import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def      lowercase_							(     _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  List[Any]          =							pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3						,      dim=0)
    lowercase__					:  Dict          =							pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3						,      dim=0)

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

    lowercase__					:  Dict          =							q_proj
    lowercase__					:  Any          =							q_proj_bias

    lowercase__					:  str          =							k_proj
    lowercase__					:  int          =							k_proj_bias

    lowercase__					:  str          =							v_proj
    lowercase__					:  str          =							v_proj_bias

    lowercase__					:  Any          =							out_proj_weights
    lowercase__					:  Optional[int]          =							out_proj_bias

def      lowercase_							(     _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			int):
    copy_linear(hf_mlp.fca						,      _lowerCamelCase						,      f'''{prefix}.c_fc''')
    copy_linear(hf_mlp.fca						,      _lowerCamelCase						,      f'''{prefix}.c_proj''')

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  Union[str, Any]          =							pt_weights[f'''{prefix}.weight'''].data
    lowercase__					:  List[Any]          =							pt_weights[f'''{prefix}.bias'''].data

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			int):
    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      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Any):


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

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Dict):
    lowercase__					:  Union[str, Any]          =							pt_weights['''text_projection'''].data.T

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

def      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  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
    lowercase__					:  Dict          =							pt_weights['''visual.conv1.weight'''].data
    lowercase__					:  Dict          =							pt_weights['''visual.class_embedding'''].data
    lowercase__					:  List[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      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			Optional[Any]=None):
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    lowercase__					:  List[Any]          =							ChineseCLIPConfig.from_pretrained(_lowerCamelCase)

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

    lowercase__					:  List[str]          =							torch.load(_lowerCamelCase						,      map_location="cpu")['''state_dict''']
    lowercase__					:  Tuple          =							{(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)
    lowercase__					:  Optional[int]          =							pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(_lowerCamelCase)


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!''')










'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Optional[int]:
					super().__init__(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Union[str, Any]				=  (
					    next_prime(value % self.size_table			)
					    if not is_prime(value % self.size_table			)
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					return (increment * self.__hash_function_a(UpperCAmelCase__   ,     UpperCAmelCase__			)) % self.size_table




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					_A :				Optional[int]				=  1
					_A :				Optional[int]				=  self.hash_function(UpperCAmelCase__			)

					while self.values[new_key] is not None and self.values[new_key] != key:
									_A :				int				=  (
									    self.__hash_double_function(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									    if self.balanced_factor() >= self.lim_charge
									    else None
									)
									if new_key is None:
													break
									else:
													i += 1

					return new_key
