
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Tuple        =    CLIPSegTextConfig()
    snake_case_			:     int        =    CLIPSegVisionConfig(patch_size=1_6							)

    snake_case_			:     List[Any]        =    True if "refined" in model_name else False
    snake_case_			:     List[Any]        =    1_6 if "rd16" in model_name else 6_4

    snake_case_			:     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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

    return name

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        snake_case_			:     Dict        =    orig_state_dict.pop(__SCREAMING_SNAKE_CASE							)

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

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

    return orig_state_dict

def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case_			:     Any        =    Image.open(requests.get(__SCREAMING_SNAKE_CASE,		stream=__SCREAMING_SNAKE_CASE							).raw							)
    return image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    get_clipseg_config(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     int        =    CLIPSegForImageSegmentation(__SCREAMING_SNAKE_CASE							)
    model.eval()

    snake_case_			:     Optional[Any]        =    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
    snake_case_			:     List[Any]        =    convert_state_dict(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
    snake_case_      ,			snake_case_			:     List[str]        =    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}'							)

    snake_case_			:     Any        =    ViTImageProcessor(size=3_5_2							)
    snake_case_			:     List[str]        =    CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
    snake_case_			:     Dict        =    CLIPSegProcessor(image_processor=__SCREAMING_SNAKE_CASE,		tokenizer=__SCREAMING_SNAKE_CASE							)

    snake_case_			:     int        =    prepare_img()
    snake_case_			:     int        =    ["a glass", "something to fill", "wood", "a jar"]

    snake_case_			:     Optional[int]        =    processor(text=__SCREAMING_SNAKE_CASE,		images=[image] * len(__SCREAMING_SNAKE_CASE							),		padding="max_length",		return_tensors="pt"							)

    with torch.no_grad():
        snake_case_			:     Optional[int]        =    model(**__SCREAMING_SNAKE_CASE							)

    # verify values
    snake_case_			:     Tuple        =    torch.tensor([0.1110, -0.1882, 0.1645]							)
    snake_case_			:     List[str]        =    torch.tensor([0.2692, -0.7197, -0.1328]							)
    if model_name == "clipseg-rd64-refined":
        snake_case_			:     Any        =    torch.tensor(
            [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]							)
    elif model_name == "clipseg-rd64":
        snake_case_			:     int        =    torch.tensor(
            [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]							)
    elif model_name == "clipseg-rd16":
        snake_case_			:     int        =    torch.tensor(
            [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]							)
    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__":
  a_           =    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."
  )

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






'''simple docstring'''
import numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


# kernel to be applied
a_           =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




