



'''simple docstring'''



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__	,  lowercase__	,  lowercase__								=    pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3						,						dim=0		)
						lowercase__	,  lowercase__	,  lowercase__								=    pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3						,						dim=0		)

						lowercase__								=    pt_weights[f'''{prefix}.out_proj.weight''']
						lowercase__								=    pt_weights[f'''{prefix}.out_proj.bias''']

						lowercase__								=    q_proj
						lowercase__								=    q_proj_bias

						lowercase__								=    k_proj
						lowercase__								=    k_proj_bias

						lowercase__								=    v_proj
						lowercase__								=    v_proj_bias

						lowercase__								=    out_proj_weights
						lowercase__								=    out_proj_bias





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						copy_linear(hf_mlp.fca						,						lowercase__						,						f'''{prefix}.c_fc'''		)
						copy_linear(hf_mlp.fca						,						lowercase__						,						f'''{prefix}.c_proj'''		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    pt_weights[f'''{prefix}.weight'''].data
						lowercase__								=    pt_weights[f'''{prefix}.bias'''].data





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						# copy layer norms
						copy_linear(hf_layer.layer_norma						,						lowercase__						,						f'''{prefix}.ln_1'''		)
						copy_linear(hf_layer.layer_norma						,						lowercase__						,						f'''{prefix}.ln_2'''		)

						# copy MLP
						copy_mlp(hf_layer.mlp						,						lowercase__						,						f'''{prefix}.mlp'''		)

						# copy attn
						copy_attn_layer(hf_layer.self_attn						,						lowercase__						,						f'''{prefix}.attn'''		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						for layer_id, hf_layer in enumerate(lowercase__		):
												copy_layer(lowercase__						,						lowercase__						,						f'''{prefix}.{layer_id}'''		)





def 	_A						(							lowercase__						,						lowercase__		):
						# copy projection
						lowercase__								=    pt_weights["""text_projection"""].data.T

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





def 	_A						(							lowercase__						,						lowercase__		):
						# copy projection
						lowercase__								=    pt_weights["""visual.proj"""].data.T

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

						# copy embeddings
						lowercase__								=    pt_weights["""visual.conv1.weight"""].data
						lowercase__								=    pt_weights["""visual.class_embedding"""].data
						lowercase__								=    pt_weights["""visual.positional_embedding"""].data

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





@torch.no_grad()
def 	_A						(							lowercase__						,						lowercase__						,						lowercase__=None		):
						assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
						lowercase__								=    ChineseCLIPConfig.from_pretrained(lowercase__		)

						lowercase__								=    ChineseCLIPModel(lowercase__		).eval()

						lowercase__								=    torch.load(lowercase__						,						map_location="""cpu"""		)["""state_dict"""]
						lowercase__								=    {(name[7:] if name.startswith("""module."""		) else name): value for name, value in pt_weights.items()}

						copy_text_model_and_projection(lowercase__						,						lowercase__		)
						copy_vision_model_and_projection(lowercase__						,						lowercase__		)
						lowercase__								=    pt_weights["""logit_scale"""].data

						hf_model.save_pretrained(lowercase__		)


if __name__ == "__main__":
				__A       =      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       =      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						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






