


"""simple docstring"""







from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    UpperCAmelCase__       (lowerCAmelCase_ = ""   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_ = "IMDb_Top_250_Movies.csv"   ):




					'''simple docstring'''


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


if __name__ == "__main__":
		write_movies()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




