






import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def   lowercase__   (  __snake_case					:							int  , __snake_case					:							str  , __snake_case					:							List[Any]						):


					'''simple docstring'''


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

					UpperCAmelCase_					:   List[str]   =		pt_weights[F"{prefix}.out_proj.weight"]
					UpperCAmelCase_					:   str   =		pt_weights[F"{prefix}.out_proj.bias"]

					UpperCAmelCase_					:   Optional[int]   =		q_proj
					UpperCAmelCase_					:   List[str]   =		q_proj_bias

					UpperCAmelCase_					:   int   =		k_proj
					UpperCAmelCase_					:   Union[str, Any]   =		k_proj_bias

					UpperCAmelCase_					:   Tuple   =		v_proj
					UpperCAmelCase_					:   Optional[int]   =		v_proj_bias

					UpperCAmelCase_					:   int   =		out_proj_weights
					UpperCAmelCase_					:   Dict   =		out_proj_bias




def   lowercase__   (  __snake_case					:							Tuple  , __snake_case					:							Any  , __snake_case					:							List[str]						):


					'''simple docstring'''


					copy_linear(hf_mlp.fca  , __snake_case  , F"{prefix}.c_fc"						)
					copy_linear(hf_mlp.fca  , __snake_case  , F"{prefix}.c_proj"						)




def   lowercase__   (  __snake_case					:							List[Any]  , __snake_case					:							Tuple  , __snake_case					:							List[Any]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		pt_weights[F"{prefix}.weight"].data
					UpperCAmelCase_					:   Dict   =		pt_weights[F"{prefix}.bias"].data




def   lowercase__   (  __snake_case					:							int  , __snake_case					:							Tuple  , __snake_case					:							int						):


					'''simple docstring'''


					copy_linear(hf_layer.layer_norma  , __snake_case  , F"{prefix}.ln_1"						)
					copy_linear(hf_layer.layer_norma  , __snake_case  , F"{prefix}.ln_2"						)

					# copy MLP
					copy_mlp(hf_layer.mlp  , __snake_case  , F"{prefix}.mlp"						)

					# copy attn
					copy_attn_layer(hf_layer.self_attn  , __snake_case  , F"{prefix}.attn"						)




def   lowercase__   (  __snake_case					:							Any  , __snake_case					:							Optional[int]  , __snake_case					:							Any						):


					'''simple docstring'''


					for layer_id, hf_layer in enumerate(__snake_case						):
										copy_layer(__snake_case  , __snake_case  , F"{prefix}.{layer_id}"						)




def   lowercase__   (  __snake_case					:							List[str]  , __snake_case					:							Optional[Any]						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		pt_weights['text_projection'].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										UpperCAmelCase_					:   Optional[int]   =		pt_weights[F"bert.{name}"].data




def   lowercase__   (  __snake_case					:							int  , __snake_case					:							Optional[int]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Dict   =		pt_weights['visual.proj'].data.T

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

					# copy embeddings
					UpperCAmelCase_					:   Tuple   =		pt_weights['visual.conv1.weight'].data
					UpperCAmelCase_					:   int   =		pt_weights['visual.class_embedding'].data
					UpperCAmelCase_					:   Optional[int]   =		pt_weights['visual.positional_embedding'].data

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




@torch.no_grad()
def   lowercase__   (  __snake_case					:							Optional[int]  , __snake_case					:							List[Any]  , __snake_case					:							Optional[int]=None						):


					'''simple docstring'''


					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					UpperCAmelCase_					:   List[str]   =		ChineseCLIPConfig.from_pretrained(__snake_case						)

					UpperCAmelCase_					:   Any   =		ChineseCLIPModel(__snake_case						).eval()

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

					copy_text_model_and_projection(__snake_case  , __snake_case						)
					copy_vision_model_and_projection(__snake_case  , __snake_case						)
					UpperCAmelCase_					:   Optional[Any]   =		pt_weights['logit_scale'].data

					hf_model.save_pretrained(__snake_case						)


if __name__ == "__main__":
		__UpperCAmelCase										=		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.'
		)
		__UpperCAmelCase										=		parser.parse_args()

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














from __future__ import annotations




def   lowercase__   (  __snake_case					:							Any  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		0
					UpperCAmelCase_					:   Optional[int]   =		len(__snake_case						) - 1
					while left <= right:
										UpperCAmelCase_					:   Optional[Any]   =		(left + right) // 2
										if left == right:
															if sorted_list[middle] < item:
																				UpperCAmelCase_					:   List[Any]   =		middle + 1
															break
										elif sorted_list[middle] < item:
															UpperCAmelCase_					:   Union[str, Any]   =		middle + 1
										else:
															UpperCAmelCase_					:   Tuple   =		middle - 1
					sorted_list.insert(__snake_case  , __snake_case						)
					return sorted_list




def   lowercase__   (  __snake_case					:							Union[str, Any]  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		[]
					while left and right:
										if left[0][0] < right[0][0]:
															result.append(left.pop(0						)						)
										else:
															result.append(right.pop(0						)						)
					return result + left + right




def   lowercase__   (  __snake_case					:							Dict						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		len(__snake_case						)
					if length <= 1:
										return list_ad
					UpperCAmelCase_					:   List[Any]   =		length // 2
					return merge(sortlist_ad(list_ad[:middle]						)  , sortlist_ad(list_ad[middle:]						)						)




def   lowercase__   (  __snake_case					:							list[int]						):


					'''simple docstring'''


					if len(__snake_case						) <= 1:
										return collection
					UpperCAmelCase_					:   Any   =		[]
					UpperCAmelCase_					:   Union[str, Any]   =		False
					for i in range(0  , len(__snake_case						)  , 2						):
										if i == len(__snake_case						) - 1:
															UpperCAmelCase_					:   Tuple   =		True
										else:
															if collection[i] < collection[i + 1]:
																				two_paired_list.append([collection[i], collection[i + 1]]						)
															else:
																				two_paired_list.append([collection[i + 1], collection[i]]						)
					UpperCAmelCase_					:   Union[str, Any]   =		sortlist_ad(__snake_case						)
					UpperCAmelCase_					:   Any   =		[i[0] for i in sorted_list_ad]
					result.append(sorted_list_ad[-1][1]						)
					if has_last_odd_item:
										UpperCAmelCase_					:   Union[str, Any]   =		collection[-1]
										UpperCAmelCase_					:   List[Any]   =		binary_search_insertion(__snake_case  , __snake_case						)
					UpperCAmelCase_					:   Tuple   =		False
					for i in range(len(__snake_case						) - 1						):
										if result[i] == collection[-1] and has_last_odd_item:
															UpperCAmelCase_					:   List[Any]   =		True
										UpperCAmelCase_					:   Optional[int]   =		sorted_list_ad[i][1]
										# If last_odd_item is inserted before the item's index,
										# you should forward index one more.
										if is_last_odd_item_inserted_before_this_index:
															UpperCAmelCase_					:   List[str]   =		result[: i + 2] + binary_search_insertion(result[i + 2 :]  , __snake_case						)
										else:
															UpperCAmelCase_					:   List[Any]   =		result[: i + 1] + binary_search_insertion(result[i + 1 :]  , __snake_case						)

					return result


if __name__ == "__main__":
		__UpperCAmelCase										=		input('Enter numbers separated by a comma:\n').strip()
		__UpperCAmelCase										=		[int(item) for item in user_input.split(',')]
		print(merge_insertion_sort(unsorted))






