




import argparse

import requests
import torch
from PIL import Image

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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  CLIPSegTextConfig()
  a_		    =  CLIPSegVisionConfig(patch_size=16			)

  a_		    =  True if '''refined''' in model_name else False
  a_		    =  16 if '''rd16''' in model_name else 64

  a_		    =  CLIPSegConfig.from_text_vision_configs(
      _A      ,      _A      ,      use_complex_transposed_convolution=_A      ,      reduce_dim=_A      ,      )
  return config






def 				UpperCAmelCase__  (							_A			):






  """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__  (							_A      ,      _A			):






  """simple docstring"""


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

    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(_A			)
      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(_A      ,      stream=_A			).raw			)
  return image






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


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

  a_		    =  torch.load(_A      ,      map_location='''cpu'''			)

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

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

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError('''Missing keys that are not expected: {}'''.format(_A			)			)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(f"Unexpected keys: {unexpected_keys}"			)

  a_		    =  ViTImageProcessor(size=352			)
  a_		    =  CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
  a_		    =  CLIPSegProcessor(image_processor=_A      ,      tokenizer=_A			)

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

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

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

  # verify values
  a_		    =  torch.tensor([0.11_10, -0.18_82, 0.16_45]			)
  a_		    =  torch.tensor([0.26_92, -0.71_97, -0.13_28]			)
  if model_name == "clipseg-rd64-refined":
    a_		    =  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_		    =  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_		    =  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]      ,      _A      ,      atol=1e-3			)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]      ,      _A      ,      atol=1e-3			)
  assert torch.allclose(outputs.pooled_output[0, :3]      ,      _A      ,      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(_A			)
    processor.save_pretrained(_A			)

  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__":
  UpperCamelCase__       				=	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.'''
  )

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






import math






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  math.log(2      ,      10			) * number
  a_		    =  round((log_number - int(_A			))      ,      15			)

  return difference






def 				UpperCAmelCase__  (							_A = 678_910			):






  """simple docstring"""


  a_		    =  90
  a_		    =  0

  a_		    =  math.log(1.23      ,      10			)
  a_		    =  math.log(1.24      ,      10			)
  a_		    =  0

  while position < number:
    a_		    =  log_difference(_A			)

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 196

    else:
      a_		    =  power_iterator
      power_iterator += 196
      position += 1

  return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")
