






"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:Tuple     							=       CLIPSegTextConfig()
    UpperCamelCase	:Union[str, Any]     							=       CLIPSegVisionConfig(patch_size=16	)

    UpperCamelCase	:Dict     							=       True if '''refined''' in model_name else False
    UpperCamelCase	:str     							=       16 if '''rd16''' in model_name else 64

    UpperCamelCase	:Tuple     							=       CLIPSegConfig.from_text_vision_configs(
        SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__					,  reduce_dim=SCREAMING_SNAKE_CASE__					,  )
    return config


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]	):







    if "clip_model" in name:
        UpperCamelCase	:List[str]     							=       name.replace('''clip_model'''					,  '''clip'''	)
    if "transformer" in name:
        if "visual" in name:
            UpperCamelCase	:Dict     							=       name.replace('''visual.transformer'''					,  '''vision_model'''	)
        else:
            UpperCamelCase	:Union[str, Any]     							=       name.replace('''transformer'''					,  '''text_model'''	)
    if "resblocks" in name:
        UpperCamelCase	:Dict     							=       name.replace('''resblocks'''					,  '''encoder.layers'''	)
    if "ln_1" in name:
        UpperCamelCase	:Dict     							=       name.replace('''ln_1'''					,  '''layer_norm1'''	)
    if "ln_2" in name:
        UpperCamelCase	:Union[str, Any]     							=       name.replace('''ln_2'''					,  '''layer_norm2'''	)
    if "c_fc" in name:
        UpperCamelCase	:str     							=       name.replace('''c_fc'''					,  '''fc1'''	)
    if "c_proj" in name:
        UpperCamelCase	:Tuple     							=       name.replace('''c_proj'''					,  '''fc2'''	)
    if "attn" in name and "self" not in name:
        UpperCamelCase	:Any     							=       name.replace('''attn'''					,  '''self_attn'''	)
    # text encoder
    if "token_embedding" in name:
        UpperCamelCase	:str     							=       name.replace('''token_embedding'''					,  '''text_model.embeddings.token_embedding'''	)
    if "positional_embedding" in name and "visual" not in name:
        UpperCamelCase	:Optional[int]     							=       name.replace('''positional_embedding'''					,  '''text_model.embeddings.position_embedding.weight'''	)
    if "ln_final" in name:
        UpperCamelCase	:Any     							=       name.replace('''ln_final'''					,  '''text_model.final_layer_norm'''	)
    # vision encoder
    if "visual.class_embedding" in name:
        UpperCamelCase	:Any     							=       name.replace('''visual.class_embedding'''					,  '''vision_model.embeddings.class_embedding'''	)
    if "visual.conv1" in name:
        UpperCamelCase	:List[Any]     							=       name.replace('''visual.conv1'''					,  '''vision_model.embeddings.patch_embedding'''	)
    if "visual.positional_embedding" in name:
        UpperCamelCase	:List[str]     							=       name.replace('''visual.positional_embedding'''					,  '''vision_model.embeddings.position_embedding.weight'''	)
    if "visual.ln_pre" in name:
        UpperCamelCase	:str     							=       name.replace('''visual.ln_pre'''					,  '''vision_model.pre_layrnorm'''	)
    if "visual.ln_post" in name:
        UpperCamelCase	:Union[str, Any]     							=       name.replace('''visual.ln_post'''					,  '''vision_model.post_layernorm'''	)
    # projection layers
    if "visual.proj" in name:
        UpperCamelCase	:Tuple     							=       name.replace('''visual.proj'''					,  '''visual_projection.weight'''	)
    if "text_projection" in name:
        UpperCamelCase	:str     							=       name.replace('''text_projection'''					,  '''text_projection.weight'''	)
    # decoder
    if "trans_conv" in name:
        UpperCamelCase	:int     							=       name.replace('''trans_conv'''					,  '''transposed_convolution'''	)
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
        UpperCamelCase	:Optional[Any]     							=       '''decoder.''' + name
    if "blocks" in name:
        UpperCamelCase	:str     							=       name.replace('''blocks'''					,  '''decoder.layers'''	)
    if "linear1" in name:
        UpperCamelCase	:List[Any]     							=       name.replace('''linear1'''					,  '''mlp.fc1'''	)
    if "linear2" in name:
        UpperCamelCase	:Tuple     							=       name.replace('''linear2'''					,  '''mlp.fc2'''	)
    if "norm1" in name and "layer_" not in name:
        UpperCamelCase	:Union[str, Any]     							=       name.replace('''norm1'''					,  '''layer_norm1'''	)
    if "norm2" in name and "layer_" not in name:
        UpperCamelCase	:Union[str, Any]     							=       name.replace('''norm2'''					,  '''layer_norm2'''	)

    return name


def   _A       (    SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     List[str]	):







    for key in orig_state_dict.copy().keys():
        UpperCamelCase	:Optional[int]     							=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

        if key.startswith('''clip_model'''	) and "attn.in_proj" in key:
            UpperCamelCase	:Tuple     							=       key.split('''.'''	)
            if "visual" in key:
                UpperCamelCase	:int     							=       int(key_split[4]	)
                UpperCamelCase	:Tuple     							=       config.vision_config.hidden_size
                UpperCamelCase	:Optional[int]     							=       '''vision_model'''
            else:
                UpperCamelCase	:Optional[int]     							=       int(key_split[3]	)
                UpperCamelCase	:List[Any]     							=       config.text_config.hidden_size
                UpperCamelCase	:int     							=       '''text_model'''

            if "weight" in key:
                UpperCamelCase	:Any     							=       val[:dim, :]
                UpperCamelCase	:Any     							=       val[
                    dim : dim * 2, :
                ]
                UpperCamelCase	:str     							=       val[-dim:, :]
            else:
                UpperCamelCase	:Union[str, Any]     							=       val[:dim]
                UpperCamelCase	:str     							=       val[dim : dim * 2]
                UpperCamelCase	:List[Any]     							=       val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            UpperCamelCase	:str     							=       key.split('''.'''	)
            UpperCamelCase	:Any     							=       int(key_split[1]	)
            UpperCamelCase	:List[str]     							=       config.reduce_dim
            if "weight" in key:
                UpperCamelCase	:Dict     							=       val[:dim, :]
                UpperCamelCase	:Union[str, Any]     							=       val[dim : dim * 2, :]
                UpperCamelCase	:Any     							=       val[-dim:, :]
            else:
                UpperCamelCase	:Any     							=       val[:dim]
                UpperCamelCase	:Dict     							=       val[dim : dim * 2]
                UpperCamelCase	:Union[str, Any]     							=       val[-dim:]
        else:
            UpperCamelCase	:int     							=       rename_key(SCREAMING_SNAKE_CASE__	)
            if "visual_projection" in new_name or "text_projection" in new_name:
                UpperCamelCase	:List[Any]     							=       val.T
            UpperCamelCase	:List[Any]     							=       val

    return orig_state_dict


def   _A       (    ):
    UpperCamelCase	:List[str]     							=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase	:str     							=       Image.open(requests.get(SCREAMING_SNAKE_CASE__					,  stream=SCREAMING_SNAKE_CASE__	).raw	)
    return image


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     List[str]					,  SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     str	):
    UpperCamelCase	:Dict     							=       get_clipseg_config(SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__	)
    model.eval()

    UpperCamelCase	:List[str]     							=       torch.load(SCREAMING_SNAKE_CASE__					,  map_location='''cpu'''	)

    # remove some keys
    for key in state_dict.copy().keys():
        if key.startswith('''model'''	):
            state_dict.pop(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)

    # rename some keys
    UpperCamelCase	:Optional[Any]     							=       convert_state_dict(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase							,       UpperCamelCase	:Optional[int]     							=       model.load_state_dict(SCREAMING_SNAKE_CASE__					,  strict=SCREAMING_SNAKE_CASE__	)

    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
        raise ValueError('''Missing keys that are not expected: {}'''.format(SCREAMING_SNAKE_CASE__	)	)
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
        raise ValueError(F'''Unexpected keys: {unexpected_keys}'''	)

    UpperCamelCase	:List[Any]     							=       ViTImageProcessor(size=352	)
    UpperCamelCase	:Union[str, Any]     							=       CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''	)
    UpperCamelCase	:Dict     							=       CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__					,  tokenizer=SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:Optional[int]     							=       prepare_img()
    UpperCamelCase	:Tuple     							=       ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

    UpperCamelCase	:Optional[Any]     							=       processor(text=SCREAMING_SNAKE_CASE__					,  images=[image] * len(SCREAMING_SNAKE_CASE__	)					,  padding='''max_length'''					,  return_tensors='''pt'''	)

    with torch.no_grad():
        UpperCamelCase	:Any     							=       model(**SCREAMING_SNAKE_CASE__	)

    # verify values
    UpperCamelCase	:List[str]     							=       torch.tensor([0.11_10, -0.18_82, 0.16_45]	)
    UpperCamelCase	:Dict     							=       torch.tensor([0.26_92, -0.71_97, -0.13_28]	)
    if model_name == "clipseg-rd64-refined":
        UpperCamelCase	:Optional[int]     							=       torch.tensor(
            [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]	)
    elif model_name == "clipseg-rd64":
        UpperCamelCase	:Any     							=       torch.tensor(
            [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]	)
    elif model_name == "clipseg-rd16":
        UpperCamelCase	:int     							=       torch.tensor(
            [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]	)
    else:
        raise ValueError(F'''Model name {model_name} not supported.'''	)

    assert torch.allclose(outputs.logits[0, :3, :3]					,  SCREAMING_SNAKE_CASE__					,  atol=1e-3	)
    assert torch.allclose(outputs.conditional_embeddings[0, :3]					,  SCREAMING_SNAKE_CASE__					,  atol=1e-3	)
    assert torch.allclose(outputs.pooled_output[0, :3]					,  SCREAMING_SNAKE_CASE__					,  atol=1e-3	)
    print('''Looks ok!'''	)

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor to {pytorch_dump_folder_path}'''	)
        model.save_pretrained(SCREAMING_SNAKE_CASE__	)
        processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

    if push_to_hub:
        print(F'''Pushing model and processor for {model_name} to the hub'''	)
        model.push_to_hub(F'''CIDAS/{model_name}'''	)
        processor.push_to_hub(F'''CIDAS/{model_name}'''	)


if __name__ == "__main__":
 __snake_case							 =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""clipseg-rd64""",
     type=str,
     choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
     help=(
         """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
         """ reduce dimension)"""
     ),
 )
 parser.add_argument(
     """--checkpoint_path""",
     default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
     type=str,
     help=(
         """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
         """ the decoder weights."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __snake_case							 =  parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    if hue < 0 or hue > 360:
        raise Exception('''hue should be between 0 and 360'''	)

    if saturation < 0 or saturation > 1:
        raise Exception('''saturation should be between 0 and 1'''	)

    if value < 0 or value > 1:
        raise Exception('''value should be between 0 and 1'''	)

    UpperCamelCase	:Tuple     							=       value * saturation
    UpperCamelCase	:Any     							=       hue / 60
    UpperCamelCase	:Any     							=       chroma * (1 - abs(hue_section % 2 - 1	))
    UpperCamelCase	:Optional[int]     							=       value - chroma

    if hue_section >= 0 and hue_section <= 1:
        UpperCamelCase	:Any     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
    elif hue_section > 1 and hue_section <= 2:
        UpperCamelCase	:Dict     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:Dict     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
    elif hue_section > 2 and hue_section <= 3:
        UpperCamelCase	:Any     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
    elif hue_section > 3 and hue_section <= 4:
        UpperCamelCase	:Tuple     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (chroma + match_value)	)
    elif hue_section > 4 and hue_section <= 5:
        UpperCamelCase	:int     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (chroma + match_value)	)
    else:
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[int]     							=       round(255 * (second_largest_component + match_value)	)

    return [red, green, blue]


def   _A       (    SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int	):
    if red < 0 or red > 255:
        raise Exception('''red should be between 0 and 255'''	)

    if green < 0 or green > 255:
        raise Exception('''green should be between 0 and 255'''	)

    if blue < 0 or blue > 255:
        raise Exception('''blue should be between 0 and 255'''	)

    UpperCamelCase	:str     							=       red / 255
    UpperCamelCase	:List[str]     							=       green / 255
    UpperCamelCase	:int     							=       blue / 255
    UpperCamelCase	:Optional[int]     							=       max(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       value - min(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Dict     							=       0 if value == 0 else chroma / value

    if chroma == 0:
        UpperCamelCase	:Dict     							=       0.0
    elif value == float_red:
        UpperCamelCase	:Optional[Any]     							=       60 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        UpperCamelCase	:List[str]     							=       60 * (2 + (float_blue - float_red) / chroma)
    else:
        UpperCamelCase	:str     							=       60 * (4 + (float_red - float_green) / chroma)

    UpperCamelCase	:int     							=       (hue + 360) % 360

    return [hue, saturation, value]



def   _A       (    SCREAMING_SNAKE_CASE__  :     list[float]					,  SCREAMING_SNAKE_CASE__  :     list[float]	):
    UpperCamelCase	:str     							=       abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCamelCase	:List[str]     							=       abs(hsv_a[1] - hsv_a[1]	) < 0.0_02
    UpperCamelCase	:Any     							=       abs(hsv_a[2] - hsv_a[2]	) < 0.0_02

    return check_hue and check_saturation and check_value





