
'''simple docstring'''
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				int:


		'''simple docstring'''


		snake_case       ,snake_case       ,snake_case   :    List[str]			       =		pt_weights[F'{prefix}.in_proj_weight'].chunk(3      ,    dim=0       )
		snake_case       ,snake_case       ,snake_case   :    Any			       =		pt_weights[F'{prefix}.in_proj_bias'].chunk(3      ,    dim=0       )

		snake_case   :    Optional[Any]			       =		pt_weights[F'{prefix}.out_proj.weight']
		snake_case   :    Union[str, Any]			       =		pt_weights[F'{prefix}.out_proj.bias']

		snake_case   :    Optional[int]			       =		q_proj
		snake_case   :    Dict			       =		q_proj_bias

		snake_case   :    Tuple			       =		k_proj
		snake_case   :    Union[str, Any]			       =		k_proj_bias

		snake_case   :    int			       =		v_proj
		snake_case   :    Optional[int]			       =		v_proj_bias

		snake_case   :    List[str]			       =		out_proj_weights
		snake_case   :    str			       =		out_proj_bias




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				str:


		'''simple docstring'''


		copy_linear(hf_mlp.fca      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.c_fc'       )
		copy_linear(hf_mlp.fca      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.c_proj'       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		pt_weights[F'{prefix}.weight'].data
		snake_case   :    Tuple			       =		pt_weights[F'{prefix}.bias'].data




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				str:


		'''simple docstring'''


		copy_linear(hf_layer.layer_norma      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.ln_1'       )
		copy_linear(hf_layer.layer_norma      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.ln_2'       )

		# copy MLP
		copy_mlp(hf_layer.mlp      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.mlp'       )

		# copy attn
		copy_attn_layer(hf_layer.self_attn      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.attn'       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		for layer_id, hf_layer in enumerate(SCREAMING_SNAKE_CASE__       ):
				copy_layer(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    F'{prefix}.{layer_id}'       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    Optional[Any]			       =		pt_weights['''text_projection'''].data.T

		# copy text encoder
		for name, param in hf_model.text_model.named_parameters():
				snake_case   :    List[Any]			       =		pt_weights[F'bert.{name}'].data




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[Any]			       =		pt_weights['''visual.proj'''].data.T

		# copy layer norms
		copy_linear(hf_model.vision_model.pre_layrnorm      ,    SCREAMING_SNAKE_CASE__      ,    '''visual.ln_pre'''       )
		copy_linear(hf_model.vision_model.post_layernorm      ,    SCREAMING_SNAKE_CASE__      ,    '''visual.ln_post'''       )

		# copy embeddings
		snake_case   :    Dict			       =		pt_weights['''visual.conv1.weight'''].data
		snake_case   :    Union[str, Any]			       =		pt_weights['''visual.class_embedding'''].data
		snake_case   :    Optional[int]			       =		pt_weights['''visual.positional_embedding'''].data

		# copy encoder
		copy_layers(hf_model.vision_model.encoder.layers      ,    SCREAMING_SNAKE_CASE__      ,    '''visual.transformer.resblocks'''       )




@torch.no_grad()
def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=None       )   ->				Union[str, Any]:


		'''simple docstring'''


		assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
		snake_case   :    int			       =		ChineseCLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )

		snake_case   :    Tuple			       =		ChineseCLIPModel(SCREAMING_SNAKE_CASE__       ).eval()

		snake_case   :    Dict			       =		torch.load(SCREAMING_SNAKE_CASE__      ,    map_location='''cpu'''       )['''state_dict''']
		snake_case   :    Any			       =		{(name[7:] if name.startswith('''module.'''       ) else name): value for name, value in pt_weights.items()}

		copy_text_model_and_projection(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
		copy_vision_model_and_projection(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[str]			       =		pt_weights['''logit_scale'''].data

		hf_model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
						lowercase__		    =	argparse.ArgumentParser()
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    help="Path to the output folder storing converted hf PyTorch model.",
						)
						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
						)
						parser.add_argument(
						    "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
						)
						lowercase__		    =	parser.parse_args()

						convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
						print("The conversion is finished!")




'''simple docstring'''
import bsa
import requests




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				dict[str, str]:


		'''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       )   ->				tuple:


		'''simple docstring'''


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

						lowercase__		    =	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)
						    )
						)


