



'''simple docstring'''



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Any			)		->						Optional[int]:
				snake_case__  ,      snake_case__  ,      snake_case__      :   Dict      =    pt_attn_layer.in_proj_weight.chunk(3   ,				dim=0			)
				snake_case__  ,      snake_case__  ,      snake_case__      :   List[str]      =    pt_attn_layer.in_proj_bias.chunk(3   ,				dim=0			)

				snake_case__      :   List[Any]      =    pt_attn_layer.out_proj.weight
				snake_case__      :   int      =    pt_attn_layer.out_proj.bias

				snake_case__      :   str      =    q_proj
				snake_case__      :   List[Any]      =    q_proj_bias

				snake_case__      :   Dict      =    k_proj
				snake_case__      :   Dict      =    k_proj_bias

				snake_case__      :   List[str]      =    v_proj
				snake_case__      :   List[str]      =    v_proj_bias

				snake_case__      :   List[Any]      =    out_proj_weights
				snake_case__      :   List[Any]      =    out_proj_bias


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Any			)		->						List[str]:
				copy_linear(hf_mlp.fca   ,				pt_mlp.c_fc			)
				copy_linear(hf_mlp.fca   ,				pt_mlp.c_proj			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:Tuple			)		->						Union[str, Any]:
				snake_case__      :   str      =    pt_linear.weight
				snake_case__      :   Optional[int]      =    pt_linear.bias


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Optional[Any]			)		->						Optional[int]:
				# copy layer norms
				copy_linear(hf_layer.layer_norma   ,				pt_layer.ln_a			)
				copy_linear(hf_layer.layer_norma   ,				pt_layer.ln_a			)

				# copy MLP
				copy_mlp(hf_layer.mlp   ,				pt_layer.mlp			)

				# copy attn
				copy_attn_layer(hf_layer.self_attn   ,				pt_layer.attn			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:Any			)		->						Optional[int]:
				for hf_layer, pt_layer in zip(UpperCamelCase__   ,				UpperCamelCase__			):
								copy_layer(UpperCamelCase__   ,				UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:List[str]			)		->						Dict:
				# copy  embeds
				snake_case__      :   Any      =    pt_model.token_embedding.weight
				snake_case__      :   Optional[int]      =    pt_model.positional_embedding

				# copy layer norm
				copy_linear(hf_encoder.final_layer_norm   ,				pt_model.ln_final			)

				# copy hidden layers
				copy_layers(hf_encoder.encoder.layers   ,				pt_model.transformer.resblocks			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Optional[Any]			)		->						str:
				# copy projection
				snake_case__      :   Optional[Any]      =    pt_model.text_projection.data.T

				# copy text encoder
				copy_encoder(hf_model.text_model   ,				UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:Optional[int]			)		->						Any:
				# copy projection
				snake_case__      :   int      =    pt_model.visual.proj.data.T

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

				# copy embeds
				snake_case__      :   Union[str, Any]      =    pt_model.visual.conva.weight.data
				snake_case__      :   List[str]      =    pt_model.visual.class_embedding
				snake_case__      :   Any      =    pt_model.visual.positional_embedding.data

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


@torch.no_grad()
def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:int=None			)		->						Tuple:
				if config_path is not None:
								snake_case__      :   Tuple      =    CLIPConfig.from_pretrained(UpperCamelCase__			)
				else:
								snake_case__      :   List[str]      =    CLIPConfig(projection_dim=512   ,				text_config={}   ,				vision_config={}			)

				snake_case__      :   Any      =    CLIPModel(UpperCamelCase__			).eval()

				snake_case__  ,      snake_case__      :   Optional[int]      =    load(UpperCamelCase__   ,				device='''cpu'''   ,				jit=UpperCamelCase__			)
				snake_case__      :   str      =    pt_model.eval()

				copy_text_model_and_projection(UpperCamelCase__   ,				UpperCamelCase__			)
				copy_vison_model_and_projection(UpperCamelCase__   ,				UpperCamelCase__			)
				snake_case__      :   Any      =    pt_model.logit_scale

				snake_case__      :   str      =    torch.arange(0   ,				77			).unsqueeze(0			)
				snake_case__      :   Dict      =    torch.randn(1   ,				3   ,				224   ,				224			)

				snake_case__      :   Any      =    hf_model(input_ids=UpperCamelCase__   ,				pixel_values=UpperCamelCase__   ,				return_dict=UpperCamelCase__			)
				snake_case__      :   Tuple      =    hf_outputs.logits_per_image
				snake_case__      :   Optional[int]      =    hf_outputs.logits_per_text
				snake_case__  ,      snake_case__      :   Dict      =    pt_model(UpperCamelCase__   ,				UpperCamelCase__			)

				assert torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3			)
				assert torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3			)

				hf_model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						str						=argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				_lowercase       :						Any						=parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Any:
				snake_case__      :   Dict      =    len(UpperCamelCase__			)
				for _ in range(
				    0   ,				int(((no_of_elements - 1) / 2) + 1			)			):  # we don't need to traverse to end of list as
								for j in range(0   ,				no_of_elements - 1			):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																snake_case__      :   int      =    lst[j + 1]
																snake_case__      :   Tuple      =    lst[j]
																snake_case__      :   Tuple      =    temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																snake_case__      :   Optional[Any]      =    lst[no_of_elements - 1 - j]
																snake_case__      :   Tuple      =    lst[no_of_elements - 2 - j]
																snake_case__      :   Any      =    temp
				return lst


if __name__ == "__main__":
				print("enter the list to be sorted")
				_lowercase       :						Any						=[int(x) for x in input().split()]  # inputing elements of the list in one line
				_lowercase       :						Tuple						=double_sort(lst)
				print("the sorted list is")
				print(sorted_lst)




