




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    copy_linear(hf_mlp.fca       ,  lowercase_       ,  F"{prefix}.c_fc"   )
    copy_linear(hf_mlp.fca       ,  lowercase_       ,  F"{prefix}.c_proj"   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    pt_weights[F"{prefix}.weight"].data
    __UpperCamelCase			=    pt_weights[F"{prefix}.bias"].data




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[Any]:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    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
    __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       ,  lowercase_       ,  '''visual.transformer.resblocks'''   )




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						List[Any]:

    """simple docstring"""


    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    __UpperCamelCase			=    ChineseCLIPConfig.from_pretrained(lowercase_   )

    __UpperCamelCase			=    ChineseCLIPModel(lowercase_   ).eval()

    __UpperCamelCase			=    torch.load(lowercase_       ,  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(lowercase_       ,  lowercase_   )
    copy_vision_model_and_projection(lowercase_       ,  lowercase_   )
    __UpperCamelCase			=    pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  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."
 )
 a_ 		=  parser.parse_args()

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













import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						,lowercase						,lowercase     :			str     						=	random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')






