
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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


UpperCamelCase			:       Dict									=				get_tests_dir("fixtures")
UpperCamelCase			:       List[str]									=				get_tests_dir("fixtures/dummy_feature_extractor_config.json")
UpperCamelCase			:       Dict									=				get_tests_dir("fixtures/dummy-config.json")

class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 0





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'   )
      self.assertIsInstance(A__			,  A__   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__   )
      self.assertIsInstance(A__			,  A__   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCamelCase       						= WavaVecaConfig()

       # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__   ).to_dict()

       config_dict.pop('feature_extractor_type'   )
       __UpperCamelCase       						= WavaVecaFeatureExtractor(**A__   )

       # save in new folder
       model_config.save_pretrained(A__   )
       config.save_pretrained(A__   )

       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__   )

       # make sure private variable is not incorrectly saved
       __UpperCamelCase       						= json.loads(config.to_json_string()   )
       self.assertTrue('_processor_class' not in dict_as_saved   )

      self.assertIsInstance(A__			,  A__   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__   )
      self.assertIsInstance(A__			,  A__   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with self.assertRaisesRegex(
          A__			,  'bert-base is not a local folder and is not a valid model identifier'   ):
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained('bert-base'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with self.assertRaisesRegex(
          A__			,  R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'   ):
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__			,  revision='aaaaaa'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with self.assertRaisesRegex(
          A__			,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'			,  ):
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      with self.assertRaises(A__   ):
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'   )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(A__   ):
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'			,  trust_remote_code=A__   )

      __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
          'hf-internal-testing/test_dynamic_feature_extractor'			,  trust_remote_code=A__   )
      self.assertEqual(feature_extractor.__class__.__name__			,  'NewFeatureExtractor'   )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(A__   )
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(A__			,  trust_remote_code=A__   )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,  'NewFeatureExtractor'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

       # Now that the config is registered, it can be used as any other config with the auto-API
       __UpperCamelCase       						= CustomFeatureExtractor.from_pretrained(A__   )
       with tempfile.TemporaryDirectory() as tmp_dir:
        feature_extractor.save_pretrained(A__   )
        __UpperCamelCase       						= AutoFeatureExtractor.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]







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
           lowercase		     =		True

      try:
       AutoConfig.register('custom'			,  A__   )
       AutoFeatureExtractor.register(A__			,  A__   )
       # If remote code is not set, the default is to use local
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'   )
       self.assertEqual(feature_extractor.__class__.__name__			,  'NewFeatureExtractor'   )
       self.assertTrue(feature_extractor.is_local   )

       # If remote code is disabled, we load the local one.
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'			,  trust_remote_code=A__   )
       self.assertEqual(feature_extractor.__class__.__name__			,  'NewFeatureExtractor'   )
       self.assertTrue(feature_extractor.is_local   )

       # If remote is enabled, we load from the Hub
       __UpperCamelCase       						= AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'			,  trust_remote_code=A__   )
       self.assertEqual(feature_extractor.__class__.__name__			,  'NewFeatureExtractor'   )
       self.assertTrue(not hasattr(A__			,  'is_local'   )   )

      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]





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



