


'''simple docstring'''





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__  			=				get_tests_dir('''fixtures''')
UpperCAmelCase__  			=				get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
UpperCAmelCase__  			=				get_tests_dir('''fixtures/dummy-config.json''')
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			str	)      ->		List[Any]:
						__A=	0
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		str:
						__A=	AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Tuple:
						__A=	AutoFeatureExtractor.from_pretrained(lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						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(lowerCAmelCase_	).to_dict()

									config_dict.pop('feature_extractor_type'	)
									__A=	WavaVecaFeatureExtractor(**lowerCAmelCase_	)

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

									__A=	AutoFeatureExtractor.from_pretrained(lowerCAmelCase_	)

									# 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(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						__A=	AutoFeatureExtractor.from_pretrained(lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						with self.assertRaisesRegex(
						    lowerCAmelCase_			,    'bert-base is not a local folder and is not a valid model identifier'	):
									__A=	AutoFeatureExtractor.from_pretrained('bert-base'	)
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[str]:
						with self.assertRaisesRegex(
						    lowerCAmelCase_			,    r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'	):
									__A=	AutoFeatureExtractor.from_pretrained(lowerCAmelCase_			,    revision='aaaaaa'	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Union[str, Any]:
						with self.assertRaisesRegex(
						    lowerCAmelCase_			,    '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 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(lowerCAmelCase_	):
									__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(lowerCAmelCase_	):
									__A=	AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'			,    trust_remote_code=lowerCAmelCase_	)

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

						# Test feature extractor can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(lowerCAmelCase_	)
									__A=	AutoFeatureExtractor.from_pretrained(lowerCAmelCase_			,    trust_remote_code=lowerCAmelCase_	)
						self.assertEqual(reloaded_feature_extractor.__class__.__name__			,    'NewFeatureExtractor'	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		List[str]:
						try:
									AutoConfig.register('custom'			,    lowerCAmelCase_	)
									AutoFeatureExtractor.register(lowerCAmelCase_			,    lowerCAmelCase_	)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(lowerCAmelCase_	):
												AutoFeatureExtractor.register(lowerCAmelCase_			,    lowerCAmelCase_	)

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

			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						class 						a__					(					a_   ):





									'''simple docstring'''

									A						:				Optional[int]					      =			True

						try:
									AutoConfig.register('custom'			,    lowerCAmelCase_	)
									AutoFeatureExtractor.register(lowerCAmelCase_			,    lowerCAmelCase_	)
									# 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=lowerCAmelCase_	)
									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=lowerCAmelCase_	)
									self.assertEqual(feature_extractor.__class__.__name__			,    'NewFeatureExtractor'	)
									self.assertTrue(not hasattr(lowerCAmelCase_			,    '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

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	soup.find_all('td',class_='ratingColumn imdbRating' )
			return {
			    title.a.text: float(rating.strong.text )
			    for title, rating in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



