"""simple docstring"""






import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   lowercase__:   Optional[int]					=      XCLIPTextConfig()

   # derive patch size from model name
   lowercase__:   Union[str, Any]					=      model_name.find('''patch''' )
   lowercase__:   Optional[Any]					=      int(model_name[start_idx + len('''patch''' ) : start_idx + len('''patch''' ) + 2] )
   lowercase__:   Optional[int]					=      XCLIPVisionConfig(patch_size=__UpperCAmelCase       ,    num_frames=__UpperCAmelCase )

   if "large" in model_name:
      lowercase__:   List[Any]					=      7_6_8
      lowercase__:   Optional[Any]					=      3_0_7_2
      lowercase__:   Optional[Any]					=      1_2

      lowercase__:   List[Any]					=      1_0_2_4
      lowercase__:   List[str]					=      4_0_9_6
      lowercase__:   List[Any]					=      1_6
      lowercase__:   str					=      2_4
      lowercase__:   Tuple					=      7_6_8
      lowercase__:   Tuple					=      3_0_7_2

   if model_name == "xclip-large-patch14-16-frames":
      lowercase__:   Dict					=      3_3_6

   lowercase__:   Union[str, Any]					=      XCLIPConfig.from_text_vision_configs(__UpperCAmelCase       ,    __UpperCAmelCase )

   if "large" in model_name:
      lowercase__:   Any					=      7_6_8

   return config



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[int]:
   # text encoder
   if name == "token_embedding.weight":
      lowercase__:   List[Any]					=      name.replace('''token_embedding.weight'''       ,    '''text_model.embeddings.token_embedding.weight''' )
   if name == "positional_embedding":
      lowercase__:   Union[str, Any]					=      name.replace('''positional_embedding'''       ,    '''text_model.embeddings.position_embedding.weight''' )
   if "ln_1" in name:
      lowercase__:   str					=      name.replace('''ln_1'''       ,    '''layer_norm1''' )
   if "ln_2" in name:
      lowercase__:   List[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__:   List[Any]					=      name.replace('''c_proj'''       ,    '''fc2''' )
   if name.startswith('''transformer.resblocks''' ):
      lowercase__:   Any					=      name.replace('''transformer.resblocks'''       ,    '''text_model.encoder.layers''' )
   if "attn.out_proj" in name and "message" not in name:
      lowercase__:   Any					=      name.replace('''attn.out_proj'''       ,    '''self_attn.out_proj''' )
   if "ln_final" in name:
      lowercase__:   List[str]					=      name.replace('''ln_final'''       ,    '''text_model.final_layer_norm''' )
   # visual encoder
   if name == "visual.class_embedding":
      lowercase__:   Optional[int]					=      name.replace('''visual.class_embedding'''       ,    '''vision_model.embeddings.class_embedding''' )
   if name == "visual.positional_embedding":
      lowercase__:   str					=      name.replace('''visual.positional_embedding'''       ,    '''vision_model.embeddings.position_embedding.weight''' )
   if name.startswith('''visual.transformer.resblocks''' ):
      lowercase__:   Tuple					=      name.replace('''visual.transformer.resblocks'''       ,    '''vision_model.encoder.layers''' )
   if "visual.conv1" in name:
      lowercase__:   Any					=      name.replace('''visual.conv1'''       ,    '''vision_model.embeddings.patch_embedding''' )
   if "visual.ln_pre" in name:
      lowercase__:   Tuple					=      name.replace('''visual.ln_pre'''       ,    '''vision_model.pre_layernorm''' )
   if "visual.ln_post" in name:
      lowercase__:   Optional[Any]					=      name.replace('''visual.ln_post'''       ,    '''vision_model.post_layernorm''' )
   if "visual.proj" in name:
      lowercase__:   Any					=      name.replace('''visual.proj'''       ,    '''visual_projection.weight''' )
   if "text_projection" in name:
      lowercase__:   Optional[Any]					=      name.replace('''text_projection'''       ,    '''text_projection.weight''' )
   # things on top
   if "prompts_visual_proj" in name:
      lowercase__:   List[Any]					=      name.replace('''prompts_visual_proj'''       ,    '''prompts_visual_projection''' )
   if "prompts_visual_ln" in name:
      lowercase__:   Union[str, Any]					=      name.replace('''prompts_visual_ln'''       ,    '''prompts_visual_layernorm''' )
   # mit
   if name == "mit.positional_embedding":
      lowercase__:   Optional[Any]					=      name.replace('''positional'''       ,    '''position''' )
   if name.startswith('''mit.resblocks''' ):
      lowercase__:   List[str]					=      name.replace('''mit.resblocks'''       ,    '''mit.encoder.layers''' )
   # prompts generator
   if name.startswith('''prompts_generator.norm''' ):
      lowercase__:   List[str]					=      name.replace('''prompts_generator.norm'''       ,    '''prompts_generator.layernorm''' )

   return name



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:
   for key in orig_state_dict.copy().keys():
      lowercase__:   str					=      orig_state_dict.pop(__UpperCAmelCase )

      if "attn.in_proj" in key:
         lowercase__:   List[str]					=      key.split('''.''' )
         if key.startswith('''visual''' ):
            lowercase__:   Union[str, Any]					=      key_split[3]
            lowercase__:   Optional[Any]					=      config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  lowercase__:   List[str]					=      val[
                      :dim, :
                  ]
                  lowercase__:   Optional[int]					=      val[
                      dim : dim * 2, :
                  ]
                  lowercase__:   Optional[Any]					=      val[
                      -dim:, :
                  ]
               else:
                  lowercase__:   Optional[int]					=      val[
                      :dim
                  ]
                  lowercase__:   Optional[Any]					=      val[
                      dim : dim * 2
                  ]
                  lowercase__:   Optional[Any]					=      val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  lowercase__:   int					=      val[
                      :dim, :
                  ]
                  lowercase__:   Union[str, Any]					=      val[
                      dim : dim * 2, :
                  ]
                  lowercase__:   List[Any]					=      val[
                      -dim:, :
                  ]
               else:
                  lowercase__:   str					=      val[:dim]
                  lowercase__:   Any					=      val[
                      dim : dim * 2
                  ]
                  lowercase__:   Dict					=      val[-dim:]
         elif key.startswith('''mit''' ):
            lowercase__:   Tuple					=      key_split[2]
            lowercase__:   Tuple					=      config.vision_config.mit_hidden_size
            if "weight" in key:
               lowercase__:   str					=      val[:dim, :]
               lowercase__:   List[Any]					=      val[dim : dim * 2, :]
               lowercase__:   List[str]					=      val[-dim:, :]
            else:
               lowercase__:   Any					=      val[:dim]
               lowercase__:   Optional[Any]					=      val[dim : dim * 2]
               lowercase__:   Optional[int]					=      val[-dim:]
         else:
            lowercase__:   Tuple					=      key_split[2]
            lowercase__:   str					=      config.text_config.hidden_size
            if "weight" in key:
               lowercase__:   int					=      val[:dim, :]
               lowercase__:   Any					=      val[
                   dim : dim * 2, :
               ]
               lowercase__:   int					=      val[-dim:, :]
            else:
               lowercase__:   str					=      val[:dim]
               lowercase__:   Optional[int]					=      val[
                   dim : dim * 2
               ]
               lowercase__:   Optional[Any]					=      val[-dim:]
      else:
         lowercase__:   Dict					=      rename_key(__UpperCAmelCase )
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            lowercase__:   Union[str, Any]					=      val.T
         lowercase__:   Dict					=      val

   return orig_state_dict



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Tuple:
   if num_frames == 8:
      lowercase__:   Optional[Any]					=      '''eating_spaghetti_8_frames.npy'''
   elif num_frames == 1_6:
      lowercase__:   str					=      '''eating_spaghetti.npy'''
   elif num_frames == 3_2:
      lowercase__:   str					=      '''eating_spaghetti_32_frames.npy'''
   lowercase__:   List[Any]					=      hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''       ,    filename=__UpperCAmelCase       ,    repo_type='''dataset'''       ,    )
   lowercase__:   str					=      np.load(__UpperCAmelCase )
   return list(__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=False )  ->  List[Any]:
   lowercase__:   Optional[Any]					=      {
       # fully supervised kinetics-400 checkpoints
       '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
       '''xclip-base-patch32-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
       ),
       '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
       '''xclip-base-patch16-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
       ),
       '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
       '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
       # fully supervised kinetics-600 checkpoints
       '''xclip-base-patch16-kinetics-600''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
       ),
       '''xclip-base-patch16-kinetics-600-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
       ),
       '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
       # few shot
       '''xclip-base-patch16-hmdb-2-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
       ),
       '''xclip-base-patch16-hmdb-4-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
       ),
       '''xclip-base-patch16-hmdb-8-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
       ),
       '''xclip-base-patch16-hmdb-16-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
       ),
       '''xclip-base-patch16-ucf-2-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
       ),
       '''xclip-base-patch16-ucf-4-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
       ),
       '''xclip-base-patch16-ucf-8-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
       ),
       '''xclip-base-patch16-ucf-16-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
       ),
       # zero shot
       '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
   }

   lowercase__:   Optional[Any]					=      model_to_url[model_name]
   lowercase__:   Dict					=      8
   if "16-frames" in model_name:
      lowercase__:   Optional[Any]					=      1_6
   elif "shot" in model_name:
      lowercase__:   Any					=      3_2

   lowercase__:   Any					=      get_xclip_config(__UpperCAmelCase       ,    __UpperCAmelCase )
   lowercase__:   Any					=      XCLIPModel(__UpperCAmelCase )
   model.eval()

   if "drive" in checkpoint_url:
      lowercase__:   int					=      '''pytorch_model.bin'''
      gdown.cached_download(__UpperCAmelCase       ,    __UpperCAmelCase       ,    quiet=__UpperCAmelCase )
      lowercase__:   Optional[int]					=      torch.load(__UpperCAmelCase       ,    map_location='''cpu''' )['''model''']
   else:
      lowercase__:   Optional[int]					=      torch.hub.load_state_dict_from_url(__UpperCAmelCase )['''model''']

   lowercase__:   Any					=      convert_state_dict(__UpperCAmelCase       ,    __UpperCAmelCase )

   lowercase__:   Any					=      XCLIPModel(__UpperCAmelCase )
   lowercase__, lowercase__:   Dict					=      model.load_state_dict(__UpperCAmelCase       ,    strict=__UpperCAmelCase )
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   lowercase__:   Union[str, Any]					=      3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
   lowercase__:   str					=      VideoMAEImageProcessor(size=__UpperCAmelCase )
   lowercase__:   List[str]					=      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' )
   lowercase__:   List[str]					=      CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32''' )
   lowercase__:   Union[str, Any]					=      XCLIPProcessor(image_processor=__UpperCAmelCase       ,    tokenizer=__UpperCAmelCase )

   lowercase__:   str					=      prepare_video(__UpperCAmelCase )
   lowercase__:   List[str]					=      processor(
       text=['''playing sports''', '''eating spaghetti''', '''go shopping''']       ,    videos=__UpperCAmelCase       ,    return_tensors='''pt'''       ,    padding=__UpperCAmelCase )

   print('''Shape of pixel values:'''       ,    inputs.pixel_values.shape )

   with torch.no_grad():
      lowercase__:   Any					=      model(**__UpperCAmelCase )

   # Verify outputs
   lowercase__:   Any					=      outputs.logits_per_video
   lowercase__:   str					=      logits_per_video.softmax(dim=1 )
   print('''Probs:'''       ,    __UpperCAmelCase )
   # kinetics-400
   if model_name == "xclip-base-patch32":
      lowercase__:   Tuple					=      torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]] )
   elif model_name == "xclip-base-patch32-16-frames":
      lowercase__:   int					=      torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
   elif model_name == "xclip-base-patch16":
      lowercase__:   List[str]					=      torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]] )
   elif model_name == "xclip-base-patch16-16-frames":
      lowercase__:   Optional[Any]					=      torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
   elif model_name == "xclip-large-patch14":
      lowercase__:   Tuple					=      torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]] )
   elif model_name == "xclip-large-patch14-16-frames":
      lowercase__:   Union[str, Any]					=      torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      lowercase__:   List[str]					=      torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]] )
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      lowercase__:   Dict					=      torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
   elif model_name == "xclip-large-patch14-kinetics-600":
      lowercase__:   Dict					=      torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]] )
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      lowercase__:   Optional[int]					=      torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      lowercase__:   List[str]					=      torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      lowercase__:   Dict					=      torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      lowercase__:   List[Any]					=      torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      lowercase__:   Any					=      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      lowercase__:   List[Any]					=      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      lowercase__:   int					=      torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]] )
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      lowercase__:   Optional[int]					=      torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      lowercase__:   int					=      torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
   else:
      raise ValueError(F"""Model name {model_name} not supported""" )
   assert torch.allclose(__UpperCAmelCase       ,    __UpperCAmelCase       ,    atol=1e-3 )
   print('''Looks ok!''' )

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
      model.save_pretrained(__UpperCAmelCase )

   if push_to_hub:
      print('''Pushing model, processor and slow tokenizer files to the hub...''' )
      model.push_to_hub(__UpperCAmelCase       ,    organization='''nielsr''' )
      processor.push_to_hub(__UpperCAmelCase       ,    organization='''nielsr''' )
      slow_tokenizer.push_to_hub(__UpperCAmelCase       ,    organization='''nielsr''' )


if __name__ == "__main__":
       __A			   = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="xclip-base-patch32",
           type=str,
           help="Name of the model.",
       )
       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_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   if n == 1 or not isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
      return 0
   elif n == 2:
      return 1
   else:
      lowercase__:   List[Any]					=      [0, 1]
      for i in range(2       ,    n + 1 ):
         sequence.append(sequence[i - 1] + sequence[i - 2] )

      return sequence[n]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Union[str, Any]					=      0
   lowercase__:   List[Any]					=      2

   while digits < n:
      index += 1
      lowercase__:   Dict					=      len(str(fibonacci(__UpperCAmelCase ) ) )

   return index



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 1_0_0_0 )  ->  int:
   return fibonacci_digits_index(__UpperCAmelCase )


if __name__ == "__main__":
       print(solution(int(str(input()).strip())))
