



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase					:				Any   	={
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				Union[str, Any]   	=[
		    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TvltModel',
		    'TvltForPreTraining',
		    'TvltForAudioVisualClassification',
		    'TvltPreTrainedModel',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				Dict   	=['TvltImageProcessor']


if TYPE_CHECKING:
		from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
		from .processing_tvlt import TvltProcessor
		from .feature_extraction_tvlt import TvltFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tvlt import (
				    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TvltForAudioVisualClassification,
				    TvltForPreTraining,
				    TvltModel,
				    TvltPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_tvlt import TvltImageProcessor


else:
		import sys

		lowerCAmelCase					:				List[Any]   	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						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  (				SCREAMING_SNAKE_CASE__ = 5  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					int(float(SCREAMING_SNAKE_CASE__  )  )
						if num_movies < 1:
												return ()
						lowerCAmelCase	:   List[str]					     =					(
						    """https://www.imdb.com/search/title?title_type="""
						    F"""feature&sort=num_votes,desc&count={num_movies}"""
						)
						lowerCAmelCase	:   Optional[Any]					     =					bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__  ).content			,"""html.parser"""  )
						return tuple(
						    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__  )
						    for movie in source.find_all("""div"""			,class_="""lister-item mode-advanced"""  )  )


if __name__ == "__main__":
		import json

		lowerCAmelCase					:				Union[str, Any]   	=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)
		    )
		)

