






"""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


UpperCamelCase_	:				Optional[int]										= get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCamelCase_	:				Optional[Any]										= get_tests_dir("""fixtures/vocab.json""")
UpperCamelCase_	:				Tuple										= get_tests_dir("""fixtures""")


class      _lowercase (     unittest.TestCase						):
      _a   :     Optional[int]								=   ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=0


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''						)
            self.assertIsInstance(a							,   a						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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

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

                  __snake_case      :    Union[str, Any]  			=AutoProcessor.from_pretrained(a						)

            self.assertIsInstance(a							,   a						)


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





            """simple docstring"""







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

                  __snake_case      :    Any  			=AutoProcessor.from_pretrained(a						)

            self.assertIsInstance(a							,   a						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            with tempfile.TemporaryDirectory() as tmpdirname:
                  __snake_case      :    Dict  			=WavaVecaFeatureExtractor()
                  __snake_case      :    Optional[Any]  			=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''						)

                  __snake_case      :    Union[str, Any]  			=WavaVecaProcessor(a							,   a						)

                  # save in new folder
                  processor.save_pretrained(a						)

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

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

                  __snake_case      :    Any  			=AutoProcessor.from_pretrained(a						)

            self.assertIsInstance(a							,   a						)


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            with tempfile.TemporaryDirectory() as tmpdirname:
                  __snake_case      :    Optional[int]  			=WavaVecaFeatureExtractor()
                  __snake_case      :    Any  			=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''						)

                  __snake_case      :    int  			=WavaVecaProcessor(a							,   a						)

                  # save in new folder
                  processor.save_pretrained(a						)

                  # drop `processor_class` in feature extractor
                  with open(os.path.join(a							,   a						)							,   '''r'''						) as f:
                        __snake_case      :    Any  			=json.load(a						)
                        config_dict.pop('''processor_class'''						)

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

                  __snake_case      :    Optional[Any]  			=AutoProcessor.from_pretrained(a						)

            self.assertIsInstance(a							,   a						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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

                  __snake_case      :    Union[str, Any]  			=AutoProcessor.from_pretrained(a						)

            self.assertIsInstance(a							,   a						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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

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

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

            __snake_case      :    str  			=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
                  __snake_case      :    Any  			=AutoProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_processor'''							,   trust_remote_code=a							,   use_fast=a						)
                  __snake_case      :    Dict  			=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 _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







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

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

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        __snake_case      :    str  			=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]						)						)
                        __snake_case      :    List[str]  			=CustomTokenizer(a						)

                  __snake_case      :    Dict  			=CustomProcessor(a							,   a						)

                  with tempfile.TemporaryDirectory() as tmp_dir:
                        processor.save_pretrained(a						)
                        __snake_case      :    List[Any]  			=AutoProcessor.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]
                  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 _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            class      _lowercase (     lowerCAmelCase						):
                  _a   :     List[Any]								=   False



            class      _lowercase (     lowerCAmelCase						):
                  _a   :     str								=   False



            class      _lowercase (     lowerCAmelCase						):
                  _a   :     List[str]								=   '''AutoFeatureExtractor'''
                  _a   :     Tuple								=   '''AutoTokenizer'''
                  _a   :     str								=   False

            try:
                  AutoConfig.register('''custom'''							,   a						)
                  AutoFeatureExtractor.register(a							,   a						)
                  AutoTokenizer.register(a							,   slow_tokenizer_class=a						)
                  AutoProcessor.register(a							,   a						)
                  # If remote code is not set, the default is to use local classes.
                  __snake_case      :    Tuple  			=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.
                  __snake_case      :    str  			=AutoProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_processor'''							,   trust_remote_code=a						)
                  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.
                  __snake_case      :    Any  			=AutoProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_processor'''							,   trust_remote_code=a						)
                  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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''						)
            self.assertEqual(processor.__class__.__name__							,   '''BertTokenizerFast'''						)







      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    List[str]  			=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''						)
            self.assertEqual(processor.__class__.__name__							,   '''ConvNextImageProcessor'''						)



@is_staging_test
class      _lowercase (     unittest.TestCase						):
      _a   :     Optional[Any]								=   ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


      @classmethod
      def _UpperCamelCase						(    cls				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			=TOKEN
            HfFolder.save_token(a						)


      @classmethod
      def _UpperCamelCase						(    cls				:	List[Any]						):





            """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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







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

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


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    int  			=WavaVecaProcessor.from_pretrained(a						)

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

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







      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







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

            __snake_case      :    List[Any]  			=CustomFeatureExtractor.from_pretrained(a						)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  __snake_case      :    Optional[Any]  			=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]						)						)
                  __snake_case      :    Union[str, Any]  			=CustomTokenizer(a						)

            __snake_case      :    List[str]  			=CustomProcessor(a							,   a						)

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

                  # 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(a							,   '''tokenizer_config.json'''						)						) as f:
                        __snake_case      :    str  			=json.load(a						)
                  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(a							,   '''custom_feature_extraction.py'''						)						)						)
                  self.assertTrue(os.path.isfile(os.path.join(a							,   '''custom_tokenization.py'''						)						)						)
                  self.assertTrue(os.path.isfile(os.path.join(a							,   '''custom_processing.py'''						)						)						)

                  repo.push_to_hub()

            __snake_case      :    List[Any]  			=AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''							,   trust_remote_code=a						)
            # 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"""





def     __lowercase					(    a :				str       ,		a :				str )					->					str:
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __snake_case      :    list  			=[]
      for char_count in range(a ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count] )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count] )
      return "".join(a )


if __name__ == "__main__":
    print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)


