






import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 				A	(			a_				,a_				,a_   )	->		Dict:
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   int									=pt_weights[F'{prefix}.in_proj_weight'].chunk(3				,dim=0   )
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Tuple									=pt_weights[F'{prefix}.in_proj_bias'].chunk(3				,dim=0   )

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

					__UpperCamelCase :   List[str]									=q_proj
					__UpperCamelCase :   int									=q_proj_bias

					__UpperCamelCase :   Optional[int]									=k_proj
					__UpperCamelCase :   Optional[Any]									=k_proj_bias

					__UpperCamelCase :   Any									=v_proj
					__UpperCamelCase :   List[str]									=v_proj_bias

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






def 				A	(			a_				,a_				,a_   )	->		str:
					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 				A	(			a_				,a_				,a_   )	->		List[str]:
					__UpperCamelCase :   Tuple									=pt_weights[F'{prefix}.weight'].data
					__UpperCamelCase :   Union[str, Any]									=pt_weights[F'{prefix}.bias'].data






def 				A	(			a_				,a_				,a_   )	->		Optional[Any]:
					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 				A	(			a_				,a_				,a_   )	->		Optional[int]:


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






def 				A	(			a_				,a_   )	->		Dict:
					__UpperCamelCase :   Dict									=pt_weights['text_projection'].data.T

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






def 				A	(			a_				,a_   )	->		List[Any]:
					__UpperCamelCase :   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
					__UpperCamelCase :   List[str]									=pt_weights['visual.conv1.weight'].data
					__UpperCamelCase :   Union[str, Any]									=pt_weights['visual.class_embedding'].data
					__UpperCamelCase :   Dict									=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 				A	(			a_				,a_				,a_=None   )	->		str:
					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					__UpperCamelCase :   List[str]									=ChineseCLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )

					__UpperCamelCase :   Dict									=ChineseCLIPModel(_SCREAMING_SNAKE_CASE   ).eval()

					__UpperCamelCase :   Any									=torch.load(_SCREAMING_SNAKE_CASE				,map_location='cpu'   )['state_dict']
					__UpperCamelCase :   Optional[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   )
					__UpperCamelCase :   List[Any]									=pt_weights['logit_scale'].data

					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							A_			:str	    =		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.'''
							)
							A_			:List[Any]	    =		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"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






