



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



  def 		lowerCamelCase__				(  self       :    str						)   ->    Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=0



  def 		lowerCamelCase__				(  self       :    int						)   ->    List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""						)
     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Union[str, Any]:


     '''simple docstring'''




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

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

        SCREAMING_SNAKE_CASE_:       Optional[int]   	=AutoProcessor.from_pretrained(lowerCAmelCase						)

     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Optional[Any]						)   ->    Any:


     '''simple docstring'''




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

        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=AutoProcessor.from_pretrained(lowerCAmelCase						)

     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Optional[Any]:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE_:       Any   	=WavaVecaFeatureExtractor()
        SCREAMING_SNAKE_CASE_:       int   	=AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""						)

        SCREAMING_SNAKE_CASE_:       List[Any]   	=WavaVecaProcessor(lowerCAmelCase				,						lowerCAmelCase						)

        # save in new folder
        processor.save_pretrained(lowerCAmelCase						)

        # drop `processor_class` in tokenizer
        with open(os.path.join(lowerCAmelCase				,						lowerCAmelCase						)				,						"""r"""						) as f:
           SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=json.load(lowerCAmelCase						)
           config_dict.pop("""processor_class"""						)

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

        SCREAMING_SNAKE_CASE_:       Any   	=AutoProcessor.from_pretrained(lowerCAmelCase						)

     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    int						)   ->    int:


     '''simple docstring'''




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

        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=WavaVecaProcessor(lowerCAmelCase				,						lowerCAmelCase						)

        # save in new folder
        processor.save_pretrained(lowerCAmelCase						)

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

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

        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=AutoProcessor.from_pretrained(lowerCAmelCase						)

     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Optional[Any]:


     '''simple docstring'''




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

        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=AutoProcessor.from_pretrained(lowerCAmelCase						)

     self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Union[str, Any]						)   ->    Optional[int]:


     '''simple docstring'''




     with self.assertRaises(lowerCAmelCase						):
        SCREAMING_SNAKE_CASE_:       Dict   	=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""						)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(lowerCAmelCase						):
        SCREAMING_SNAKE_CASE_:       str   	=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""				,						trust_remote_code=lowerCAmelCase						)

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

     SCREAMING_SNAKE_CASE_:       int   	=processor.feature_extractor
     self.assertTrue(feature_extractor.special_attribute_present						)
     self.assertEqual(feature_extractor.__class__.__name__				,						"""NewFeatureExtractor"""						)

     SCREAMING_SNAKE_CASE_:       Union[str, 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_:       str   	=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""				,						trust_remote_code=lowerCAmelCase				,						use_fast=lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       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       :    Union[str, Any]						)   ->    List[str]:


     '''simple docstring'''




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

        # 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(lowerCAmelCase						)

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

        SCREAMING_SNAKE_CASE_:       int   	=CustomProcessor(lowerCAmelCase				,						lowerCAmelCase						)

        with tempfile.TemporaryDirectory() as tmp_dir:
           processor.save_pretrained(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=AutoProcessor.from_pretrained(lowerCAmelCase						)
           self.assertIsInstance(lowerCAmelCase				,						lowerCAmelCase						)

     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						)   ->    Dict:


     '''simple docstring'''




     class     a		(     _a				):
       UpperCamelCase		: List[str]     =    False







     class     a		(     _a				):
       UpperCamelCase		: Optional[Any]     =    False







     class     a		(     _a				):
       UpperCamelCase		: Any     =    """AutoFeatureExtractor"""
       UpperCamelCase		: Tuple     =    """AutoTokenizer"""
       UpperCamelCase		: int     =    False

     try:
        AutoConfig.register("""custom"""				,						lowerCAmelCase						)
        AutoFeatureExtractor.register(lowerCAmelCase				,						lowerCAmelCase						)
        AutoTokenizer.register(lowerCAmelCase				,						slow_tokenizer_class=lowerCAmelCase						)
        AutoProcessor.register(lowerCAmelCase				,						lowerCAmelCase						)
        # 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=lowerCAmelCase						)
        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_:       int   	=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""				,						trust_remote_code=lowerCAmelCase						)
        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						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)
     self.assertEqual(processor.__class__.__name__				,						"""BertTokenizerFast"""						)





  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    int:


     '''simple docstring'''




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







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



  @classmethod
  def 		lowerCamelCase__				(  cls       :    Optional[Any]						)   ->    Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=TOKEN
     HfFolder.save_token(lowerCAmelCase						)



  @classmethod
  def 		lowerCamelCase__				(  cls       :    Dict						)   ->    Optional[int]:


     '''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 		lowerCamelCase__				(  self       :    Dict						)   ->    Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=WavaVecaProcessor.from_pretrained(lowerCAmelCase						)
     with tempfile.TemporaryDirectory() as tmp_dir:
        processor.save_pretrained(
            os.path.join(lowerCAmelCase				,						"""test-processor"""						)				,						push_to_hub=lowerCAmelCase				,						use_auth_token=self._token						)

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



  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=WavaVecaProcessor.from_pretrained(lowerCAmelCase						)

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

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





  def 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    Any:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:       int   	=CustomFeatureExtractor.from_pretrained(lowerCAmelCase						)

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

     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=CustomProcessor(lowerCAmelCase				,						lowerCAmelCase						)

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

        # 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(lowerCAmelCase				,						"""tokenizer_config.json"""						)						) as f:
           SCREAMING_SNAKE_CASE_:       int   	=json.load(lowerCAmelCase						)
        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(lowerCAmelCase				,						"""custom_feature_extraction.py"""						)						)						)
        self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase				,						"""custom_tokenization.py"""						)						)						)
        self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase				,						"""custom_processing.py"""						)						)						)

        repo.push_to_hub()

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







def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							Tuple      		=		(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total



def 				UpperCamelCase_(      )    ->						Optional[int]:
    print(sum_of_series(1							, 1							, 10 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


