'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

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







def 				__UpperCamelCase (					__UpperCAmelCase  :					Union[str, Any]			)							->	Any:
  lowercase				:							Optional[int]									=   CLIPSegTextConfig()
  lowercase				:							Dict									=   CLIPSegVisionConfig(patch_size=16			)

  lowercase				:							int									=   True if '''refined''' in model_name else False
  lowercase				:							Optional[int]									=   16 if '''rd16''' in model_name else 64

  lowercase				:							int									=   CLIPSegConfig.from_text_vision_configs(
      __UpperCAmelCase ,   __UpperCAmelCase ,   use_complex_transposed_convolution=__UpperCAmelCase ,   reduce_dim=__UpperCAmelCase ,   )
  return config







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

  return name







def 				__UpperCamelCase (					__UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					Dict			)							->	Optional[int]:
  for key in orig_state_dict.copy().keys():
    lowercase				:							str									=   orig_state_dict.pop(__UpperCAmelCase			)

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

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

  return orig_state_dict







def 				__UpperCamelCase (					)							->	str:
  lowercase				:							str									=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowercase				:							List[Any]									=   Image.open(requests.get(__UpperCAmelCase ,   stream=__UpperCAmelCase			).raw			)
  return image







def 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[int] ,   __UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					Tuple ,   __UpperCAmelCase  :					str			)							->	Tuple:
  lowercase				:							Union[str, Any]									=   get_clipseg_config(__UpperCAmelCase			)
  lowercase				:							Union[str, Any]									=   CLIPSegForImageSegmentation(__UpperCAmelCase			)
  model.eval()

  lowercase				:							str									=   torch.load(__UpperCAmelCase ,   map_location='''cpu'''			)

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

    # rename some keys
  lowercase				:							Tuple									=   convert_state_dict(__UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				,      lowercase				:							int									=   model.load_state_dict(__UpperCAmelCase ,   strict=__UpperCAmelCase			)

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

  lowercase				:							Optional[int]									=   ViTImageProcessor(size=352			)
  lowercase				:							List[Any]									=   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
  lowercase				:							List[str]									=   CLIPSegProcessor(image_processor=__UpperCAmelCase ,   tokenizer=__UpperCAmelCase			)

  lowercase				:							Tuple									=   prepare_img()
  lowercase				:							List[Any]									=   ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

  lowercase				:							Dict									=   processor(text=__UpperCAmelCase ,   images=[image] * len(__UpperCAmelCase			) ,   padding='''max_length''' ,   return_tensors='''pt'''			)

  with torch.no_grad():
    lowercase				:							Dict									=   model(**__UpperCAmelCase			)

  # verify values
  lowercase				:							Dict									=   torch.tensor([0.1110, -0.1882, 0.1645]			)
  lowercase				:							Optional[Any]									=   torch.tensor([0.2692, -0.7197, -0.1328]			)
  if model_name == "clipseg-rd64-refined":
    lowercase				:							int									=   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":
    lowercase				:							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":
    lowercase				:							List[str]									=   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] ,   __UpperCAmelCase ,   atol=1e-3			)
  assert torch.allclose(outputs.conditional_embeddings[0, :3] ,   __UpperCAmelCase ,   atol=1e-3			)
  assert torch.allclose(outputs.pooled_output[0, :3] ,   __UpperCAmelCase ,   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(__UpperCAmelCase			)
    processor.save_pretrained(__UpperCAmelCase			)

  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		:      Tuple						   =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 (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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