






'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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






def  lowerCAmelCase__		(					a_  :     Union[str, Any]			)   -> int:
       UpperCAmelCase__	:   Dict												=	CLIPSegTextConfig()
       UpperCAmelCase__	:   Any												=	CLIPSegVisionConfig(patch_size=1_6			)

       UpperCAmelCase__	:   int												=	True if '''refined''' in model_name else False
       UpperCAmelCase__	:   Any												=	1_6 if '''rd16''' in model_name else 6_4

       UpperCAmelCase__	:   List[Any]												=	CLIPSegConfig.from_text_vision_configs(
           _UpperCAmelCase			,						_UpperCAmelCase			,						use_complex_transposed_convolution=_UpperCAmelCase			,						reduce_dim=_UpperCAmelCase			,						)
       return config






def  lowerCAmelCase__		(					a_  :     List[str]			)   -> Any:






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

       return name






def  lowerCAmelCase__		(					a_  :     Optional[int]			,						a_  :     Tuple			)   -> Tuple:






       for key in orig_state_dict.copy().keys():
              UpperCAmelCase__	:   str												=	orig_state_dict.pop(_UpperCAmelCase			)

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

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

       return orig_state_dict






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






def  lowerCAmelCase__		(					a_  :     List[Any]			,						a_  :     Union[str, Any]			,						a_  :     Tuple			,						a_  :     Optional[Any]			)   -> str:
       UpperCAmelCase__	:   List[str]												=	get_clipseg_config(_UpperCAmelCase			)
       UpperCAmelCase__	:   List[str]												=	CLIPSegForImageSegmentation(_UpperCAmelCase			)
       model.eval()

       UpperCAmelCase__	:   Any												=	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
       UpperCAmelCase__	:   Dict												=	convert_state_dict(_UpperCAmelCase			,						_UpperCAmelCase			)
       UpperCAmelCase__							,							UpperCAmelCase__	:   str												=	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}"""			)

       UpperCAmelCase__	:   str												=	ViTImageProcessor(size=3_5_2			)
       UpperCAmelCase__	:   Dict												=	CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
       UpperCAmelCase__	:   Optional[Any]												=	CLIPSegProcessor(image_processor=_UpperCAmelCase			,						tokenizer=_UpperCAmelCase			)

       UpperCAmelCase__	:   Dict												=	prepare_img()
       UpperCAmelCase__	:   int												=	['''a glass''', '''something to fill''', '''wood''', '''a jar''']

       UpperCAmelCase__	:   str												=	processor(text=_UpperCAmelCase			,						images=[image] * len(_UpperCAmelCase			)			,						padding='''max_length'''			,						return_tensors='''pt'''			)

       with torch.no_grad():
              UpperCAmelCase__	:   str												=	model(**_UpperCAmelCase			)

       # verify values
       UpperCAmelCase__	:   List[Any]												=	torch.tensor([0.1_110, -0.1_882, 0.1_645]			)
       UpperCAmelCase__	:   Any												=	torch.tensor([0.2_692, -0.7_197, -0.1_328]			)
       if model_name == "clipseg-rd64-refined":
              UpperCAmelCase__	:   Optional[Any]												=	torch.tensor(
                  [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]			)
       elif model_name == "clipseg-rd64":
              UpperCAmelCase__	:   int												=	torch.tensor(
                  [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]			)
       elif model_name == "clipseg-rd16":
              UpperCAmelCase__	:   Any												=	torch.tensor(
                  [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]			)
       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__":
  UpperCamelCase_					=				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."
  )

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






"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	int:

 '''simple docstring'''






 if len(_UpperCAmelCase						) < len(_UpperCAmelCase						):
  return levenshtein_distance(_UpperCAmelCase						,  _UpperCAmelCase						)

 if len(_UpperCAmelCase						) == 0:
  return len(_UpperCAmelCase						)

 snake_case__     				=					list(range(len(_UpperCAmelCase						) + 1						)						)

 for i, ca in enumerate(_UpperCAmelCase						):
  snake_case__     				=					[i + 1]

  for j, ca in enumerate(_UpperCAmelCase						):
   # Calculate insertions, deletions and substitutions
   snake_case__     				=					previous_row[j + 1] + 1
   snake_case__     				=					current_row[j] + 1
   snake_case__     				=					previous_row[j] + (ca != ca)

   # Get the minimum to append to the current row
   current_row.append(min(_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)						)

  # Store the previous row
  snake_case__     				=					current_row

 # Returns the last element (distance)
 return previous_row[-1]


if __name__ == "__main__":
   a__       :  Optional[int]       		=					input("""Enter the first word:\n""").strip()
   a__       :  int       		=					input("""Enter the second word:\n""").strip()

   a__       :  Union[str, Any]       		=					levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')

