
"""simple docstring"""





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3,	dim=0						)
   UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      List[str]      =					pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3,	dim=0						)

   UpperCAmelCase_      :      Union[str, Any]      =					pt_weights[f"""{prefix}.out_proj.weight"""]
   UpperCAmelCase_      :      Union[str, Any]      =					pt_weights[f"""{prefix}.out_proj.bias"""]

   UpperCAmelCase_      :      Optional[int]      =					q_proj
   UpperCAmelCase_      :      Dict      =					q_proj_bias

   UpperCAmelCase_      :      int      =					k_proj
   UpperCAmelCase_      :      List[str]      =					k_proj_bias

   UpperCAmelCase_      :      Tuple      =					v_proj
   UpperCAmelCase_      :      Any      =					v_proj_bias

   UpperCAmelCase_      :      Dict      =					out_proj_weights
   UpperCAmelCase_      :      List[Any]      =					out_proj_bias







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   copy_linear(hf_mlp.fca,	__lowerCamelCase,	f"""{prefix}.c_fc"""						)
   copy_linear(hf_mlp.fca,	__lowerCamelCase,	f"""{prefix}.c_proj"""						)







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   UpperCAmelCase_      :      Tuple      =					pt_weights[f"""{prefix}.weight"""].data
   UpperCAmelCase_      :      List[str]      =					pt_weights[f"""{prefix}.bias"""].data







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   # copy layer norms
   copy_linear(hf_layer.layer_norma,	__lowerCamelCase,	f"""{prefix}.ln_1"""						)
   copy_linear(hf_layer.layer_norma,	__lowerCamelCase,	f"""{prefix}.ln_2"""						)

   # copy MLP
   copy_mlp(hf_layer.mlp,	__lowerCamelCase,	f"""{prefix}.mlp"""						)

   # copy attn
   copy_attn_layer(hf_layer.self_attn,	__lowerCamelCase,	f"""{prefix}.attn"""						)







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   for layer_id, hf_layer in enumerate(__lowerCamelCase						):
      copy_layer(__lowerCamelCase,	__lowerCamelCase,	f"""{prefix}.{layer_id}"""						)







def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
   # copy projection
   UpperCAmelCase_      :      Tuple      =					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 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
   # copy projection
   UpperCAmelCase_      :      Union[str, Any]      =					pt_weights["visual.proj"].data.T

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

   # copy embeddings
   UpperCAmelCase_      :      str      =					pt_weights["visual.conv1.weight"].data
   UpperCAmelCase_      :      List[Any]      =					pt_weights["visual.class_embedding"].data
   UpperCAmelCase_      :      Any      =					pt_weights["visual.positional_embedding"].data

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







@torch.no_grad()
def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase=None						):
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   UpperCAmelCase_      :      Optional[int]      =					ChineseCLIPConfig.from_pretrained(__lowerCamelCase						)

   UpperCAmelCase_      :      Dict      =					ChineseCLIPModel(__lowerCamelCase						).eval()

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

   copy_text_model_and_projection(__lowerCamelCase,	__lowerCamelCase						)
   copy_vision_model_and_projection(__lowerCamelCase,	__lowerCamelCase						)
   UpperCAmelCase_      :      Any      =					pt_weights["logit_scale"].data

   hf_model.save_pretrained(__lowerCamelCase						)


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"""





from __future__ import annotations

from statistics import mean







def 			__a  (	__lowerCamelCase						):
   UpperCAmelCase_      :      Dict      =					2
   UpperCAmelCase_      :      Any      =					list(__lowerCamelCase						)
   UpperCAmelCase_      :      List[Any]      =					[0] * len(__lowerCamelCase						)
   UpperCAmelCase_      :      str      =					0
   while True:
      UpperCAmelCase_      :      int      =					True
      for i, burst_time in enumerate(__lowerCamelCase						):
         if rem_burst_times[i] > 0:
            UpperCAmelCase_      :      List[Any]      =					False
            if rem_burst_times[i] > quantum:
               t += quantum
               rem_burst_times[i] -= quantum
            else:
               t += rem_burst_times[i]
               UpperCAmelCase_      :      Optional[int]      =					t - burst_time
               UpperCAmelCase_      :      Union[str, Any]      =					0
      if done is True:
         return waiting_times







def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
   return [burst + waiting for burst, waiting in zip(__lowerCamelCase,	__lowerCamelCase						)]


if __name__ == "__main__":
    _a	 =							[3, 5, 7]
    _a	 =							calculate_waiting_times(burst_times)
    _a	 =							calculate_turn_around_times(burst_times, waiting_times)
    print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
    for i, burst_time in enumerate(burst_times):
        print(
            f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
            f"""{turn_around_times[i]}"""
        )
    print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
    print(f"""Average turn around time = {mean(turn_around_times):.5f}""")


