





import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def  __UpperCamelCase    (						snake_case      )    ->		Union[str, Any]:


   '''simple docstring'''







   __A    =  CLIPSegTextConfig()
   __A    =  CLIPSegVisionConfig(patch_size=1_6      )

   __A    =  True if '''refined''' in model_name else False
   __A    =  1_6 if '''rd16''' in model_name else 6_4

   __A    =  CLIPSegConfig.from_text_vision_configs(
       snake_case							,			snake_case							,			use_complex_transposed_convolution=snake_case							,			reduce_dim=snake_case							,			)
   return config
def  __UpperCamelCase    (						snake_case      )    ->		Tuple:


   '''simple docstring'''







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

   return name
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		List[str]:


   '''simple docstring'''







   for key in orig_state_dict.copy().keys():
      __A    =  orig_state_dict.pop(snake_case      )

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

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

   return orig_state_dict
def  __UpperCamelCase    (						)    ->		Dict:


   '''simple docstring'''







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


   '''simple docstring'''







   __A    =  get_clipseg_config(snake_case      )
   __A    =  CLIPSegForImageSegmentation(snake_case      )
   model.eval()

   __A    =  torch.load(snake_case							,			map_location='''cpu'''      )

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

    # rename some keys
   __A    =  convert_state_dict(snake_case							,			snake_case      )
   __A   ,		__A    =  model.load_state_dict(snake_case							,			strict=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(snake_case      )      )
   if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
      raise ValueError(F"Unexpected keys: {unexpected_keys}"      )

   __A    =  ViTImageProcessor(size=3_5_2      )
   __A    =  CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
   __A    =  CLIPSegProcessor(image_processor=snake_case							,			tokenizer=snake_case      )

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

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

   with torch.no_grad():
      __A    =  model(**snake_case      )

   # verify values
   __A    =  torch.tensor([0.1_110, -0.1_882, 0.1_645]      )
   __A    =  torch.tensor([0.2_692, -0.7_197, -0.1_328]      )
   if model_name == "clipseg-rd64-refined":
      __A    =  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":
      __A    =  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":
      __A    =  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]							,			snake_case							,			atol=1E-3      )
   assert torch.allclose(outputs.conditional_embeddings[0, :3]							,			snake_case							,			atol=1E-3      )
   assert torch.allclose(outputs.pooled_output[0, :3]							,			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(snake_case      )
      processor.save_pretrained(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__":
    _UpperCamelCase       :				List[str]           =			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       :				Tuple           =			parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from collections import deque
def  __UpperCamelCase    (						snake_case      )    ->		List[str]:


   '''simple docstring'''







   return input(snake_case      ).strip().split(''' '''      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[int]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> "      ))
      graph[x].append(snake_case      )
   return graph
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[int]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> "      ))
      graph[x].append(snake_case      )
      graph[y].append(snake_case      )
   return graph
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		dict[int, list[tuple[int, int]]]:


   '''simple docstring'''







   __A    =  {}
   for i in range(snake_case      ):
      __A    =  []

   for e in range(snake_case      ):
      __A   ,		__A   ,		__A    =  (int(snake_case      ) for i in _input(F"Edge {e + 1}: <node1> <node2> <weight> "      ))
      graph[x].append((y, w)      )
      graph[y].append((x, w)      )
   return graph


if __name__ == "__main__":
    _UpperCamelCase       ,    _UpperCamelCase       :				List[Any]           =			(int(i) for i in _input("""Number of nodes and edges: """))

    _UpperCamelCase       :				int           =			int(
        _input(
            """Press 1 or 2 or 3 \n"""
            """1. Unweighted directed \n"""
            """2. Unweighted undirected \n"""
            """3. Weighted undirected \n"""
        )[0]
    )

    _UpperCamelCase       :				Optional[Any]           =			{
        1: initialize_unweighted_directed_graph,
        2: initialize_unweighted_undirected_graph,
        3: initialize_weighted_undirected_graph,
    }[graph_choice](n, m)
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







   __A   ,		__A    =  {s}, [s]
   print(snake_case      )
   while _s:
      __A    =  0
      for i in g[_s[-1]]:
         if i not in vis:
            _s.append(snake_case      )
            vis.add(snake_case      )
            __A    =  1
            print(snake_case      )
            break
      if not flag:
         _s.pop()
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   __A   ,		__A    =  {s}, deque([s]      )
   print(snake_case      )
   while q:
      __A    =  q.popleft()
      for v in g[u]:
         if v not in vis:
            vis.add(snake_case      )
            q.append(snake_case      )
            print(snake_case      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		List[Any]:


   '''simple docstring'''







   __A   ,		__A   ,		__A    =  {s: 0}, set(), {s: 0}
   while True:
      if len(snake_case      ) == len(snake_case      ) - 1:
         break
      __A    =  1_0_0_0_0_0
      for i in dist:
         if i not in known and dist[i] < mini:
            __A    =  dist[i]
            __A    =  i
      known.add(snake_case      )
      for v in g[u]:
         if v[0] not in known and dist[u] + v[1] < dist.get(v[0]							,			1_0_0_0_0_0      ):
            __A    =  dist[u] + v[1]
            __A    =  u
   for i in dist:
      if i != s:
         print(dist[i]      )
def  __UpperCamelCase    (						snake_case							,			snake_case=None							,			snake_case=None      )    ->		Any:


   '''simple docstring'''







   if q is None:
      __A    =  [1]
   if ind is None:
      __A    =  [0] * (len(snake_case      ) + 1)  # SInce oth Index is ignored
      for u in g:
         for v in g[u]:
            ind[v] += 1
      __A    =  deque()
      for i in g:
         if ind[i] == 0:
            q.append(snake_case      )
   if len(snake_case      ) == 0:
      return
   __A    =  q.popleft()
   print(snake_case      )
   for w in g[v]:
      ind[w] -= 1
      if ind[w] == 0:
         q.append(snake_case      )
   topo(snake_case							,			snake_case							,			snake_case      )
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  input().strip()
   __A    =  []
   for _ in range(snake_case      ):
      a.append(map(snake_case							,			input().strip().split()      )      )
   return a, n
def  __UpperCamelCase    (						snake_case      )    ->		List[Any]:


   '''simple docstring'''







   ((__A)   ,		(__A))    =  a_and_n
   __A    =  list(snake_case      )
   __A    =  [[0] * n for i in range(snake_case      )]
   for k in range(snake_case      ):
      for i in range(snake_case      ):
         for j in range(snake_case      ):
            if dist[i][j] > dist[i][k] + dist[k][j]:
               __A    =  dist[i][k] + dist[k][j]
               __A    =  k
   print(snake_case      )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   __A   ,		__A   ,		__A    =  {s: 0}, set(), {s: 0}
   while True:
      if len(snake_case      ) == len(snake_case      ) - 1:
         break
      __A    =  1_0_0_0_0_0
      for i in dist:
         if i not in known and dist[i] < mini:
            __A    =  dist[i]
            __A    =  i
      known.add(snake_case      )
      for v in g[u]:
         if v[0] not in known and v[1] < dist.get(v[0]							,			1_0_0_0_0_0      ):
            __A    =  v[1]
            __A    =  u
   return dist
def  __UpperCamelCase    (						)    ->		List[str]:


   '''simple docstring'''







   __A   ,		__A    =  map(snake_case							,			input().split(''' '''      )      )
   __A    =  []
   for _ in range(snake_case      ):
      edges.append(map(snake_case							,			input().split(''' '''      )      )      )
   return edges, n
def  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   ((__A)   ,		(__A))    =  e_and_n
   e.sort(reverse=snake_case							,			key=lambda snake_case      : x[2]      )
   __A    =  [{i} for i in range(1							,			n + 1      )]
   while True:
      if len(snake_case      ) == 1:
         break
      print(snake_case      )
      __A    =  e.pop()
      for i in range(len(snake_case      )      ):
         if x[0] in s[i]:
            break
      for j in range(len(snake_case      )      ):
         if x[1] in s[j]:
            if i == j:
               break
            s[j].update(s[i]      )
            s.pop(snake_case      )
            break
def  __UpperCamelCase    (						snake_case      )    ->		Tuple:


   '''simple docstring'''







   __A    =  []
   for node in graph:
      if not graph[node]:
         isolated.append(snake_case      )
   return isolated

