



"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def        A_  (      _lowercase     ):







      '''simple docstring'''


      snake_case_			:int						=      CLIPSegTextConfig()
      snake_case_			:str						=      CLIPSegVisionConfig(patch_size=16     )

      snake_case_			:Any						=      True if """refined""" in model_name else False
      snake_case_			:int						=      16 if """rd16""" in model_name else 64

      snake_case_			:Union[str, Any]						=      CLIPSegConfig.from_text_vision_configs(
          _lowercase, _lowercase, use_complex_transposed_convolution=_lowercase, reduce_dim=_lowercase, )
      return config
def        A_  (      _lowercase     ):







      '''simple docstring'''


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

      return name
def        A_  (      _lowercase, _lowercase     ):







      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            snake_case_			:Any						=      orig_state_dict.pop(_lowercase     )

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

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

      return orig_state_dict
def        A_  (      ):







      '''simple docstring'''


      snake_case_			:Optional[int]						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case_			:Optional[int]						=      Image.open(requests.get(_lowercase, stream=_lowercase     ).raw     )
      return image
def        A_  (      _lowercase, _lowercase, _lowercase, _lowercase     ):







      '''simple docstring'''


      snake_case_			:Union[str, Any]						=      get_clipseg_config(_lowercase     )
      snake_case_			:str						=      CLIPSegForImageSegmentation(_lowercase     )
      model.eval()

      snake_case_			:Any						=      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
      snake_case_			:Dict						=      convert_state_dict(_lowercase, _lowercase     )
      snake_case_			:List[str]						=      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}"""     )

      snake_case_			:Optional[int]						=      ViTImageProcessor(size=352     )
      snake_case_			:Union[str, Any]						=      CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""     )
      snake_case_			:List[Any]						=      CLIPSegProcessor(image_processor=_lowercase, tokenizer=_lowercase     )

      snake_case_			:Optional[int]						=      prepare_img()
      snake_case_			:List[Any]						=      ["""a glass""", """something to fill""", """wood""", """a jar"""]

      snake_case_			:str						=      processor(text=_lowercase, images=[image] * len(_lowercase     ), padding="""max_length""", return_tensors="""pt"""     )

      with torch.no_grad():
            snake_case_			:int						=      model(**_lowercase     )

      # verify values
      snake_case_			:Dict						=      torch.tensor([0.1110, -0.1882, 0.1645]     )
      snake_case_			:Dict						=      torch.tensor([0.2692, -0.7197, -0.1328]     )
      if model_name == "clipseg-rd64-refined":
            snake_case_			:Dict						=      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":
            snake_case_			:str						=      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":
            snake_case_			:List[Any]						=      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__":
      __a     =    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."
      )

      __a     =    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 pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@patch("""diffusers.utils.hub_utils.get_full_repo_name"""						)
					def        lowerCAmelCase_    (	self:							int						,					snake_case:							Mock						)       ->     None:
											snake_case_			:str						=      """full_repo_name"""
											with TemporaryDirectory() as tmpdir:
																	# Dummy args values
																	snake_case_			:int						=      Mock()
																	snake_case_			:Any						=      tmpdir
																	snake_case_			:int						=      0
																	snake_case_			:Optional[int]						=      """hub_token"""
																	snake_case_			:str						=      """dataset_name"""
																	snake_case_			:List[str]						=      0.0_1
																	snake_case_			:Dict						=      100_000
																	snake_case_			:Optional[Any]						=      10_000
																	snake_case_			:Optional[Any]						=      0.0_1
																	snake_case_			:Dict						=      0.0_2
																	snake_case_			:Dict						=      0.0_3
																	snake_case_			:Union[str, Any]						=      0.0_0_0_5
																	snake_case_			:Optional[Any]						=      0.0_0_0_0_0_1
																	snake_case_			:List[Any]						=      1
																	snake_case_			:Optional[Any]						=      10
																	snake_case_			:Dict						=      0.0_0_1
																	snake_case_			:Any						=      0.1
																	snake_case_			:List[Any]						=      0.2
																	snake_case_			:Any						=      True

																	# Model card mush be rendered and saved
																	diffusers.utils.hub_utils.create_model_card(snake_case						,					model_name="""model_name"""						)
																	self.assertTrue((Path(snake_case						) / """README.md""").is_file()						)
