
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]					):


					'''simple docstring'''

					lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Union[str, Any]							=       pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3							,					dim=0					)
					lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Union[str, Any]							=       pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3							,					dim=0					)

					lowerCAmelCase						:   int							=       pt_weights[f'''{prefix}.out_proj.weight''']
					lowerCAmelCase						:   Dict							=       pt_weights[f'''{prefix}.out_proj.bias''']

					lowerCAmelCase						:   Optional[Any]							=       q_proj
					lowerCAmelCase						:   Optional[int]							=       q_proj_bias

					lowerCAmelCase						:   List[Any]							=       k_proj
					lowerCAmelCase						:   int							=       k_proj_bias

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

					lowerCAmelCase						:   Optional[Any]							=       out_proj_weights
					lowerCAmelCase						:   Optional[Any]							=       out_proj_bias
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Tuple							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					copy_linear(hf_mlp.fca							,					lowercase__							,					f'''{prefix}.c_fc'''					)
					copy_linear(hf_mlp.fca							,					lowercase__							,					f'''{prefix}.c_proj'''					)
def       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       Tuple							,					__magic_name__  :       str					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       pt_weights[f'''{prefix}.weight'''].data
					lowerCAmelCase						:   Union[str, Any]							=       pt_weights[f'''{prefix}.bias'''].data
def       UpperCAmelCase__							(    __magic_name__  :       Optional[int]							,					__magic_name__  :       Optional[int]							,					__magic_name__  :       str					):


					'''simple docstring'''

					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       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					for layer_id, hf_layer in enumerate(lowercase__					):
										copy_layer(lowercase__							,					lowercase__							,					f'''{prefix}.{layer_id}'''					)
def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       Any					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       pt_weights['''text_projection'''].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										lowerCAmelCase						:   Optional[int]							=       pt_weights[f'''bert.{name}'''].data
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       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
					lowerCAmelCase						:   int							=       pt_weights['''visual.conv1.weight'''].data
					lowerCAmelCase						:   Dict							=       pt_weights['''visual.class_embedding'''].data
					lowerCAmelCase						:   str							=       pt_weights['''visual.positional_embedding'''].data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers							,					lowercase__							,					'''visual.transformer.resblocks'''					)
@torch.no_grad()
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       List[Any]=None					):


					'''simple docstring'''

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

					lowerCAmelCase						:   int							=       ChineseCLIPModel(lowercase__					).eval()

					lowerCAmelCase						:   Optional[Any]							=       torch.load(lowercase__							,					map_location='''cpu'''					)['''state_dict''']
					lowerCAmelCase						:   Union[str, Any]							=       {(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__					)
					lowerCAmelCase						:   Optional[Any]							=       pt_weights['''logit_scale'''].data

					hf_model.save_pretrained(lowercase__					)


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Any         =       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.'
		)
		__SCREAMING_SNAKE_CASE     :				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!')


import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__SCREAMING_SNAKE_CASE     :				List[Any]             =       logging.get_logger(__name__)


class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					*lowerCamelCase__      :		Dict  ,					**lowerCamelCase__      :		int			):
							warnings.warn(
							    '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use ConditionalDetrImageProcessor instead.'''  ,					lowerCamelCase__  ,					)
							super().__init__(*lowerCamelCase__  ,					**lowerCamelCase__			)
