




import argparse

import requests
import torch
from PIL import Image

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    CLIPSegTextConfig()
      SCREAMING_SNAKE_CASE_					=    CLIPSegVisionConfig(patch_size=16							)

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

      SCREAMING_SNAKE_CASE_					=    CLIPSegConfig.from_text_vision_configs(
          __UpperCAmelCase    ,    __UpperCAmelCase    ,    use_complex_transposed_convolution=__UpperCAmelCase    ,    reduce_dim=__UpperCAmelCase    ,    )
      return config

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

      return name

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			int							)      ->				Any:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_					=    orig_state_dict.pop(__UpperCAmelCase							)

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

                  if "weight" in key:
                        SCREAMING_SNAKE_CASE_					=    val[:dim, :]
                        SCREAMING_SNAKE_CASE_					=    val[
                            dim : dim * 2, :
                        ]
                        SCREAMING_SNAKE_CASE_					=    val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE_					=    val[:dim]
                        SCREAMING_SNAKE_CASE_					=    val[dim : dim * 2]
                        SCREAMING_SNAKE_CASE_					=    val[-dim:]
            elif "self_attn" in key and "out_proj" not in key:
                  SCREAMING_SNAKE_CASE_					=    key.split('.'							)
                  SCREAMING_SNAKE_CASE_					=    int(key_split[1]							)
                  SCREAMING_SNAKE_CASE_					=    config.reduce_dim
                  if "weight" in key:
                        SCREAMING_SNAKE_CASE_					=    val[:dim, :]
                        SCREAMING_SNAKE_CASE_					=    val[dim : dim * 2, :]
                        SCREAMING_SNAKE_CASE_					=    val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE_					=    val[:dim]
                        SCREAMING_SNAKE_CASE_					=    val[dim : dim * 2]
                        SCREAMING_SNAKE_CASE_					=    val[-dim:]
            else:
                  SCREAMING_SNAKE_CASE_					=    rename_key(__UpperCAmelCase							)
                  if "visual_projection" in new_name or "text_projection" in new_name:
                        SCREAMING_SNAKE_CASE_					=    val.T
                  SCREAMING_SNAKE_CASE_					=    val

      return orig_state_dict

def       UpperCAmelCase_							(   )      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_					=    Image.open(requests.get(__UpperCAmelCase    ,    stream=__UpperCAmelCase							).raw							)
      return image

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Optional[Any]							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    get_clipseg_config(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    CLIPSegForImageSegmentation(__UpperCAmelCase							)
      model.eval()

      SCREAMING_SNAKE_CASE_					=    torch.load(__UpperCAmelCase    ,    map_location='cpu'							)

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

    # rename some keys
      SCREAMING_SNAKE_CASE_					=    convert_state_dict(__UpperCAmelCase    ,    __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_					=    ViTImageProcessor(size=3_52							)
      SCREAMING_SNAKE_CASE_					=    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
      SCREAMING_SNAKE_CASE_					=    CLIPSegProcessor(image_processor=__UpperCAmelCase    ,    tokenizer=__UpperCAmelCase							)

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

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

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_					=    model(**__UpperCAmelCase							)

      # verify values
      SCREAMING_SNAKE_CASE_					=    torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]							)
      SCREAMING_SNAKE_CASE_					=    torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]							)
      if model_name == "clipseg-rd64-refined":
            SCREAMING_SNAKE_CASE_					=    torch.tensor(
                [[-10.04_07, -9.9_4_3_1, -10.26_46], [-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":
            SCREAMING_SNAKE_CASE_					=    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":
            SCREAMING_SNAKE_CASE_					=    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]    ,    __UpperCAmelCase    ,    atol=1E-3							)
      assert torch.allclose(outputs.conditional_embeddings[0, :3]    ,    __UpperCAmelCase    ,    atol=1E-3							)
      assert torch.allclose(outputs.pooled_output[0, :3]    ,    __UpperCAmelCase    ,    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(__UpperCAmelCase							)
            processor.save_pretrained(__UpperCAmelCase							)

      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__				:     int			       =							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__				:     Dict			       =							parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












import bsa
import requests

def       UpperCAmelCase_							(   __UpperCAmelCase		:			bsa.element.ResultSet							)      ->				dict[str, str]:
      return {
          "name": soup.ha.a.text,
          "genre": soup.find('span'    ,    class_='genre'							).text.strip(),
          "rating": soup.strong.text,
          "page_link": f"https://www.imdb.com{soup.a.get('href'							)}",
      }

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int = 5							)      ->				tuple:
      SCREAMING_SNAKE_CASE_					=    int(float(__UpperCAmelCase							)							)
      if num_movies < 1:
            return ()
      SCREAMING_SNAKE_CASE_					=    (
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      SCREAMING_SNAKE_CASE_					=    bsa.BeautifulSoup(requests.get(__UpperCAmelCase							).content    ,    'html.parser'							)
      return tuple(
          get_movie_data_from_soup(__UpperCAmelCase							)
          for movie in source.find_all('div'    ,    class_='lister-item mode-advanced'							)							)


if __name__ == "__main__":
   import json

   lowerCamelCase__				:     int			       =							int(input('How many movies would you like to see? '))
   print(
       ', '.join(
           json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
       )
   )