


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


SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures/dummy_feature_extractor_config.json')
SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures/dummy-config.json')


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  0


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained(__A    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        with tempfile.TemporaryDirectory() as tmpdirname:
           __a					=  WavaVecaConfig()

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

           config_dict.pop("""feature_extractor_type"""    )
           __a					=  WavaVecaFeatureExtractor(**__A    )

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

           __a					=  AutoFeatureExtractor.from_pretrained(__A    )

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

        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained(__A    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       """bert-base is not a local folder and is not a valid model identifier"""    ):
           __a					=  AutoFeatureExtractor.from_pretrained("""bert-base"""    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
           __a					=  AutoFeatureExtractor.from_pretrained(__A  ,       revision="""aaaaaa"""    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,       ):
           __a					=  AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""    )


     def 	snake_case_  (	self    ):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__A    ):
           __a					=  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    ):
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""  ,       trust_remote_code=__A    )

        __a					=  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    )
           __a					=  AutoFeatureExtractor.from_pretrained(__A  ,       trust_remote_code=__A    )
        self.assertEqual(reloaded_feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )


     def 	snake_case_  (	self    ):
        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
           __a					=  CustomFeatureExtractor.from_pretrained(__A    )
           with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(__A    )
              __a					=  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 	snake_case_  (	self    ):


        class     __UpperCAmelCase							(       __A							):


             """simple docstring"""
             _lowerCamelCase   =      True

        try:
           AutoConfig.register("""custom"""  ,       __A    )
           AutoFeatureExtractor.register(__A  ,       __A    )
           # If remote code is not set, the default is to use local
           __a					=  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.
           __a					=  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
           __a					=  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'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


