





import argparse

import requests
import torch
from PIL import Image

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

def      __snake_case						(					_lowerCAmelCase : Tuple	):
   A_					:				int          =			CLIPSegTextConfig()
   A_					:				int          =			CLIPSegVisionConfig(patch_size=16	)

   A_					:				str          =			True if "refined" in model_name else False
   A_					:				str          =			16 if "rd16" in model_name else 64

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

   return name

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[Any]	):
   for key in orig_state_dict.copy().keys():
      A_					:				Optional[Any]          =			orig_state_dict.pop(__snake_case	)

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

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

   return orig_state_dict

def      __snake_case						(					):
   A_					:				List[str]          =			"http://images.cocodataset.org/val2017/000000039769.jpg"
   A_					:				List[str]          =			Image.open(requests.get(__snake_case				,				stream=__snake_case	).raw	)
   return image

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : str				,				_lowerCAmelCase : str	):
   A_					:				Optional[Any]          =			get_clipseg_config(__snake_case	)
   A_					:				str          =			CLIPSegForImageSegmentation(__snake_case	)
   model.eval()

   A_					:				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
   A_					:				List[Any]          =			convert_state_dict(__snake_case				,				__snake_case	)
   A_      ,						A_					:				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}"	)

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

   A_					:				List[str]          =			prepare_img()
   A_					:				Union[str, Any]          =			["a glass", "something to fill", "wood", "a jar"]

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

   with torch.no_grad():
      A_					:				List[Any]          =			model(**__snake_case	)

   # verify values
   A_					:				Dict          =			torch.tensor([0.11_10, -0.18_82, 0.16_45]	)
   A_					:				Union[str, Any]          =			torch.tensor([0.26_92, -0.71_97, -0.13_28]	)
   if model_name == "clipseg-rd64-refined":
      A_					:				Optional[Any]          =			torch.tensor(
          [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]	)
   elif model_name == "clipseg-rd64":
      A_					:				str          =			torch.tensor(
          [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]	)
   elif model_name == "clipseg-rd16":
      A_					:				Optional[Any]          =			torch.tensor(
          [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]	)
   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							:      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.'''
 )

 _lowerCAmelCase							:      str						=	parser.parse_args()
 convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

_lowerCAmelCase							:      Union[str, Any]						=	0.25 + 0.0j
_lowerCAmelCase							:      str						=	-0.4 + 0.6j
_lowerCAmelCase							:      Optional[int]						=	-0.1 + 0.6_5_1j
_lowerCAmelCase							:      Union[str, Any]						=	-2.0
_lowerCAmelCase							:      str						=	56
_lowerCAmelCase							:      Optional[Any]						=	2.0
_lowerCAmelCase							:      Dict						=	666

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return numpy.exp(_lowerCAmelCase	) + c_parameter

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return z_values * z_values + c_parameter

def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						numpy.ndarray:
   A_					:				Tuple          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Optional[int]          =			x.reshape((nb_pixels, 1)	)
   A_					:				str          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Tuple          =			y.reshape((1, nb_pixels)	)
   return x + 1.0j * y

def      __snake_case						(					_lowerCAmelCase : Callable[[Any, numpy.ndarray], numpy.ndarray]				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : int				,				_lowerCAmelCase : numpy.ndarray				,				_lowerCAmelCase : float | None = None				,				) ->						numpy.ndarray:
   A_					:				List[Any]          =			z_a.astype("complex64"	)
   for _ in range(_lowerCAmelCase	):
      A_					:				List[Any]          =			eval_function(_lowerCAmelCase				,				_lowerCAmelCase	)
      if infinity is not None:
         numpy.nan_to_num(_lowerCAmelCase				,				copy=_lowerCAmelCase				,				nan=_lowerCAmelCase	)
         A_					:				List[Any]          =			infinity
   return z_n

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : float				,				_lowerCAmelCase : numpy.ndarray				,				) ->						None:
   A_					:				Optional[int]          =			(abs(_lowerCAmelCase	)).transpose()
   A_					:				Optional[int]          =			abs_z_final[::-1, :]
   pyplot.matshow(abs_z_final < escape_radius	)
   pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
   pyplot.show()

def      __snake_case						(					) ->						None:
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in multiply"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="invalid value encountered in multiply"				,				)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in absolute"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in exp"	)


if __name__ == "__main__":
 _lowerCAmelCase							:      List[str]						=	prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 _lowerCAmelCase							:      str						=	24
 _lowerCAmelCase							:      str						=	2 * abs(c_cauliflower) + 1
 _lowerCAmelCase							:      Optional[Any]						=	iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_cauliflower, escape_radius, z_final)

 _lowerCAmelCase							:      Optional[int]						=	64
 _lowerCAmelCase							:      List[Any]						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[str]						=	iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

 _lowerCAmelCase							:      Tuple						=	161
 _lowerCAmelCase							:      int						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[Any]						=	iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

 _lowerCAmelCase							:      List[str]						=	12
 _lowerCAmelCase							:      str						=	1_0000.0
 _lowerCAmelCase							:      Tuple						=	iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0e10,
 )
 show_results('''e^z+c''', c_exponential, escape_radius, z_final)
