






import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def    a__    (	A       ):
   lowercase__    :						Dict       =			CLIPSegTextConfig()
   lowercase__    :						List[Any]       =			CLIPSegVisionConfig(patch_size=16       )

   lowercase__    :						int       =			True if """refined""" in model_name else False
   lowercase__    :						Tuple       =			16 if """rd16""" in model_name else 64

   lowercase__    :						List[Any]       =			CLIPSegConfig.from_text_vision_configs(
       lowercase__		,					lowercase__		,					use_complex_transposed_convolution=lowercase__		,					reduce_dim=lowercase__		,					)
   return config
def    a__    (	A       ):







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

   return name
def    a__    (	A		,					A       ):







   for key in orig_state_dict.copy().keys():
      lowercase__    :						Optional[int]       =			orig_state_dict.pop(lowercase__       )

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

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

   return orig_state_dict
def    a__    (	):
   lowercase__    :						Any       =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowercase__    :						List[str]       =			Image.open(requests.get(lowercase__		,					stream=lowercase__       ).raw       )
   return image
def    a__    (	A		,					A		,					A		,					A       ):
   lowercase__    :						str       =			get_clipseg_config(lowercase__       )
   lowercase__    :						List[str]       =			CLIPSegForImageSegmentation(lowercase__       )
   model.eval()

   lowercase__    :						Tuple       =			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
   lowercase__    :						Tuple       =			convert_state_dict(lowercase__		,					lowercase__       )
   lowercase__  ,       lowercase__    :						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}'''       )

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

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

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

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

   # verify values
   lowercase__    :						str       =			torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]       )
   lowercase__    :						Optional[Any]       =			torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]       )
   if model_name == "clipseg-rd64-refined":
      lowercase__    :						List[Any]       =			torch.tensor(
          [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]       )
   elif model_name == "clipseg-rd64":
      lowercase__    :						List[str]       =			torch.tensor(
          [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]       )
   elif model_name == "clipseg-rd16":
      lowercase__    :						Dict       =			torch.tensor(
          [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]       )
   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 argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


