

"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

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





def 					__UpperCamelCase (     a__:			List[Any]      )					->      Optional[int]:




					"""simple docstring"""



					__lowercase        =      CLIPSegTextConfig()
					__lowercase        =      CLIPSegVisionConfig(patch_size=16      )

					__lowercase        =      True if "refined" in model_name else False
					__lowercase        =      16 if "rd16" in model_name else 64

					__lowercase        =      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 (     a__:			Any      )					->      Optional[int]:




					"""simple docstring"""



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

					return name





def 					__UpperCamelCase (     a__:			str			,						a__:			Optional[int]      )					->      int:




					"""simple docstring"""



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

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

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

					return orig_state_dict





def 					__UpperCamelCase (     )					->      Any:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Any			,						a__:			List[Any]			,						a__:			Tuple      )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      get_clipseg_config(__SCREAMING_SNAKE_CASE      )
					__lowercase        =      CLIPSegForImageSegmentation(__SCREAMING_SNAKE_CASE      )
					model.eval()

					__lowercase        =      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
					__lowercase        =      convert_state_dict(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )
					__lowercase        =      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}'''      )

					__lowercase        =      ViTImageProcessor(size=3_52      )
					__lowercase        =      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
					__lowercase        =      CLIPSegProcessor(image_processor=__SCREAMING_SNAKE_CASE			,						tokenizer=__SCREAMING_SNAKE_CASE      )

					__lowercase        =      prepare_img()
					__lowercase        =      ["a glass", "something to fill", "wood", "a jar"]

					__lowercase        =      processor(text=__SCREAMING_SNAKE_CASE			,						images=[image] * len(__SCREAMING_SNAKE_CASE      )			,						padding='''max_length'''			,						return_tensors='''pt'''      )

					with torch.no_grad():
										__lowercase        =      model(**__SCREAMING_SNAKE_CASE      )

					# verify values
					__lowercase        =      torch.tensor([0.11_10, -0.18_82, 0.16_45]      )
					__lowercase        =      torch.tensor([0.26_92, -0.71_97, -0.13_28]      )
					if model_name == "clipseg-rd64-refined":
										__lowercase        =      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":
										__lowercase        =      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":
										__lowercase        =      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__":
							a__			:						Dict							=			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__			:						Optional[int]							=			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")




