


'''simple docstring'''


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


UpperCAmelCase_		:       Any							       =		logging.get_logger(__name__)





class    lowercase__  (						UpperCamelCase_ ):





							'''simple docstring'''


							A_							:				Optional[int]				     =  """semantic"""






							def __init__(   self   , __snake_case=1_0000   , __snake_case=True   , __snake_case=768   , __snake_case=False   , __snake_case=False   , __snake_case=False   , __snake_case=False   , __snake_case=0.7   , __snake_case=True   , __snake_case=1_0048   , __snake_case=12_9595   , __snake_case=12_9599   , __snake_case=1_0000   , __snake_case=256   , __snake_case=49.9   , **__snake_case   , ):
											super().__init__(
											    temperature=__SCREAMING_SNAKE_CASE   , do_sample=__SCREAMING_SNAKE_CASE   , eos_token_id=__SCREAMING_SNAKE_CASE   , renormalize_logits=__SCREAMING_SNAKE_CASE   , max_new_tokens=__SCREAMING_SNAKE_CASE   , output_scores=__SCREAMING_SNAKE_CASE   , return_dict_in_generate=__SCREAMING_SNAKE_CASE   , output_hidden_states=__SCREAMING_SNAKE_CASE   , output_attentions=__SCREAMING_SNAKE_CASE   , **__SCREAMING_SNAKE_CASE   , )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				text_encoding_offset
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				text_pad_token
											_SCREAMING_SNAKE_CASE			:	str							=				eos_token_id
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				semantic_infer_token
											_SCREAMING_SNAKE_CASE			:	int							=				semantic_vocab_size
											_SCREAMING_SNAKE_CASE			:	Dict							=				max_input_semantic_length
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				semantic_rate_hz





class    lowercase__  (						UpperCamelCase_ ):





							'''simple docstring'''


							A_							:				List[Any]				     =  """coarse_acoustics"""






							def __init__(   self   , __snake_case=True   , __snake_case=False   , __snake_case=False   , __snake_case=False   , __snake_case=False   , __snake_case=0.7   , __snake_case=True   , __snake_case=1_2048   , __snake_case=75   , __snake_case=2   , __snake_case=1_2050   , __snake_case=256   , __snake_case = 630   , __snake_case = 60   , **__snake_case   , ):
											super().__init__(
											    temperature=__SCREAMING_SNAKE_CASE   , do_sample=__SCREAMING_SNAKE_CASE   , renormalize_logits=__SCREAMING_SNAKE_CASE   , output_scores=__SCREAMING_SNAKE_CASE   , return_dict_in_generate=__SCREAMING_SNAKE_CASE   , output_hidden_states=__SCREAMING_SNAKE_CASE   , output_attentions=__SCREAMING_SNAKE_CASE   , **__SCREAMING_SNAKE_CASE   , )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				coarse_semantic_pad_token
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				coarse_rate_hz
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				n_coarse_codebooks
											_SCREAMING_SNAKE_CASE			:	str							=				coarse_infer_token
											_SCREAMING_SNAKE_CASE			:	List[str]							=				max_coarse_input_length
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max_coarse_history
											_SCREAMING_SNAKE_CASE			:	Any							=				sliding_window_len





class    lowercase__  (						UpperCamelCase_ ):





							'''simple docstring'''


							A_							:				List[str]				     =  """fine_acoustics"""






							def __init__(   self   , __snake_case=0.5   , __snake_case=512   , __snake_case=1024   , __snake_case=8   , **__snake_case   , ):
											super().__init__(temperature=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				max_fine_history_length
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_fine_input_length
											_SCREAMING_SNAKE_CASE			:	Any							=				n_fine_codebooks





class    lowercase__  (						UpperCamelCase_ ):





							'''simple docstring'''


							A_							:				List[Any]				     =  """bark"""
							A_							:				Optional[Any]				     =  True






							def __init__(   self   , __snake_case = None   , __snake_case = None   , __snake_case = None   , __snake_case=2_4000   , __snake_case=1024   , **__snake_case   , ):
											if semantic_config is None:
															_SCREAMING_SNAKE_CASE			:	List[str]							=				{}
															logger.info("""semantic_config is None. initializing the semantic model with default values."""  )

											if coarse_acoustics_config is None:
															_SCREAMING_SNAKE_CASE			:	Dict							=				{}
															logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )

											if fine_acoustics_config is None:
															_SCREAMING_SNAKE_CASE			:	Dict							=				{}
															logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				sample_rate
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				codebook_size






							@classmethod
							def UpperCAmelCase_     (   cls   , __snake_case   , __snake_case   , __snake_case   , **__snake_case   , ):
											return cls(
											    semantic_config=semantic_config.to_dict()   , coarse_acoustics_config=coarse_acoustics_config.to_dict()   , fine_acoustics_config=fine_acoustics_config.to_dict()   , **__SCREAMING_SNAKE_CASE   , )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				copy.deepcopy(self.__dict__  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.semantic_config.to_dict()
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.coarse_acoustics_config.to_dict()
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.fine_acoustics_config.to_dict()

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.__class__.model_type
											return output



'''simple docstring'''


import bsa
import requests







def       snake_case_       (  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       snake_case_       (  SCREAMING_SNAKE_CASE__ = 5					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[str]							=				int(float(SCREAMING_SNAKE_CASE__					)					)
				if num_movies < 1:
								return ()
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				(
				    """https://www.imdb.com/search/title?title_type="""
				    f"""feature&sort=num_votes,desc&count={num_movies}"""
				)
				_SCREAMING_SNAKE_CASE			:	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

				UpperCAmelCase_		:       List[str]							       =		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)
				    )
				)




