






'''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


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






class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):
 snake_case_     							=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  int:
       SCREAMING_SNAKE_CASE__   : List[str]													=0




 def    __magic_name__		(					self		:							Any					)		->  str:
       SCREAMING_SNAKE_CASE__   : List[str]													=AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''					)
       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							List[Any]					)		->  Dict:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : str													=WavaVecaConfig()
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''					)

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

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

       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Any					)		->  Any:
       with tempfile.TemporaryDirectory() as tmpdirname:
             # copy relevant files
             copyfile(_UpperCAmelCase    ,					os.path.join(_UpperCAmelCase    ,					_UpperCAmelCase					)					)
             copyfile(_UpperCAmelCase    ,					os.path.join(_UpperCAmelCase    ,					'''vocab.json'''					)					)

             SCREAMING_SNAKE_CASE__   : str													=AutoProcessor.from_pretrained(_UpperCAmelCase					)

       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Any					)		->  Any:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=WavaVecaFeatureExtractor()
             SCREAMING_SNAKE_CASE__   : List[str]													=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''					)

             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=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:
                   SCREAMING_SNAKE_CASE__   : Dict													=json.load(_UpperCAmelCase					)
                   config_dict.pop('''processor_class'''					)

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

             SCREAMING_SNAKE_CASE__   : str													=AutoProcessor.from_pretrained(_UpperCAmelCase					)

       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Any					)		->  str:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=WavaVecaFeatureExtractor()
             SCREAMING_SNAKE_CASE__   : Any													=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''					)

             SCREAMING_SNAKE_CASE__   : Optional[int]													=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:
                   SCREAMING_SNAKE_CASE__   : List[str]													=json.load(_UpperCAmelCase					)
                   config_dict.pop('''processor_class'''					)

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

             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=AutoProcessor.from_pretrained(_UpperCAmelCase					)

       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Dict:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : 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('''{}'''					)

             SCREAMING_SNAKE_CASE__   : List[str]													=AutoProcessor.from_pretrained(_UpperCAmelCase					)

       self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Optional[Any]:
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_UpperCAmelCase					):
             SCREAMING_SNAKE_CASE__   : 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					):
             SCREAMING_SNAKE_CASE__   : Tuple													=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,					trust_remote_code=_UpperCAmelCase					)

       SCREAMING_SNAKE_CASE__   : str													=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'''					)

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

       SCREAMING_SNAKE_CASE__   : Optional[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
             SCREAMING_SNAKE_CASE__   : Any													=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,					trust_remote_code=_UpperCAmelCase    ,					use_fast=_UpperCAmelCase					)
             SCREAMING_SNAKE_CASE__   : Tuple													=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    __magic_name__		(					self		:							Any					)		->  int:
       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
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=CustomFeatureExtractor.from_pretrained(_UpperCAmelCase					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                   SCREAMING_SNAKE_CASE__   : List[str]													=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]					)					)
                   SCREAMING_SNAKE_CASE__   : Tuple													=CustomTokenizer(_UpperCAmelCase					)

             SCREAMING_SNAKE_CASE__   : Optional[int]													=CustomProcessor(_UpperCAmelCase    ,					_UpperCAmelCase					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                   processor.save_pretrained(_UpperCAmelCase					)
                   SCREAMING_SNAKE_CASE__   : List[str]													=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    __magic_name__		(					self		:							int					)		->  str:






       class    __SCREAMING_SNAKE_CASE					(	_UpperCAmelCase ):
        snake_case_     							=							False






       class    __SCREAMING_SNAKE_CASE					(	_UpperCAmelCase ):
        snake_case_     							=							False






       class    __SCREAMING_SNAKE_CASE					(	_UpperCAmelCase ):
        snake_case_     							=							"AutoFeatureExtractor"
        snake_case_     							=							"AutoTokenizer"
        snake_case_     							=							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.
             SCREAMING_SNAKE_CASE__   : int													=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.
             SCREAMING_SNAKE_CASE__   : Tuple													=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.
             SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							Optional[Any]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : str													=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
       self.assertEqual(processor.__class__.__name__    ,					'''BertTokenizerFast'''					)




 def    __magic_name__		(					self		:							List[str]					)		->  Dict:
       SCREAMING_SNAKE_CASE__   : Any													=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''					)
       self.assertEqual(processor.__class__.__name__    ,					'''ConvNextImageProcessor'''					)






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




 @classmethod
 def    __magic_name__		(					cls		:							Union[str, Any]					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=TOKEN
       HfFolder.save_token(_UpperCAmelCase					)




 @classmethod
 def    __magic_name__		(					cls		:							int					)		->  str:
       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    __magic_name__		(					self		:							List[str]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Optional[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					)

             SCREAMING_SNAKE_CASE__   : List[Any]													=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    __magic_name__		(					self		:							int					)		->  str:
       SCREAMING_SNAKE_CASE__   : 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'''    ,					)

             SCREAMING_SNAKE_CASE__   : List[str]													=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    __magic_name__		(					self		:							List[str]					)		->  Tuple:
       CustomFeatureExtractor.register_for_auto_class()
       CustomTokenizer.register_for_auto_class()
       CustomProcessor.register_for_auto_class()

       SCREAMING_SNAKE_CASE__   : Optional[Any]													=CustomFeatureExtractor.from_pretrained(_UpperCAmelCase					)

       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__   : Optional[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]					)					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=CustomTokenizer(_UpperCAmelCase					)

       SCREAMING_SNAKE_CASE__   : Any													=CustomProcessor(_UpperCAmelCase    ,					_UpperCAmelCase					)

       with tempfile.TemporaryDirectory() as tmp_dir:
             create_repo(F"{USER}/test-dynamic-processor"    ,					token=self._token					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=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:
                   SCREAMING_SNAKE_CASE__   : int													=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()

       SCREAMING_SNAKE_CASE__   : Any													=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"""






__A			   = [
    (1_0_0_0, "M"),
    (9_0_0, "CM"),
    (5_0_0, "D"),
    (4_0_0, "CD"),
    (1_0_0, "C"),
    (9_0, "XC"),
    (5_0, "L"),
    (4_0, "XL"),
    (1_0, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Dict					=      {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
   lowercase__:   List[str]					=      0
   lowercase__:   List[Any]					=      0
   while place < len(__UpperCAmelCase ):
      if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      []
   for arabic, roman in ROMAN:
      ((lowercase__), (lowercase__)):   Tuple					=      divmod(__UpperCAmelCase       ,    __UpperCAmelCase )
      result.append(roman * factor )
      if number == 0:
         break
   return "".join(__UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
