


'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			if "img_encoder.pos_embed" in name:
						__A=	name.replace('img_encoder.pos_embed','vision_model.embeddings.position_embeddings' )
			if "img_encoder.patch_embed.proj" in name:
						__A=	name.replace('img_encoder.patch_embed.proj','vision_model.embeddings.patch_embeddings.projection' )
			if "img_encoder.patch_embed.norm" in name:
						__A=	name.replace('img_encoder.patch_embed.norm','vision_model.embeddings.layernorm' )
			if "img_encoder.layers" in name:
						__A=	name.replace('img_encoder.layers','vision_model.encoder.stages' )
			if "blocks" in name and "res" not in name:
						__A=	name.replace('blocks','layers' )
			if "attn" in name and "pre_assign" not in name:
						__A=	name.replace('attn','self_attn' )
			if "proj" in name and "self_attn" in name and "text" not in name:
						__A=	name.replace('proj','out_proj' )
			if "pre_assign_attn.attn.proj" in name:
						__A=	name.replace('pre_assign_attn.attn.proj','pre_assign_attn.attn.out_proj' )
			if "norm1" in name:
						__A=	name.replace('norm1','layer_norm1' )
			if "norm2" in name and "pre_assign" not in name:
						__A=	name.replace('norm2','layer_norm2' )
			if "img_encoder.norm" in name:
						__A=	name.replace('img_encoder.norm','vision_model.layernorm' )
			# text encoder
			if "text_encoder.token_embedding" in name:
						__A=	name.replace('text_encoder.token_embedding','text_model.embeddings.token_embedding' )
			if "text_encoder.positional_embedding" in name:
						__A=	name.replace('text_encoder.positional_embedding','text_model.embeddings.position_embedding.weight' )
			if "text_encoder.transformer.resblocks." in name:
						__A=	name.replace('text_encoder.transformer.resblocks.','text_model.encoder.layers.' )
			if "ln_1" in name:
						__A=	name.replace('ln_1','layer_norm1' )
			if "ln_2" in name:
						__A=	name.replace('ln_2','layer_norm2' )
			if "c_fc" in name:
						__A=	name.replace('c_fc','fc1' )
			if "c_proj" in name:
						__A=	name.replace('c_proj','fc2' )
			if "text_encoder" in name:
						__A=	name.replace('text_encoder','text_model' )
			if "ln_final" in name:
						__A=	name.replace('ln_final','final_layer_norm' )
			# projection layers
			if "img_projector.linear_hidden." in name:
						__A=	name.replace('img_projector.linear_hidden.','visual_projection.' )
			if "img_projector.linear_out." in name:
						__A=	name.replace('img_projector.linear_out.','visual_projection.3.' )
			if "text_projector.linear_hidden" in name:
						__A=	name.replace('text_projector.linear_hidden','text_projection' )
			if "text_projector.linear_out" in name:
						__A=	name.replace('text_projector.linear_out','text_projection.3' )

			return name






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			for key in orig_state_dict.copy().keys():
						__A=	orig_state_dict.pop(_SCREAMING_SNAKE_CASE )

						if "qkv" in key:
									# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
									# we need to split them up into separate matrices/vectors
									__A=	key.split('.' )
									__A,	__A=	int(key_split[2] ), int(key_split[4] )
									__A=	config.vision_config.hidden_size
									if "weight" in key:
												__A=	val[:dim, :]
												__A=	val[dim : dim * 2, :]
												__A=	val[-dim:, :]
									else:
												__A=	val[:dim]
												__A=	val[dim : dim * 2]
												__A=	val[-dim:]
						elif "in_proj" in key:
									# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
									# we need to split them up into separate matrices/vectors
									__A=	key.split('.' )
									__A=	int(key_split[3] )
									__A=	config.text_config.hidden_size
									if "weight" in key:
												__A=	val[:dim, :]
												__A=	val[
												    dim : dim * 2, :
												]
												__A=	val[-dim:, :]
									else:
												__A=	val[:dim]
												__A=	val[dim : dim * 2]
												__A=	val[-dim:]
						else:
									__A=	rename_key(_SCREAMING_SNAKE_CASE )
									# squeeze if necessary
									if (
									    "text_projection.0" in new_name
									    or "text_projection.3" in new_name
									    or "visual_projection.0" in new_name
									    or "visual_projection.3" in new_name
									):
												__A=	val.squeeze_()
									else:
												__A=	val

			return orig_state_dict






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	'http://images.cocodataset.org/val2017/000000039769.jpg'
			__A=	Image.open(requests.get(_SCREAMING_SNAKE_CASE,stream=_SCREAMING_SNAKE_CASE ).raw )
			return im






@torch.no_grad()
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Union[str, Any]="groupvit-gcc-yfcc",_SCREAMING_SNAKE_CASE					: int=False ):






			"""simple docstring"""




			__A=	GroupViTConfig()
			__A=	GroupViTModel(_SCREAMING_SNAKE_CASE ).eval()

			__A=	torch.load(_SCREAMING_SNAKE_CASE,map_location='cpu' )['model']
			__A=	convert_state_dict(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A,	__A=	model.load_state_dict(_SCREAMING_SNAKE_CASE,strict=_SCREAMING_SNAKE_CASE )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_SCREAMING_SNAKE_CASE ) == 0)

			# verify result
			__A=	CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32' )
			__A=	prepare_img()
			__A=	processor(text=['a photo of a cat', 'a photo of a dog'],images=_SCREAMING_SNAKE_CASE,padding=_SCREAMING_SNAKE_CASE,return_tensors='pt' )

			with torch.no_grad():
						__A=	model(**_SCREAMING_SNAKE_CASE )

			if model_name == "groupvit-gcc-yfcc":
						__A=	torch.tensor([[13.35_23, 6.36_29]] )
			elif model_name == "groupvit-gcc-redcaps":
						__A=	torch.tensor([[16.18_73, 8.62_30]] )
			else:
						raise ValueError(f"""Model name {model_name} not supported.""" )
			assert torch.allclose(outputs.logits_per_image,_SCREAMING_SNAKE_CASE,atol=1e-3 )

			processor.save_pretrained(_SCREAMING_SNAKE_CASE )
			model.save_pretrained(_SCREAMING_SNAKE_CASE )
			print('Successfully saved processor and model to',_SCREAMING_SNAKE_CASE )

			if push_to_hub:
						print('Pushing to the hub...' )
						processor.push_to_hub(_SCREAMING_SNAKE_CASE,organization='nielsr' )
						model.push_to_hub(_SCREAMING_SNAKE_CASE,organization='nielsr' )


if __name__ == "__main__":
					UpperCAmelCase__  			=				argparse.ArgumentParser()
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
					)
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
					parser.add_argument(
					    '''--model_name''',
					    default='''groupvit-gccy-fcc''',
					    type=str,
					    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
					)
					parser.add_argument(
					    '''--push_to_hub''',
					    action='''store_true''',
					    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
					)
					UpperCAmelCase__  			=				parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




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


if __name__ == "__main__":
					write_movies()



