


"""simple docstring"""
import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


__SCREAMING_SNAKE_CASE				:     Tuple		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     List[Any]		=     OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

__SCREAMING_SNAKE_CASE				:     str		=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					List[Any]:
							for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
														if class_name in processors:
																					snake_case_     							=		model_type_to_module_name(_lowercase					)

																					snake_case_     							=		importlib.import_module(f""".{module_name}"""					,			"""transformers.models"""					)
																					try:
																												return getattr(_lowercase					,			_lowercase					)
																					except AttributeError:
																												continue

							for processor in PROCESSOR_MAPPING._extra_content.values():
														if getattr(_lowercase					,			"""__name__"""					,			_lowercase					) == class_name:
																					return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							snake_case_     							=		importlib.import_module("""transformers"""					)
							if hasattr(_lowercase					,			_lowercase					):
														return getattr(_lowercase					,			_lowercase					)

							return None





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   Dict							)       ->Optional[Any]:



														"""simple docstring"""



														raise EnvironmentError(
														    """AutoProcessor is designed to be instantiated """
														    """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""							)



							@classmethod
							@replace_list_option_in_docstrings(UpperCamelCase_							)
							def 		lowerCAmelCase							(					cls				:   int		,							UpperCAmelCase_				:   Tuple		,							**UpperCAmelCase_				:   Dict							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		kwargs.pop("""config"""		,							UpperCamelCase_							)
														snake_case_     							=		kwargs.pop("""trust_remote_code"""		,							UpperCamelCase_							)
														snake_case_     							=		True

														snake_case_     							=		None
														snake_case_     							=		None

														# First, let's see if we have a preprocessor config.
														# Filter the kwargs for `get_file_from_repo`.
														snake_case_     							=		{
														    key: kwargs[key] for key in inspect.signature(UpperCamelCase_							).parameters.keys() if key in kwargs
														}
														# Let's start by checking whether the processor class is saved in an image processor
														snake_case_     							=		get_file_from_repo(
														    UpperCamelCase_		,							UpperCamelCase_		,							**UpperCamelCase_							)
														if preprocessor_config_file is not None:
																					snake_case_     							=		ImageProcessingMixin.get_image_processor_dict(UpperCamelCase_		,							**UpperCamelCase_							)
																					snake_case_     							=		config_dict.get("""processor_class"""		,							UpperCamelCase_							)
																					if "AutoProcessor" in config_dict.get("""auto_map"""		,							{}							):
																												snake_case_     							=		config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
														if preprocessor_config_file is not None and processor_class is None:
																					snake_case_     							=		FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_		,							**UpperCamelCase_							)
																					snake_case_     							=		config_dict.get("""processor_class"""		,							UpperCamelCase_							)
																					if "AutoProcessor" in config_dict.get("""auto_map"""		,							{}							):
																												snake_case_     							=		config_dict['auto_map']['AutoProcessor']

														if processor_class is None:
																					# Next, let's check whether the processor class is saved in a tokenizer
																					snake_case_     							=		get_file_from_repo(
																					    UpperCamelCase_		,							UpperCamelCase_		,							**UpperCamelCase_							)
																					if tokenizer_config_file is not None:
																												with open(UpperCamelCase_		,							encoding="""utf-8"""							) as reader:
																																			snake_case_     							=		json.load(UpperCamelCase_							)

																												snake_case_     							=		config_dict.get("""processor_class"""		,							UpperCamelCase_							)
																												if "AutoProcessor" in config_dict.get("""auto_map"""		,							{}							):
																																			snake_case_     							=		config_dict['auto_map']['AutoProcessor']

														if processor_class is None:
																					# Otherwise, load config, if it can be loaded.
																					if not isinstance(UpperCamelCase_		,							UpperCamelCase_							):
																												snake_case_     							=		AutoConfig.from_pretrained(
																												    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)

																					# And check if the config contains the processor class.
																					snake_case_     							=		getattr(UpperCamelCase_		,							"""processor_class"""		,							UpperCamelCase_							)
																					if hasattr(UpperCamelCase_		,							"""auto_map"""							) and "AutoProcessor" in config.auto_map:
																												snake_case_     							=		config.auto_map['AutoProcessor']

														if processor_class is not None:
																					snake_case_     							=		processor_class_from_name(UpperCamelCase_							)

														snake_case_     							=		processor_auto_map is not None
														snake_case_     							=		processor_class is not None or type(UpperCamelCase_							) in PROCESSOR_MAPPING
														snake_case_     							=		resolve_trust_remote_code(
														    UpperCamelCase_		,							UpperCamelCase_		,							UpperCamelCase_		,							UpperCamelCase_							)

														if has_remote_code and trust_remote_code:
																					snake_case_     							=		get_class_from_dynamic_module(
																					    UpperCamelCase_		,							UpperCamelCase_		,							**UpperCamelCase_							)
																					snake_case_     							=		kwargs.pop("""code_revision"""		,							UpperCamelCase_							)
																					if os.path.isdir(UpperCamelCase_							):
																												processor_class.register_for_auto_class()
																					return processor_class.from_pretrained(
																					    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)
														elif processor_class is not None:
																					return processor_class.from_pretrained(
																					    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)
														# Last try: we use the PROCESSOR_MAPPING.
														elif type(UpperCamelCase_							) in PROCESSOR_MAPPING:
																					return PROCESSOR_MAPPING[type(UpperCamelCase_							)].from_pretrained(UpperCamelCase_		,							**UpperCamelCase_							)

														# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
														# tokenizer.
														try:
																					return AutoTokenizer.from_pretrained(
																					    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)
														except Exception:
																					try:
																												return AutoImageProcessor.from_pretrained(
																												    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)
																					except Exception:
																												pass

																					try:
																												return AutoFeatureExtractor.from_pretrained(
																												    UpperCamelCase_		,							trust_remote_code=UpperCamelCase_		,							**UpperCamelCase_							)
																					except Exception:
																												pass

														raise ValueError(
														    F"""Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a """
														    """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
														    """the files of at least one of those processing classes."""							)



							@staticmethod
							def 		lowerCAmelCase							(					UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Tuple							)       ->Optional[Any]:



														"""simple docstring"""



														PROCESSOR_MAPPING.register(UpperCamelCase_		,							UpperCamelCase_							)








'''simple docstring'''

import bsa
import requests







def     __UpperCamelCase					(				_lowercase       )			->      dict[str, str]:
	return {
	    "name": soup.ha.a.text,
	    "genre": soup.find('span',					class_='genre'       ).text.strip(),
	    "rating": soup.strong.text,
	    "page_link": f'''https://www.imdb.com{soup.a.get("href"       )}''',
	}







def     __UpperCamelCase					(				_lowercase = 5       )			->      tuple:
	_lowercase       :							List[Any]  	=		int(float(_lowercase       )       )
	if num_movies < 1:
		return ()
	_lowercase       :							Any  	=		(
	    'https://www.imdb.com/search/title?title_type='
	    f'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	_lowercase       :							Tuple  	=		bsa.BeautifulSoup(requests.get(_lowercase       ).content,					'html.parser'       )
	return tuple(
	    get_movie_data_from_soup(_lowercase       )
	    for movie in source.find_all('div',					class_='lister-item mode-advanced'       )       )


if __name__ == "__main__":
		import json

		_A					:     Optional[int]      =int(input('''How many movies would you like to see? '''))
		print(
		    ''', '''.join(
		        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
		    )
		)
