






"""simple docstring"""







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	pt_weights[F'''{prefix}.in_proj_weight'''].chunk(3		,					dim=0						)
       UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	pt_weights[F'''{prefix}.in_proj_bias'''].chunk(3		,					dim=0						)

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

       UpperCAmelCase					=	q_proj
       UpperCAmelCase					=	q_proj_bias

       UpperCAmelCase					=	k_proj
       UpperCAmelCase					=	k_proj_bias

       UpperCAmelCase					=	v_proj
       UpperCAmelCase					=	v_proj_bias

       UpperCAmelCase					=	out_proj_weights
       UpperCAmelCase					=	out_proj_bias






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       copy_linear(hf_mlp.fca		,					lowerCAmelCase		,					F'''{prefix}.c_fc'''						)
       copy_linear(hf_mlp.fca		,					lowerCAmelCase		,					F'''{prefix}.c_proj'''						)






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	pt_weights[F'''{prefix}.weight'''].data
       UpperCAmelCase					=	pt_weights[F'''{prefix}.bias'''].data






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # copy layer norms
       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 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for layer_id, hf_layer in enumerate(lowerCAmelCase						):
              copy_layer(lowerCAmelCase		,					lowerCAmelCase		,					F'''{prefix}.{layer_id}'''						)






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # copy projection
       UpperCAmelCase					=	pt_weights["""text_projection"""].data.T

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






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # copy projection
       UpperCAmelCase					=	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
       UpperCAmelCase					=	pt_weights["""visual.conv1.weight"""].data
       UpperCAmelCase					=	pt_weights["""visual.class_embedding"""].data
       UpperCAmelCase					=	pt_weights["""visual.positional_embedding"""].data

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






@torch.no_grad()
def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None						):







       '''simple docstring'''
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       UpperCAmelCase					=	ChineseCLIPConfig.from_pretrained(lowerCAmelCase						)

       UpperCAmelCase					=	ChineseCLIPModel(lowerCAmelCase						).eval()

       UpperCAmelCase					=	torch.load(lowerCAmelCase		,					map_location="""cpu"""						)["""state_dict"""]
       UpperCAmelCase					=	{(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						)
       UpperCAmelCase					=	pt_weights["""logit_scale"""].data

       hf_model.save_pretrained(lowerCAmelCase						)


if __name__ == "__main__":
 lowerCAmelCase_		:			Optional[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.'''
 )
 lowerCAmelCase_		:			Optional[int]				   =					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 cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     UpperCAmelCase					=	gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row, col - 1]
                            UpperCAmelCase					=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row + 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col]
                            UpperCAmelCase					=	sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   UpperCAmelCase					=	sobel_grad[row, col]

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0]						):
              for col in range(1		,					image_shape[1]						):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowerCAmelCase_		:			Optional[Any]				   =					cva.imread(R'''../image_data/lena.jpg''', 0)
 # canny edge detection
 lowerCAmelCase_		:			Dict				   =					canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)
