


"""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     					=   get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__UpperCAmelCase     					=   get_tests_dir("fixtures/vocab.json")
__UpperCAmelCase     					=   get_tests_dir("fixtures")

class 			lowercase_	(			unittest.TestCase    ):
      __magic_name__  :				Optional[int] 							=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]







      def 					_lowerCAmelCase							(   self			:				List[str]     ):
        lowerCAmelCase__     :     Optional[int]										=       0







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        lowerCAmelCase__     :     List[str]										=       AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"     )
        self.assertIsInstance(_lowercase		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				List[str]     ):

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase__     :     Any										=       WavaVecaConfig()
          lowerCAmelCase__     :     Dict										=       AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"     )

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

          lowerCAmelCase__     :     int										=       AutoProcessor.from_pretrained(_lowercase     )

        self.assertIsInstance(_lowercase		,	_lowercase     )







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

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

          lowerCAmelCase__     :     str										=       AutoProcessor.from_pretrained(_lowercase     )

        self.assertIsInstance(_lowercase		,	_lowercase     )







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

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

          lowerCAmelCase__     :     Any										=       WavaVecaProcessor(_lowercase		,	_lowercase     )

          # save in new folder
          processor.save_pretrained(_lowercase     )

          # drop `processor_class` in tokenizer
          with open(os.path.join(_lowercase		,	_lowercase     )		,	"r"     ) as f:
            lowerCAmelCase__     :     int										=       json.load(_lowercase     )
            config_dict.pop("processor_class"     )

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

          lowerCAmelCase__     :     Optional[int]										=       AutoProcessor.from_pretrained(_lowercase     )

        self.assertIsInstance(_lowercase		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				List[Any]     ):

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase__     :     Union[str, Any]										=       WavaVecaFeatureExtractor()
          lowerCAmelCase__     :     Dict										=       AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"     )

          lowerCAmelCase__     :     Tuple										=       WavaVecaProcessor(_lowercase		,	_lowercase     )

          # save in new folder
          processor.save_pretrained(_lowercase     )

          # drop `processor_class` in feature extractor
          with open(os.path.join(_lowercase		,	_lowercase     )		,	"r"     ) as f:
            lowerCAmelCase__     :     Dict										=       json.load(_lowercase     )
            config_dict.pop("processor_class"     )

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

          lowerCAmelCase__     :     Union[str, Any]										=       AutoProcessor.from_pretrained(_lowercase     )

        self.assertIsInstance(_lowercase		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				List[str]     ):

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase__     :     str										=       WavaVecaConfig(processor_class="Wav2Vec2Processor"     )
          model_config.save_pretrained(_lowercase     )
          # copy relevant files
          copyfile(_lowercase		,	os.path.join(_lowercase		,	"vocab.json"     )     )
          # create emtpy sample processor
          with open(os.path.join(_lowercase		,	_lowercase     )		,	"w"     ) as f:
            f.write("{}"     )

          lowerCAmelCase__     :     str										=       AutoProcessor.from_pretrained(_lowercase     )

        self.assertIsInstance(_lowercase		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				Tuple     ):

        with self.assertRaises(_lowercase     ):
          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(_lowercase     ):
          lowerCAmelCase__     :     Optional[Any]										=       AutoProcessor.from_pretrained(
              "hf-internal-testing/test_dynamic_processor"		,	trust_remote_code=_lowercase     )

        lowerCAmelCase__     :     List[Any]										=       AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		,	trust_remote_code=_lowercase     )
        self.assertTrue(processor.special_attribute_present     )
        self.assertEqual(processor.__class__.__name__		,	"NewProcessor"     )

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

        lowerCAmelCase__     :     Tuple										=       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__     :     List[Any]										=       AutoProcessor.from_pretrained(
              "hf-internal-testing/test_dynamic_processor"		,	trust_remote_code=_lowercase		,	use_fast=_lowercase     )
          lowerCAmelCase__     :     Optional[int]										=       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 					_lowerCAmelCase							(   self			:				str     ):

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

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

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

          lowerCAmelCase__     :     Dict										=       CustomProcessor(_lowercase		,	_lowercase     )

          with tempfile.TemporaryDirectory() as tmp_dir:
            processor.save_pretrained(_lowercase     )
            lowerCAmelCase__     :     str										=       AutoProcessor.from_pretrained(_lowercase     )
            self.assertIsInstance(_lowercase		,	_lowercase     )

        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 					_lowerCAmelCase							(   self			:				Any     ):

        class 			lowercase_	(			_a    ):
              __magic_name__  :				int 							=					False

        class 			lowercase_	(			_a    ):
              __magic_name__  :				int 							=					False

        class 			lowercase_	(			_a    ):
              __magic_name__  :				str 							=					"""AutoFeatureExtractor"""
              __magic_name__  :				Optional[int] 							=					"""AutoTokenizer"""
              __magic_name__  :				str 							=					False

        try:
          AutoConfig.register("custom"		,	_lowercase     )
          AutoFeatureExtractor.register(_lowercase		,	_lowercase     )
          AutoTokenizer.register(_lowercase		,	slow_tokenizer_class=_lowercase     )
          AutoProcessor.register(_lowercase		,	_lowercase     )
          # If remote code is not set, the default is to use local classes.
          lowerCAmelCase__     :     Optional[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.
          lowerCAmelCase__     :     Dict										=       AutoProcessor.from_pretrained(
              "hf-internal-testing/test_dynamic_processor"		,	trust_remote_code=_lowercase     )
          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__     :     Optional[Any]										=       AutoProcessor.from_pretrained(
              "hf-internal-testing/test_dynamic_processor"		,	trust_remote_code=_lowercase     )
          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 					_lowerCAmelCase							(   self			:				Tuple     ):
        lowerCAmelCase__     :     int										=       AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"     )
        self.assertEqual(processor.__class__.__name__		,	"BertTokenizerFast"     )







      def 					_lowerCAmelCase							(   self			:				Tuple     ):
        lowerCAmelCase__     :     Optional[Any]										=       AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"     )
        self.assertEqual(processor.__class__.__name__		,	"ConvNextImageProcessor"     )

@is_staging_test
class 			lowercase_	(			unittest.TestCase    ):
      __magic_name__  :				Optional[int] 							=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]







      @classmethod
      def 					_lowerCAmelCase							(   cls			:				str     ):
        lowerCAmelCase__     :     Optional[int]										=       TOKEN
        HfFolder.save_token(_lowercase     )







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

        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 					_lowerCAmelCase							(   self			:				List[str]     ):
        lowerCAmelCase__     :     int										=       WavaVecaProcessor.from_pretrained(_lowercase     )
        with tempfile.TemporaryDirectory() as tmp_dir:
          processor.save_pretrained(
              os.path.join(_lowercase		,	"test-processor"     )		,	push_to_hub=_lowercase		,	use_auth_token=self._token     )

          lowerCAmelCase__     :     Any										=       WavaVecaProcessor.from_pretrained(f"{USER}/test-processor"     )
          for k, v in processor.feature_extractor.__dict__.items():
            self.assertEqual(_lowercase		,	getattr(new_processor.feature_extractor		,	_lowercase     )     )
          self.assertDictEqual(new_processor.tokenizer.get_vocab()		,	processor.tokenizer.get_vocab()     )







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        lowerCAmelCase__     :     Dict										=       WavaVecaProcessor.from_pretrained(_lowercase     )

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

          lowerCAmelCase__     :     Union[str, Any]										=       WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"     )
          for k, v in processor.feature_extractor.__dict__.items():
            self.assertEqual(_lowercase		,	getattr(new_processor.feature_extractor		,	_lowercase     )     )
          self.assertDictEqual(new_processor.tokenizer.get_vocab()		,	processor.tokenizer.get_vocab()     )







      def 					_lowerCAmelCase							(   self			:				List[Any]     ):
        CustomFeatureExtractor.register_for_auto_class()
        CustomTokenizer.register_for_auto_class()
        CustomProcessor.register_for_auto_class()

        lowerCAmelCase__     :     int										=       CustomFeatureExtractor.from_pretrained(_lowercase     )

        with tempfile.TemporaryDirectory() as tmp_dir:
          lowerCAmelCase__     :     Union[str, Any]										=       os.path.join(_lowercase		,	"vocab.txt"     )
          with open(_lowercase		,	"w"		,	encoding="utf-8"     ) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]     )     )
          lowerCAmelCase__     :     Optional[int]										=       CustomTokenizer(_lowercase     )

        lowerCAmelCase__     :     Tuple										=       CustomProcessor(_lowercase		,	_lowercase     )

        with tempfile.TemporaryDirectory() as tmp_dir:
          create_repo(f"{USER}/test-dynamic-processor"		,	token=self._token     )
          lowerCAmelCase__     :     Any										=       Repository(_lowercase		,	clone_from=f"{USER}/test-dynamic-processor"		,	token=self._token     )
          processor.save_pretrained(_lowercase     )

          # 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(_lowercase		,	"tokenizer_config.json"     )     ) as f:
            lowerCAmelCase__     :     Dict										=       json.load(_lowercase     )
          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(_lowercase		,	"custom_feature_extraction.py"     )     )     )
          self.assertTrue(os.path.isfile(os.path.join(_lowercase		,	"custom_tokenization.py"     )     )     )
          self.assertTrue(os.path.isfile(os.path.join(_lowercase		,	"custom_processing.py"     )     )     )

          repo.push_to_hub()

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




import baseaa





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						bytes:


						'''simple docstring'''






						return baseaa.baaencode(string.encode("utf-8"			)			)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						return baseaa.baadecode(_lowerCAmelCase			).decode("utf-8"			)


if __name__ == "__main__":
	A				:       Dict      =  'Hello World!'
	A				:       Any      =  baseaa_encode(test)
	print(encoded)

	A				:       Optional[int]      =  baseaa_decode(encoded)
	print(decoded)





