
def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   # The longer word should come first
   if len(__lowerCamelCase						) < len(__lowerCamelCase						):
      return levenshtein_distance(__lowerCamelCase,  __lowerCamelCase						)

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

   SCREAMING_SNAKE_CASE_	=      list(range(len(__lowerCamelCase						) + 1						)						)

   for i, ca in enumerate(__lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	=      [i + 1]

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

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

      # Store the previous row
      SCREAMING_SNAKE_CASE_	=      current_row

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


if __name__ == "__main__":
   __UpperCAmelCase   						=     input("Enter the first word:\n").strip()
   __UpperCAmelCase   						=     input("Enter the second word:\n").strip()

   __UpperCAmelCase   						=     levenshtein_distance(first_word, second_word)
   print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




import argparse

import requests
import torch
from PIL import Image

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


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      CLIPSegTextConfig()
   SCREAMING_SNAKE_CASE_	=      CLIPSegVisionConfig(patch_size=16						)

   SCREAMING_SNAKE_CASE_	=      True if '''refined''' in model_name else False
   SCREAMING_SNAKE_CASE_	=      16 if '''rd16''' in model_name else 64

   SCREAMING_SNAKE_CASE_	=      CLIPSegConfig.from_text_vision_configs(
       __lowerCamelCase,  __lowerCamelCase,  use_complex_transposed_convolution=__lowerCamelCase,  reduce_dim=__lowerCamelCase,  )
   return config


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

   return name


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_	=      orig_state_dict.pop(__lowerCamelCase						)

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

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

   return orig_state_dict


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE_	=      Image.open(requests.get(__lowerCamelCase,  stream=__lowerCamelCase						).raw						)
   return image


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      get_clipseg_config(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegForImageSegmentation(__lowerCamelCase						)
   model.eval()

   SCREAMING_SNAKE_CASE_	=      torch.load(__lowerCamelCase,  map_location='''cpu'''						)

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

    # rename some keys
   SCREAMING_SNAKE_CASE_	=      convert_state_dict(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      model.load_state_dict(__lowerCamelCase,  strict=__lowerCamelCase						)

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

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

   SCREAMING_SNAKE_CASE_	=      prepare_img()
   SCREAMING_SNAKE_CASE_	=      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

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

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_	=      model(**__lowerCamelCase						)

   # verify values
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.11_10, -0.18_82, 0.16_45]						)
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.26_92, -0.71_97, -0.13_28]						)
   if model_name == "clipseg-rd64-refined":
      SCREAMING_SNAKE_CASE_	=      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":
      SCREAMING_SNAKE_CASE_	=      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":
      SCREAMING_SNAKE_CASE_	=      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],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.conditional_embeddings[0, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.pooled_output[0, :3],  __lowerCamelCase,  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(__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

   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)


