


'''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_       ):
 UpperCAmelCase_				       =					CLIPSegTextConfig()
 UpperCAmelCase_				       =					CLIPSegVisionConfig(patch_size=16       )

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

 UpperCAmelCase_				       =					CLIPSegConfig.from_text_vision_configs(
     _A       ,			_A       ,			use_complex_transposed_convolution=_A       ,			reduce_dim=_A       ,			)
 return config



def lowerCamelCase__						(       A_       ):


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

 return name



def lowerCamelCase__						(       A_       ,			A_       ):


 for key in orig_state_dict.copy().keys():
  UpperCAmelCase_				       =					orig_state_dict.pop(_A       )

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

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

 return orig_state_dict



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					"http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCAmelCase_				       =					Image.open(requests.get(_A       ,			stream=_A       ).raw       )
 return image



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					get_clipseg_config(_A       )
 UpperCAmelCase_				       =					CLIPSegForImageSegmentation(_A       )
 model.eval()

 UpperCAmelCase_				       =					torch.load(_A       ,			map_location="cpu"       )

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

    # rename some keys
 UpperCAmelCase_				       =					convert_state_dict(_A       ,			_A       )
 UpperCAmelCase_       ,       UpperCAmelCase_				       =					model.load_state_dict(_A       ,			strict=_A       )

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

 UpperCAmelCase_				       =					ViTImageProcessor(size=352       )
 UpperCAmelCase_				       =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"       )
 UpperCAmelCase_				       =					CLIPSegProcessor(image_processor=_A       ,			tokenizer=_A       )

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

 UpperCAmelCase_				       =					processor(text=_A       ,			images=[image] * len(_A       )       ,			padding="max_length"       ,			return_tensors="pt"       )

 with torch.no_grad():
  UpperCAmelCase_				       =					model(**_A       )

 # verify values
 UpperCAmelCase_				       =					torch.tensor([0.1110, -0.1882, 0.1645]       )
 UpperCAmelCase_				       =					torch.tensor([0.2692, -0.7197, -0.1328]       )
 if model_name == "clipseg-rd64-refined":
  UpperCAmelCase_				       =					torch.tensor(
      [[-10.0_407, -9.9431, -10.2_646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]       )
 elif model_name == "clipseg-rd64":
  UpperCAmelCase_				       =					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":
  UpperCAmelCase_				       =					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]       ,			_A       ,			atol=1e-3       )
 assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,			_A       ,			atol=1e-3       )
 assert torch.allclose(outputs.pooled_output[0, :3]       ,			_A       ,			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(_A       )
  processor.save_pretrained(_A       )

 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__":
      __snake_case       :   Optional[Any]  		=    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.'''
      )

      __snake_case       :   int  		=    parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''

def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_				       =					len(A_       )
 for _ in range(
     0       ,			int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
  for j in range(0       ,			no_of_elements - 1       ):
   if (
       lst[j + 1] < lst[j]
   ):  # applying bubble sort algorithm from left to right (or forwards)
    UpperCAmelCase_				       =					lst[j + 1]
    UpperCAmelCase_				       =					lst[j]
    UpperCAmelCase_				       =					temp
   if (
       lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
   ):  # applying bubble sort algorithm from right to left (or backwards)
    UpperCAmelCase_				       =					lst[no_of_elements - 1 - j]
    UpperCAmelCase_				       =					lst[no_of_elements - 2 - j]
    UpperCAmelCase_				       =					temp
 return lst


if __name__ == "__main__":
      print('''enter the list to be sorted''')
      __snake_case       :   List[str]  		=    [int(x) for x in input().split()]  # inputing elements of the list in one line
      __snake_case       :   Optional[int]  		=    double_sort(lst)
      print('''the sorted list is''')
      print(sorted_lst)
