





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







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

					return name







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Tuple:
					for key in orig_state_dict.copy().keys():
										__A	:						int       =				orig_state_dict.pop(a  )

										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	:						Tuple       =				key.split('.'  )
															__A    ,				__A	:						Union[str, Any]       =				int(key_split[2]  ), int(key_split[4]  )
															__A	:						List[Any]       =				config.vision_config.hidden_size
															if "weight" in key:
																				__A	:						Union[str, Any]       =				val[:dim, :]
																				__A	:						Any       =				val[dim : dim * 2, :]
																				__A	:						List[str]       =				val[-dim:, :]
															else:
																				__A	:						Optional[Any]       =				val[:dim]
																				__A	:						Dict       =				val[dim : dim * 2]
																				__A	:						Union[str, Any]       =				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	:						int       =				key.split('.'  )
															__A	:						Optional[int]       =				int(key_split[3]  )
															__A	:						int       =				config.text_config.hidden_size
															if "weight" in key:
																				__A	:						int       =				val[:dim, :]
																				__A	:						Tuple       =				val[
																				    dim : dim * 2, :
																				]
																				__A	:						Optional[int]       =				val[-dim:, :]
															else:
																				__A	:						Union[str, Any]       =				val[:dim]
																				__A	:						Optional[int]       =				val[dim : dim * 2]
																				__A	:						Any       =				val[-dim:]
										else:
															__A	:						Optional[int]       =				rename_key(a  )
															# 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	:						Optional[Any]       =				val.squeeze_()
															else:
																				__A	:						Optional[int]       =				val

					return orig_state_dict







def  _SCREAMING_SNAKE_CASE				(   )      ->      Dict:
					__A	:						Optional[Any]       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
					__A	:						Union[str, Any]       =				Image.open(requests.get(a					,						stream=a  ).raw  )
					return im







@torch.no_grad()
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a="groupvit-gcc-yfcc"					,						a=False  )      ->      Optional[int]:
					__A	:						Dict       =				GroupViTConfig()
					__A	:						Any       =				GroupViTModel(a  ).eval()

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

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

					with torch.no_grad():
										__A	:						List[Any]       =				model(**a  )

					if model_name == "groupvit-gcc-yfcc":
										__A	:						str       =				torch.tensor([[13.3_523, 6.3_629]]  )
					elif model_name == "groupvit-gcc-redcaps":
										__A	:						Dict       =				torch.tensor([[16.1_873, 8.6_230]]  )
					else:
										raise ValueError(F"""Model name {model_name} not supported."""  )
					assert torch.allclose(outputs.logits_per_image					,						a					,						atol=1e-3  )

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

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


if __name__ == "__main__":
				UpperCAmelCase						:   Optional[int]			  =     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						:   Tuple			  =     parser.parse_args()

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











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




