




"""simple docstring"""

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def    snake_case__						(							lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		int					,       lowerCAmelCase :		Union[str, Any]		):
					__snake_case   ,   __snake_case   ,   __snake_case        =						pt_weights[F'''{prefix}.in_proj_weight'''].chunk(3					,       dim=0		)
					__snake_case   ,   __snake_case   ,   __snake_case        =						pt_weights[F'''{prefix}.in_proj_bias'''].chunk(3					,       dim=0		)

					__snake_case        =						pt_weights[F'''{prefix}.out_proj.weight''']
					__snake_case        =						pt_weights[F'''{prefix}.out_proj.bias''']

					__snake_case        =						q_proj
					__snake_case        =						q_proj_bias

					__snake_case        =						k_proj
					__snake_case        =						k_proj_bias

					__snake_case        =						v_proj
					__snake_case        =						v_proj_bias

					__snake_case        =						out_proj_weights
					__snake_case        =						out_proj_bias




def    snake_case__						(							lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict		):
					copy_linear(hf_mlp.fca					,       snake_case__					,       F'''{prefix}.c_fc'''		)
					copy_linear(hf_mlp.fca					,       snake_case__					,       F'''{prefix}.c_proj'''		)




def    snake_case__						(							lowerCAmelCase :		Any					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		Any		):
					__snake_case        =						pt_weights[F'''{prefix}.weight'''].data
					__snake_case        =						pt_weights[F'''{prefix}.bias'''].data




def    snake_case__						(							lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Any					,       lowerCAmelCase :		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    snake_case__						(							lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Any					,       lowerCAmelCase :		Union[str, Any]		):
					for layer_id, hf_layer in enumerate(snake_case__		):
										copy_layer(snake_case__					,       snake_case__					,       F'''{prefix}.{layer_id}'''		)




def    snake_case__						(							lowerCAmelCase :		Dict					,       lowerCAmelCase :		int		):
					# copy projection
					__snake_case        =						pt_weights["""text_projection"""].data.T

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




def    snake_case__						(							lowerCAmelCase :		Dict					,       lowerCAmelCase :		Tuple		):
					# copy projection
					__snake_case        =						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
					__snake_case        =						pt_weights["""visual.conv1.weight"""].data
					__snake_case        =						pt_weights["""visual.class_embedding"""].data
					__snake_case        =						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    snake_case__						(							lowerCAmelCase :		Any					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Union[str, Any]=None		):
					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					__snake_case        =						ChineseCLIPConfig.from_pretrained(snake_case__		)

					__snake_case        =						ChineseCLIPModel(snake_case__		).eval()

					__snake_case        =						torch.load(snake_case__					,       map_location="""cpu"""		)["""state_dict"""]
					__snake_case        =						{(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__		)
					__snake_case        =						pt_weights["""logit_scale"""].data

					hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
		UpperCamelCase__			:     List[str]			       =				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__			:     Optional[Any]			       =				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

sys.set_int_max_str_digits(0)  # type: ignore





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    if not f:
        return f

    # mark first 9 numbers
    A_								=					int(str(snake_case__ )[:9] )

    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10

    # check first 9 numbers for pandigitality

    for x in range(9 ):
        if not check_front[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				)->			int:
    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers

    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    A_								=					[0] * 1000000
    A_								=					1000000000

    for x in range(1000000 ):
        A_								=					(aa + ba) % m
        A_								=					ba % m
        A_								=					ca % m
        if checka(snake_case__ ):
            A_								=					1

    for x in range(1000000 ):
        A_								=					a + b
        A_								=					b
        A_								=					c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(snake_case__ ):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
     print(f"""{solution() = }""")






