

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 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      XCLIPTextConfig()

 # derive patch size from model name
 SCREAMING_SNAKE_CASE		    =      model_name.find('patch'						)
 SCREAMING_SNAKE_CASE		    =      int(model_name[start_idx + len('patch'						) : start_idx + len('patch'						) + 2]						)
 SCREAMING_SNAKE_CASE		    =      XCLIPVisionConfig(patch_size=_UpperCamelCase		,				num_frames=_UpperCamelCase						)

 if "large" in model_name:
  SCREAMING_SNAKE_CASE		    =      7_68
  SCREAMING_SNAKE_CASE		    =      30_72
  SCREAMING_SNAKE_CASE		    =      12

  SCREAMING_SNAKE_CASE		    =      10_24
  SCREAMING_SNAKE_CASE		    =      40_96
  SCREAMING_SNAKE_CASE		    =      16
  SCREAMING_SNAKE_CASE		    =      24
  SCREAMING_SNAKE_CASE		    =      7_68
  SCREAMING_SNAKE_CASE		    =      30_72

 if model_name == "xclip-large-patch14-16-frames":
  SCREAMING_SNAKE_CASE		    =      3_36

 SCREAMING_SNAKE_CASE		    =      XCLIPConfig.from_text_vision_configs(_UpperCamelCase		,				_UpperCamelCase						)

 if "large" in model_name:
  SCREAMING_SNAKE_CASE		    =      7_68

 return config


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any						)		->				Union[str, Any]:
 '''simple docstring'''


 if name == "token_embedding.weight":
  SCREAMING_SNAKE_CASE		    =      name.replace('token_embedding.weight'		,				'text_model.embeddings.token_embedding.weight'						)
 if name == "positional_embedding":
  SCREAMING_SNAKE_CASE		    =      name.replace('positional_embedding'		,				'text_model.embeddings.position_embedding.weight'						)
 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 name.startswith('transformer.resblocks'						):
  SCREAMING_SNAKE_CASE		    =      name.replace('transformer.resblocks'		,				'text_model.encoder.layers'						)
 if "attn.out_proj" in name and "message" not in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('attn.out_proj'		,				'self_attn.out_proj'						)
 if "ln_final" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('ln_final'		,				'text_model.final_layer_norm'						)
 # visual encoder
 if name == "visual.class_embedding":
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.class_embedding'		,				'vision_model.embeddings.class_embedding'						)
 if name == "visual.positional_embedding":
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.positional_embedding'		,				'vision_model.embeddings.position_embedding.weight'						)
 if name.startswith('visual.transformer.resblocks'						):
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.transformer.resblocks'		,				'vision_model.encoder.layers'						)
 if "visual.conv1" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.conv1'		,				'vision_model.embeddings.patch_embedding'						)
 if "visual.ln_pre" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.ln_pre'		,				'vision_model.pre_layernorm'						)
 if "visual.ln_post" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('visual.ln_post'		,				'vision_model.post_layernorm'						)
 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'						)
 # things on top
 if "prompts_visual_proj" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('prompts_visual_proj'		,				'prompts_visual_projection'						)
 if "prompts_visual_ln" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('prompts_visual_ln'		,				'prompts_visual_layernorm'						)
 # mit
 if name == "mit.positional_embedding":
  SCREAMING_SNAKE_CASE		    =      name.replace('positional'		,				'position'						)
 if name.startswith('mit.resblocks'						):
  SCREAMING_SNAKE_CASE		    =      name.replace('mit.resblocks'		,				'mit.encoder.layers'						)
 # prompts generator
 if name.startswith('prompts_generator.norm'						):
  SCREAMING_SNAKE_CASE		    =      name.replace('prompts_generator.norm'		,				'prompts_generator.layernorm'						)

 return name


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Dict						)		->				Union[str, Any]:
 '''simple docstring'''


 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE		    =      orig_state_dict.pop(_UpperCamelCase						)

  if "attn.in_proj" in key:
   SCREAMING_SNAKE_CASE		    =      key.split('.'						)
   if key.startswith('visual'						):
    SCREAMING_SNAKE_CASE		    =      key_split[3]
    SCREAMING_SNAKE_CASE		    =      config.vision_config.hidden_size
    if "message_attn" in key:
     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:
     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 key.startswith('mit'						):
    SCREAMING_SNAKE_CASE		    =      key_split[2]
    SCREAMING_SNAKE_CASE		    =      config.vision_config.mit_hidden_size
    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		    =      key_split[2]
    SCREAMING_SNAKE_CASE		    =      config.text_config.hidden_size
    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 new_key_name in ["visual_projection.weight", "text_projection.weight"]:
    SCREAMING_SNAKE_CASE		    =      val.T
   SCREAMING_SNAKE_CASE		    =      val

 return orig_state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				Optional[Any]:
 '''simple docstring'''


 if num_frames == 8:
  SCREAMING_SNAKE_CASE		    =      'eating_spaghetti_8_frames.npy'
 elif num_frames == 16:
  SCREAMING_SNAKE_CASE		    =      'eating_spaghetti.npy'
 elif num_frames == 32:
  SCREAMING_SNAKE_CASE		    =      'eating_spaghetti_32_frames.npy'
 SCREAMING_SNAKE_CASE		    =      hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'		,				filename=_UpperCamelCase		,				repo_type='dataset'		,				)
 SCREAMING_SNAKE_CASE		    =      np.load(_UpperCamelCase						)
 return list(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	List[Any]=None		,				_UpperCamelCase		:	Any=False						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      {
     # 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',
 }

 SCREAMING_SNAKE_CASE		    =      model_to_url[model_name]
 SCREAMING_SNAKE_CASE		    =      8
 if "16-frames" in model_name:
  SCREAMING_SNAKE_CASE		    =      16
 elif "shot" in model_name:
  SCREAMING_SNAKE_CASE		    =      32

 SCREAMING_SNAKE_CASE		    =      get_xclip_config(_UpperCamelCase		,				_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      XCLIPModel(_UpperCamelCase						)
 model.eval()

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

 SCREAMING_SNAKE_CASE		    =      convert_state_dict(_UpperCamelCase		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      XCLIPModel(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      model.load_state_dict(_UpperCamelCase		,				strict=_UpperCamelCase						)
 assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
 model.eval()

 SCREAMING_SNAKE_CASE		    =      3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
 SCREAMING_SNAKE_CASE		    =      VideoMAEImageProcessor(size=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						)
 SCREAMING_SNAKE_CASE		    =      CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'						)
 SCREAMING_SNAKE_CASE		    =      XCLIPProcessor(image_processor=_UpperCamelCase		,				tokenizer=_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      prepare_video(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      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():
  SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						)

 # Verify outputs
 SCREAMING_SNAKE_CASE		    =      outputs.logits_per_video
 SCREAMING_SNAKE_CASE		    =      logits_per_video.softmax(dim=1						)
 print('Probs:'		,				_UpperCamelCase						)
 # kinetics-400
 if model_name == "xclip-base-patch32":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.00_19, 0.99_51, 0.00_30]]						)
 elif model_name == "xclip-base-patch32-16-frames":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
 elif model_name == "xclip-base-patch16":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.00_83, 0.96_81, 0.02_36]]						)
 elif model_name == "xclip-base-patch16-16-frames":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
 elif model_name == "xclip-large-patch14":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.00_62, 0.98_64, 0.00_75]]						)
 elif model_name == "xclip-large-patch14-16-frames":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
 # kinetics-600
 elif model_name == "xclip-base-patch16-kinetics-600":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.05_55, 0.89_14, 0.05_31]]						)
 elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
 elif model_name == "xclip-large-patch14-kinetics-600":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.00_36, 0.99_20, 0.00_45]]						)
 # few shot
 elif model_name == "xclip-base-patch16-hmdb-2-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-4-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-8-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-16-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-2-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-4-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-8-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[0.00_27, 0.99_04, 0.00_70]]						)
 elif model_name == "xclip-base-patch16-ucf-16-shot":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
 # zero shot
 elif model_name == "xclip-base-patch16-zero-shot":
  SCREAMING_SNAKE_CASE		    =      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_     :    Tuple     		=   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_     :    Union[str, Any]     		=   parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import string


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''
 for i in sequence:
  SCREAMING_SNAKE_CASE		    =      ord(_UpperCamelCase						)
  if 65 <= extract <= 90:
   output += chr(1_55 - extract						)
  elif 97 <= extract <= 1_22:
   output += chr(2_19 - extract						)
  else:
   output += i
 return output


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      string.ascii_letters
 SCREAMING_SNAKE_CASE		    =      string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(_UpperCamelCase						)] if c in letters else c for c in sequence						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 from timeit import timeit

 print('Running performance benchmarks...'						)
 SCREAMING_SNAKE_CASE		    =      'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)
 print(f""">      atbash(): {timeit('atbash(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)


if __name__ == "__main__":
      for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
            print(F"""{example} encrypted in atbash: {atbash(example)}""")
      benchmark()

