"""simple docstring"""


import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       int,				_snake_case		:       Optional[int]       ):
   _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   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       Dict,				_snake_case		:       Optional[int]       ):
   copy_linear(hf_mlp.fca,				_snake_case,				f"""{prefix}.c_fc"""       )
   copy_linear(hf_mlp.fca,				_snake_case,				f"""{prefix}.c_proj"""       )


def   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       Dict,				_snake_case		:       List[Any]       ):
   _lowercase							     =       pt_weights[f"""{prefix}.weight"""].data
   _lowercase							     =       pt_weights[f"""{prefix}.bias"""].data


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       str,				_snake_case		:       Optional[Any]       ):
   # copy layer norms
   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   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       Tuple,				_snake_case		:       Optional[int]       ):
   for layer_id, hf_layer in enumerate(_snake_case       ):
      copy_layer(_snake_case,				_snake_case,				f"""{prefix}.{layer_id}"""       )


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       Union[str, Any]       ):
   # 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   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int       ):
   # copy projection
   _lowercase							     =       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
   _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,				_snake_case,				"visual.transformer.resblocks"       )


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       int,				_snake_case		:       List[Any]=None       ):
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   _lowercase							     =       ChineseCLIPConfig.from_pretrained(_snake_case       )

   _lowercase							     =       ChineseCLIPModel(_snake_case       ).eval()

   _lowercase							     =       torch.load(_snake_case,				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(_snake_case,				_snake_case       )
   copy_vision_model_and_projection(_snake_case,				_snake_case       )
   _lowercase							     =       pt_weights["logit_scale"].data

   hf_model.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		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       :    List[str]         =		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"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)