



"""simple docstring"""







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase     ,  _lowercase     ,  _lowercase				: str     =   pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3   ,				dim=0      )
		_lowercase     ,  _lowercase     ,  _lowercase				: Optional[Any]     =   pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3   ,				dim=0      )

		_lowercase				: Optional[int]     =   pt_weights[F"""{prefix}.out_proj.weight"""]
		_lowercase				: Union[str, Any]     =   pt_weights[F"""{prefix}.out_proj.bias"""]

		_lowercase				: Union[str, Any]     =   q_proj
		_lowercase				: List[str]     =   q_proj_bias

		_lowercase				: Any     =   k_proj
		_lowercase				: int     =   k_proj_bias

		_lowercase				: int     =   v_proj
		_lowercase				: List[Any]     =   v_proj_bias

		_lowercase				: str     =   out_proj_weights
		_lowercase				: str     =   out_proj_bias


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		copy_linear(hf_mlp.fca   ,				__UpperCAmelCase   ,				F"""{prefix}.c_fc"""      )
		copy_linear(hf_mlp.fca   ,				__UpperCAmelCase   ,				F"""{prefix}.c_proj"""      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: List[Any]     =   pt_weights[F"""{prefix}.weight"""].data
		_lowercase				: List[str]     =   pt_weights[F"""{prefix}.bias"""].data


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		# copy layer norms
		copy_linear(hf_layer.layer_norma   ,				__UpperCAmelCase   ,				F"""{prefix}.ln_1"""      )
		copy_linear(hf_layer.layer_norma   ,				__UpperCAmelCase   ,				F"""{prefix}.ln_2"""      )

		# copy MLP
		copy_mlp(hf_layer.mlp   ,				__UpperCAmelCase   ,				F"""{prefix}.mlp"""      )

		# copy attn
		copy_attn_layer(hf_layer.self_attn   ,				__UpperCAmelCase   ,				F"""{prefix}.attn"""      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		for layer_id, hf_layer in enumerate(__UpperCAmelCase      ):
				copy_layer(__UpperCAmelCase   ,				__UpperCAmelCase   ,				F"""{prefix}.{layer_id}"""      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		# copy projection
		_lowercase				: List[str]     =   pt_weights["""text_projection"""].data.T

		# copy text encoder
		for name, param in hf_model.text_model.named_parameters():
				_lowercase				: Any     =   pt_weights[F"""bert.{name}"""].data


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		# copy projection
		_lowercase				: Any     =   pt_weights["""visual.proj"""].data.T

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

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

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


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

		_lowercase				: Union[str, Any]     =   ChineseCLIPModel(__UpperCAmelCase      ).eval()

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

		copy_text_model_and_projection(__UpperCAmelCase   ,				__UpperCAmelCase      )
		copy_vision_model_and_projection(__UpperCAmelCase   ,				__UpperCAmelCase      )
		_lowercase				: int     =   pt_weights["""logit_scale"""].data

		hf_model.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
		UpperCAmelCase:  List[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."""
		)
		UpperCAmelCase:  Tuple			     =				parser.parse_args()

		convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
		print("""The conversion is finished!""")







import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

