



'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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






def 			UpperCAmelCase_							(		lowercase__							):


     '''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(
         lowercase__							,						lowercase__							,						use_complex_transposed_convolution=lowercase__							,						reduce_dim=lowercase__							,						)
     return config






def 			UpperCAmelCase_							(		lowercase__							):


     '''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_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     for key in orig_state_dict.copy().keys():
          a_									=orig_state_dict.pop(lowercase__							)

          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(lowercase__							)
               if "visual_projection" in new_name or "text_projection" in new_name:
                    a_									=val.T
               a_									=val

     return orig_state_dict






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_									=Image.open(requests.get(lowercase__							,						stream=lowercase__							).raw							)
     return image






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=get_clipseg_config(lowercase__							)
     a_									=CLIPSegForImageSegmentation(lowercase__							)
     model.eval()

     a_									=torch.load(lowercase__							,						map_location="cpu"							)

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

    # rename some keys
     a_									=convert_state_dict(lowercase__							,						lowercase__							)
     a_		,    a_									=model.load_state_dict(lowercase__							,						strict=lowercase__							)

     if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
          raise ValueError("Missing keys that are not expected: {}".format(lowercase__							)							)
     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=lowercase__							,						tokenizer=lowercase__							)

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

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

     with torch.no_grad():
          a_									=model(**lowercase__							)

     # verify values
     a_									=torch.tensor([0.1110, -0.1882, 0.1645]							)
     a_									=torch.tensor([0.2692, -0.7197, -0.1328]							)
     if model_name == "clipseg-rd64-refined":
          a_									=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":
          a_									=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":
          a_									=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]							,						lowercase__							,						atol=1E-3							)
     assert torch.allclose(outputs.conditional_embeddings[0, :3]							,						lowercase__							,						atol=1E-3							)
     assert torch.allclose(outputs.pooled_output[0, :3]							,						lowercase__							,						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(lowercase__							)
          processor.save_pretrained(lowercase__							)

     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__":
  lowercase											=      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.'''
  )

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







'''simple docstring'''


import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Union[List[PIL.Image.Image], np.ndarray]







class 						UpperCAmelCase						(		unittest.TestCase):


 '''simple docstring'''




 def    lowercase_	( self)			->   List[str]:



      """simple docstring"""







      a_									=CustomOutput(images=np.random.rand(1				,	3				,	4				,	4))

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	np.ndarray)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]				,	np.ndarray)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,	np.ndarray)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      a_									=CustomOutput(images=[PIL.Image.new("RGB"				,	(4, 4))])

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	lowerCAmelCase_)
      assert isinstance(outputs.images[0]				,	PIL.Image.Image)
      assert isinstance(outputs["images"]				,	lowerCAmelCase_)
      assert isinstance(outputs["images"][0]				,	PIL.Image.Image)
      assert isinstance(outputs[0]				,	lowerCAmelCase_)
      assert isinstance(outputs[0][0]				,	PIL.Image.Image)




 def    lowercase_	( self)			->   Optional[int]:



      """simple docstring"""







      a_									=CustomOutput({"images": np.random.rand(1				,	3				,	4				,	4)})

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	np.ndarray)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]				,	np.ndarray)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,	np.ndarray)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      a_									=CustomOutput({"images": [PIL.Image.new("RGB"				,	(4, 4))]})

      # check every way of getting the attribute
      assert isinstance(outputs.images				,	lowerCAmelCase_)
      assert isinstance(outputs.images[0]				,	PIL.Image.Image)
      assert isinstance(outputs["images"]				,	lowerCAmelCase_)
      assert isinstance(outputs["images"][0]				,	PIL.Image.Image)
      assert isinstance(outputs[0]				,	lowerCAmelCase_)
      assert isinstance(outputs[0][0]				,	PIL.Image.Image)


