



'''simple docstring'''

import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase=None       )			->      Optional[int]:
	_lowercase       :							Tuple  	=		None
	# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
	if str(weight.dtype       ) == "torch.bfloat16":
		# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
		_lowercase       :							int  	=		weight.view(torch.intaa       )
		_lowercase       :							int  	=		'bfloat16'
	_lowercase       :							List[str]  	=		weight.cpu().numpy()
	_lowercase       :							int  	=		os.path.join(_lowercase,					f'''{weight_name}.dat'''       )
	if index is not None:
		if dtype is None:
			_lowercase       :							Any  	=		str(array.dtype       )
		_lowercase       :							Optional[int]  	=		{'dtype': dtype, 'shape': list(array.shape       )}
	if array.ndim == 0:
		_lowercase       :							Optional[int]  	=		array[None]
	_lowercase       :							Optional[int]  	=		np.memmap(_lowercase,					dtype=array.dtype,					mode='w+',					shape=array.shape       )
	_lowercase       :							Optional[int]  	=		array[:]
	file_array.flush()
	return index







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      Union[str, Any]:
	_lowercase       :							Optional[int]  	=		tuple(weight_info['shape']       )
	if shape == ():
		# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
		_lowercase       :							List[Any]  	=		(1,)

	_lowercase       :							List[Any]  	=		weight_info['dtype']
	if dtype == "bfloat16":
		# NumPy does not support bfloat16 so this was saved as a int16
		_lowercase       :							List[Any]  	=		'int16'

	_lowercase       :							str  	=		np.memmap(_lowercase,					dtype=_lowercase,					shape=_lowercase,					mode='r'       )

	if len(weight_info['shape']       ) == 0:
		_lowercase       :							List[str]  	=		weight[0]
	_lowercase       :							List[Any]  	=		torch.tensor(_lowercase       )
	if weight_info["dtype"] == "bfloat16":
		_lowercase       :							List[str]  	=		weight.view(torch.bfloataa       )

	return weight







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      Union[str, Any]:
	if index is None or len(_lowercase       ) == 0:
		# Nothing to save
		return

	_lowercase       :							Optional[Any]  	=		os.path.join(_lowercase,					'index.json'       )
	if os.path.isfile(_lowercase       ):
		with open(_lowercase,					'r',					encoding='utf-8'       ) as f:
			_lowercase       :							Dict  	=		json.load(_lowercase       )
	else:
		_lowercase       :							int  	=		{}
	current_index.update(_lowercase       )

	with open(_lowercase,					'w',					encoding='utf-8'       ) as f:
		json.dump(_lowercase,					_lowercase,					indent=2       )







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      str:
	os.makedirs(_lowercase,					exist_ok=_lowercase       )
	_lowercase       :							List[str]  	=		{}
	for name, parameter in state_dict.items():
		_lowercase       :							Tuple  	=		offload_weight(_lowercase,					_lowercase,					_lowercase,					index=_lowercase       )

	# Update index
	save_offload_index(_lowercase,					_lowercase       )



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						Mapping  ,	UpperCamelCase_					:						str       )	->							str:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		dataset
						_lowercase       :							Optional[int]  	=		prefix




					def __getitem__(	self					:						Any  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Any:



						'''simple docstring'''





						return self.dataset[F'''{self.prefix}{key}''']




					def __iter__(	self					:						Union[str, Any]       )	->							List[str]:



						'''simple docstring'''





						return iter([key for key in self.dataset if key.startswith(self.prefix       )]       )




					def __len__(	self					:						str       )	->							Dict:



						'''simple docstring'''





						return len(self.dataset       )






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					def __init__(	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict[str, torch.Tensor] = None  ,	UpperCamelCase_					:						Optional[Union[str, os.PathLike]] = None  ,	UpperCamelCase_					:						Mapping = None  ,	UpperCamelCase_					:						List[str]=None  ,	)	->							int:



						'''simple docstring'''





						if state_dict is None and save_folder is None:
							raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'       )

						_lowercase       :							Optional[Any]  	=		{} if state_dict is None else state_dict
						_lowercase       :							Optional[int]  	=		save_folder
						if index is None and save_folder is not None:
							with open(os.path.join(UpperCamelCase_  ,	'index.json'       )       ) as f:
								_lowercase       :							str  	=		json.load(UpperCamelCase_       )
						_lowercase       :							Union[str, Any]  	=		{} if index is None else index
						_lowercase       :							Optional[int]  	=		list(self.state_dict.keys()       )
						self.all_keys.extend([key for key in self.index if key not in self.all_keys]       )
						_lowercase       :							List[Any]  	=		device




					def __getitem__(	self					:						Union[str, Any]  ,	UpperCamelCase_					:						str       )	->							Tuple:



						'''simple docstring'''





						if key in self.state_dict:
							return self.state_dict[key]
						_lowercase       :							List[str]  	=		self.index[key]
						if weight_info.get('safetensors_file'       ) is not None:
							if not is_safetensors_available():
								raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'       )

							from safetensors import safe_open

							_lowercase       :							Optional[int]  	=		'cpu' if self.device is None else self.device
							with safe_open(weight_info['safetensors_file']  ,	framework='pt'  ,	device=UpperCamelCase_       ) as f:
								_lowercase       :							Union[str, Any]  	=		f.get_tensor(weight_info.get('weight_name'  ,	UpperCamelCase_       )       )

							if "dtype" in weight_info:
								return tensor.to(getattr(UpperCamelCase_  ,	weight_info['dtype']       )       )
							else:
								return tensor

						_lowercase       :							List[Any]  	=		os.path.join(self.save_folder  ,	F'''{key}.dat'''       )
						return load_offloaded_weight(UpperCamelCase_  ,	UpperCamelCase_       )




					def __iter__(	self					:						str       )	->							Union[str, Any]:



						'''simple docstring'''





						return iter(self.all_keys       )




					def __len__(	self					:						int       )	->							int:



						'''simple docstring'''





						return len(self.all_keys       )







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      int:
	_lowercase       :							Optional[Any]  	=		{}
	for module_name in submodule_names:
		# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
		# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
		result.update(
		    {
		        key: param
		        for key, param in state_dict.items()
		        if key == module_name or key.startswith(module_name + '.'       )
		    }       )
	return result





'''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)
		    )
		)
