.load(_UpperCAmelCase		)
                       config_dict.pop('''processor_class'''		)

                with open(os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)						, '''w'''		) as f:
                       f.write(json.dumps(_UpperCAmelCase		)		)

                _lowerCamelCase						:    Dict         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)

         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase						:    List[str]         =			WavaVecaFeatureExtractor()
                _lowerCamelCase						:    Tuple         =			AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''		)

                _lowerCamelCase						:    Dict         =			WavaVecaProcessor(_UpperCAmelCase						, _UpperCAmelCase		)

                # save in new folder
                processor.save_pretrained(_UpperCAmelCase		)

                # drop `processor_class` in feature extractor
                with open(os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)						, '''r'''		) as f:
                       _lowerCamelCase						:    Optional[Any]         =			json.load(_UpperCAmelCase		)
                       config_dict.pop('''processor_class'''		)

                with open(os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)						, '''w'''		) as f:
                       f.write(json.dumps(_UpperCAmelCase		)		)

                _lowerCamelCase						:    Tuple         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)

         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCamelCase						:    Union[str, Any]         =			WavaVecaConfig(processor_class='''Wav2Vec2Processor'''		)
                model_config.save_pretrained(_UpperCAmelCase		)
                # copy relevant files
                copyfile(_UpperCAmelCase						, os.path.join(_UpperCAmelCase						, '''vocab.json'''		)		)
                # create emtpy sample processor
                with open(os.path.join(_UpperCAmelCase						, _UpperCAmelCase		)						, '''w'''		) as f:
                       f.write('''{}'''		)

                _lowerCamelCase						:    Optional[int]         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)

         self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         with self.assertRaises(_UpperCAmelCase		):
                _lowerCamelCase						:    List[Any]         =			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''		)
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(_UpperCAmelCase		):
                _lowerCamelCase						:    List[Any]         =			AutoProcessor.from_pretrained(
                    '''hf-internal-testing/test_dynamic_processor'''						, trust_remote_code=_UpperCAmelCase		)

         _lowerCamelCase						:    Dict         =			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''						, trust_remote_code=_UpperCAmelCase		)
         self.assertTrue(processor.special_attribute_present		)
         self.assertEqual(processor.__class__.__name__						, '''NewProcessor'''		)

         _lowerCamelCase						:    List[str]         =			processor.feature_extractor
         self.assertTrue(feature_extractor.special_attribute_present		)
         self.assertEqual(feature_extractor.__class__.__name__						, '''NewFeatureExtractor'''		)

         _lowerCamelCase						:    Any         =			processor.tokenizer
         self.assertTrue(tokenizer.special_attribute_present		)
         if is_tokenizers_available():
                self.assertEqual(tokenizer.__class__.__name__						, '''NewTokenizerFast'''		)

                # Test we can also load the slow version
                _lowerCamelCase						:    int         =			AutoProcessor.from_pretrained(
                    '''hf-internal-testing/test_dynamic_processor'''						, trust_remote_code=_UpperCAmelCase						, use_fast=_UpperCAmelCase		)
                _lowerCamelCase						:    str         =			new_processor.tokenizer
                self.assertTrue(new_tokenizer.special_attribute_present		)
                self.assertEqual(new_tokenizer.__class__.__name__						, '''NewTokenizer'''		)
         else:
                self.assertEqual(tokenizer.__class__.__name__						, '''NewTokenizer'''		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         try:
                AutoConfig.register('''custom'''						, _UpperCAmelCase		)
                AutoFeatureExtractor.register(_UpperCAmelCase						, _UpperCAmelCase		)
                AutoTokenizer.register(_UpperCAmelCase						, slow_tokenizer_class=_UpperCAmelCase		)
                AutoProcessor.register(_UpperCAmelCase						, _UpperCAmelCase		)
                # Trying to register something existing in the Transformers library will raise an error
                with self.assertRaises(_UpperCAmelCase		):
                       AutoProcessor.register(_UpperCAmelCase						, _UpperCAmelCase		)

                # Now that the config is registered, it can be used as any other config with the auto-API
                _lowerCamelCase						:    Optional[Any]         =			CustomFeatureExtractor.from_pretrained(_UpperCAmelCase		)

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

                _lowerCamelCase						:    List[str]         =			CustomProcessor(_UpperCAmelCase						, _UpperCAmelCase		)

                with tempfile.TemporaryDirectory() as tmp_dir:
                       processor.save_pretrained(_UpperCAmelCase		)
                       _lowerCamelCase						:    Tuple         =			AutoProcessor.from_pretrained(_UpperCAmelCase		)
                       self.assertIsInstance(_UpperCAmelCase						, _UpperCAmelCase		)

         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]
                if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]
                if CustomConfig in PROCESSOR_MAPPING._extra_content:
                       del PROCESSOR_MAPPING._extra_content[CustomConfig]




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         class 	__snake_case	(     lowerCamelCase_):
           snake_case__    :       Tuple        =						False






         class 	__snake_case	(     lowerCamelCase_):
           snake_case__    :       List[Any]        =						False






         class 	__snake_case	(     lowerCamelCase_):
           snake_case__    :       Any        =						"""AutoFeatureExtractor"""
           snake_case__    :       int        =						"""AutoTokenizer"""
           snake_case__    :       List[Any]        =						False

         try:
                AutoConfig.register('''custom'''						, _UpperCAmelCase		)
                AutoFeatureExtractor.register(_UpperCAmelCase						, _UpperCAmelCase		)
                AutoTokenizer.register(_UpperCAmelCase						, slow_tokenizer_class=_UpperCAmelCase		)
                AutoProcessor.register(_UpperCAmelCase						, _UpperCAmelCase		)
                # If remote code is not set, the default is to use local classes.
                _lowerCamelCase						:    str         =			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''		)
                self.assertEqual(processor.__class__.__name__						, '''NewProcessor'''		)
                self.assertFalse(processor.special_attribute_present		)
                self.assertFalse(processor.feature_extractor.special_attribute_present		)
                self.assertFalse(processor.tokenizer.special_attribute_present		)

                # If remote code is disabled, we load the local ones.
                _lowerCamelCase						:    List[Any]         =			AutoProcessor.from_pretrained(
                    '''hf-internal-testing/test_dynamic_processor'''						, trust_remote_code=_UpperCAmelCase		)
                self.assertEqual(processor.__class__.__name__						, '''NewProcessor'''		)
                self.assertFalse(processor.special_attribute_present		)
                self.assertFalse(processor.feature_extractor.special_attribute_present		)
                self.assertFalse(processor.tokenizer.special_attribute_present		)

                # If remote is enabled, we load from the Hub.
                _lowerCamelCase						:    List[str]         =			AutoProcessor.from_pretrained(
                    '''hf-internal-testing/test_dynamic_processor'''						, trust_remote_code=_UpperCAmelCase		)
                self.assertEqual(processor.__class__.__name__						, '''NewProcessor'''		)
                self.assertTrue(processor.special_attribute_present		)
                self.assertTrue(processor.feature_extractor.special_attribute_present		)
                self.assertTrue(processor.tokenizer.special_attribute_present		)

         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]
                if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]
                if CustomConfig in PROCESSOR_MAPPING._extra_content:
                       del PROCESSOR_MAPPING._extra_content[CustomConfig]




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






         """simple docstring"""






         _lowerCamelCase						:    int         =			AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
         self.assertEqual(processor.__class__.__name__						, '''BertTokenizerFast'''		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''		)
         self.assertEqual(processor.__class__.__name__						, '''ConvNextImageProcessor'''		)






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




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			TOKEN
         HfFolder.save_token(_UpperCAmelCase		)




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						str		):






         """simple docstring"""






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

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

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			WavaVecaProcessor.from_pretrained(_UpperCAmelCase		)
         with tempfile.TemporaryDirectory() as tmp_dir:
                processor.save_pretrained(
                    os.path.join(_UpperCAmelCase						, '''test-processor'''		)						, push_to_hub=_UpperCAmelCase						, use_auth_token=self._token		)

                _lowerCamelCase						:    Optional[int]         =			WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''		)
                for k, v in processor.feature_extractor.__dict__.items():
                       self.assertEqual(_UpperCAmelCase						, getattr(new_processor.feature_extractor						, _UpperCAmelCase		)		)
                self.assertDictEqual(new_processor.tokenizer.get_vocab()						, processor.tokenizer.get_vocab()		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			WavaVecaProcessor.from_pretrained(_UpperCAmelCase		)

         with tempfile.TemporaryDirectory() as tmp_dir:
                processor.save_pretrained(
                    os.path.join(_UpperCAmelCase						, '''test-processor-org'''		)						, push_to_hub=_UpperCAmelCase						, use_auth_token=self._token						, organization='''valid_org'''						, )

                _lowerCamelCase						:    Any         =			WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''		)
                for k, v in processor.feature_extractor.__dict__.items():
                       self.assertEqual(_UpperCAmelCase						, getattr(new_processor.feature_extractor						, _UpperCAmelCase		)		)
                self.assertDictEqual(new_processor.tokenizer.get_vocab()						, processor.tokenizer.get_vocab()		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         CustomFeatureExtractor.register_for_auto_class()
         CustomTokenizer.register_for_auto_class()
         CustomProcessor.register_for_auto_class()

         _lowerCamelCase						:    Optional[int]         =			CustomFeatureExtractor.from_pretrained(_UpperCAmelCase		)

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

         _lowerCamelCase						:    Union[str, Any]         =			CustomProcessor(_UpperCAmelCase						, _UpperCAmelCase		)

         with tempfile.TemporaryDirectory() as tmp_dir:
                create_repo(f'''{USER}/test-dynamic-processor'''						, token=self._token		)
                _lowerCamelCase						:    str         =			Repository(_UpperCAmelCase						, clone_from=f'''{USER}/test-dynamic-processor'''						, token=self._token		)
                processor.save_pretrained(_UpperCAmelCase		)

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

                # This has added the proper auto_map field to the tokenizer config
                with open(os.path.join(_UpperCAmelCase						, '''tokenizer_config.json'''		)		) as f:
                       _lowerCamelCase						:    str         =			json.load(_UpperCAmelCase		)
                self.assertDictEqual(
                    tokenizer_config['''auto_map''']						, {
                        '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                    }						, )

                # The code has been copied from fixtures
                self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase						, '''custom_feature_extraction.py'''		)		)		)
                self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase						, '''custom_tokenization.py'''		)		)		)
                self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase						, '''custom_processing.py'''		)		)		)

                repo.push_to_hub()

         _lowerCamelCase						:    List[str]         =			AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''						, trust_remote_code=_UpperCAmelCase		)
         # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
         self.assertEqual(new_processor.__class__.__name__						, '''CustomProcessor'''		)







"""simple docstring"""






import argparse


lowerCAmelCase__      =    '''docs/source/_static/js/custom.js'''






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       with open(A_,    encoding='''utf-8''',    newline='''\n'''						) as f:
              _lowerCamelCase						:    int         =			f.readlines()
       _lowerCamelCase						:    List[str]         =			0

       # First let's put the right version
       while not lines[index].startswith('''const stableVersion ='''						):
              index += 1
       _lowerCamelCase						:    List[Any]         =			F'''const stableVersion = "v{version}"\n'''

       # Then update the dictionary
       while not lines[index].startswith('''const versionMapping = {'''						):
              index += 1

       # We go until the end
       while not lines[index].startswith('''}'''						):
              index += 1
       # We add the new version at the end
       lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

       with open(A_,    '''w''',    encoding='''utf-8''',    newline='''\n'''						) as f:
              f.writelines(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    argparse.ArgumentParser()
   parser.add_argument('''--version''', help='''Release version.''')
   lowerCAmelCase__      =    parser.parse_args()
   update_custom_js(args.version)


