



import argparse

import requests
import torch
from PIL import Image

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







def  _snake_case			(							__snake_case     )  ->  List[str]:






      '''simple docstring'''







      UpperCAmelCase_	:						List[Any]					   =		CLIPSegTextConfig()
      UpperCAmelCase_	:						List[str]					   =		CLIPSegVisionConfig(patch_size=1_6     )

      UpperCAmelCase_	:						List[str]					   =		True if "refined" in model_name else False
      UpperCAmelCase_	:						List[str]					   =		1_6 if "rd16" in model_name else 6_4

      UpperCAmelCase_	:						List[Any]					   =		CLIPSegConfig.from_text_vision_configs(
          __snake_case       ,	__snake_case       ,	use_complex_transposed_convolution=__snake_case       ,	reduce_dim=__snake_case       ,	)
      return config







def  _snake_case			(							__snake_case     )  ->  Tuple:






      '''simple docstring'''







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

      return name







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[str]:






      '''simple docstring'''







      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_	:						List[Any]					   =		orig_state_dict.pop(__snake_case     )

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

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

      return orig_state_dict







def  _snake_case			(							)  ->  List[str]:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_	:						Dict					   =		Image.open(requests.get(__snake_case       ,	stream=__snake_case     ).raw     )
      return image







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						int					   =		get_clipseg_config(__snake_case     )
      UpperCAmelCase_	:						Any					   =		CLIPSegForImageSegmentation(__snake_case     )
      model.eval()

      UpperCAmelCase_	:						Any					   =		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
      UpperCAmelCase_	:						List[str]					   =		convert_state_dict(__snake_case       ,	__snake_case     )
      UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[int]					   =		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}"""     )

      UpperCAmelCase_	:						List[str]					   =		ViTImageProcessor(size=3_5_2     )
      UpperCAmelCase_	:						Optional[Any]					   =		CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
      UpperCAmelCase_	:						Optional[Any]					   =		CLIPSegProcessor(image_processor=__snake_case       ,	tokenizer=__snake_case     )

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

      UpperCAmelCase_	:						Optional[Any]					   =		processor(text=__snake_case       ,	images=[image] * len(__snake_case     )       ,	padding="max_length"       ,	return_tensors="pt"     )

      with torch.no_grad():
            UpperCAmelCase_	:						Union[str, Any]					   =		model(**__snake_case     )

      # verify values
      UpperCAmelCase_	:						int					   =		torch.tensor([0.1110, -0.1882, 0.1645]     )
      UpperCAmelCase_	:						Tuple					   =		torch.tensor([0.2692, -0.7197, -0.1328]     )
      if model_name == "clipseg-rd64-refined":
            UpperCAmelCase_	:						Any					   =		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":
            UpperCAmelCase_	:						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":
            UpperCAmelCase_	:						int					   =		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]       ,	__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__":
   __lowerCamelCase								=      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.'''
   )

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





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__lowerCamelCase								=      [(1,)]







def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       @slow
       @require_torch_gpu
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @require_torch_gpu
       @slow
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @parameterized.expand(lowercase)
       @slow
       def 			A_ (  self  ,lowercase=None):

             """simple docstring"""





             check_slow()







@slow
@require_torch_gpu
def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      check_slow_torch_cuda()







@require_torch_gpu
@slow
def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("param"       ,	[1]     )
def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      check_slow()







@pytest.mark.parametrize("param"       ,	[1]     )
@slow
def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      check_slow()
