




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				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    XCLIPTextConfig()

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

       if "large" in model_name:
              A__       =    768
              A__       =    3_072
              A__       =    12

              A__       =    1_024
              A__       =    4_096
              A__       =    16
              A__       =    24
              A__       =    768
              A__       =    3_072

       if model_name == "xclip-large-patch14-16-frames":
              A__       =    336

       A__       =    XCLIPConfig.from_text_vision_configs(lowercase_ ,				lowercase_						)

       if "large" in model_name:
              A__       =    768

       return config







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

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

       return name







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__       =    orig_state_dict.pop(lowercase_						)

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

       return orig_state_dict







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=None ,				lowercase_=False						)   ->       str:



       """simple docstring"""

       A__       =    {
           # 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''',
       }

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

       A__       =    get_xclip_config(lowercase_ ,				lowercase_						)
       A__       =    XCLIPModel(lowercase_						)
       model.eval()

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

       A__       =    convert_state_dict(lowercase_ ,				lowercase_						)

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

       A__       =    336 if model_name == '''xclip-large-patch14-16-frames''' else 224
       A__       =    VideoMAEImageProcessor(size=lowercase_						)
       A__       =    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
       A__       =    CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''						)
       A__       =    XCLIPProcessor(image_processor=lowercase_ ,				tokenizer=lowercase_						)

       A__       =    prepare_video(lowercase_						)
       A__       =    processor(
           text=['''playing sports''', '''eating spaghetti''', '''go shopping'''] ,				videos=lowercase_ ,				return_tensors='''pt''' ,				padding=lowercase_						)

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

       with torch.no_grad():
              A__       =    model(**lowercase_						)

       # Verify outputs
       A__       =    outputs.logits_per_video
       A__       =    logits_per_video.softmax(dim=1						)
       print('''Probs:''' ,				lowercase_						)
       # kinetics-400
       if model_name == "xclip-base-patch32":
              A__       =    torch.tensor([[0.00_19, 0.99_51, 0.00_30]]						)
       elif model_name == "xclip-base-patch32-16-frames":
              A__       =    torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]						)
       elif model_name == "xclip-base-patch16":
              A__       =    torch.tensor([[0.00_83, 0.96_81, 0.02_36]]						)
       elif model_name == "xclip-base-patch16-16-frames":
              A__       =    torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]						)
       elif model_name == "xclip-large-patch14":
              A__       =    torch.tensor([[0.00_62, 0.98_64, 0.00_75]]						)
       elif model_name == "xclip-large-patch14-16-frames":
              A__       =    torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]						)
       # kinetics-600
       elif model_name == "xclip-base-patch16-kinetics-600":
              A__       =    torch.tensor([[0.05_55, 0.89_14, 0.05_31]]						)
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              A__       =    torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]						)
       elif model_name == "xclip-large-patch14-kinetics-600":
              A__       =    torch.tensor([[0.00_36, 0.99_20, 0.00_45]]						)
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              A__       =    torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-4-shot":
              A__       =    torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-8-shot":
              A__       =    torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-16-shot":
              A__       =    torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-2-shot":
              A__       =    torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-4-shot":
              A__       =    torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-8-shot":
              A__       =    torch.tensor([[0.00_27, 0.99_04, 0.00_70]]						)
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              A__       =    torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]						)
       # zero shot
       elif model_name == "xclip-base-patch16-zero-shot":
              A__       =    torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]						)
       else:
              raise ValueError(f"""Model name {model_name} not supported"""						)
       assert torch.allclose(lowercase_ ,				lowercase_ ,				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(lowercase_						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		int							       =   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."""
  )

  _lowerCamelCase	:		List[str]							       =   parser.parse_args()
  convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

       A__       =    args.model_name_or_path.rstrip('''/'''						)
       A__       =    args.target_model_path

       print(f"""Load fine-pruned model from {model_name_or_path}"""						)
       A__       =    torch.load(os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       A__       =    {}

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "classifier" in name or "qa_output" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "bias" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              else:
                     if pruning_method == "magnitude":
                            A__       =    MagnitudeBinarizer.apply(inputs=lowercase_ ,				threshold=lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    TopKBinarizer.apply(lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    ThresholdBinarizer.apply(lowercase_ ,				lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            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__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     else:
                            raise ValueError('''Unknown pruning method'''						)

       if target_model_path is None:
              A__       =    os.path.join(
                  os.path.dirname(lowercase_						) ,				f"""bertarized_{os.path.basename(lowercase_						)}"""						)

       if not os.path.isdir(lowercase_						):
              shutil.copytree(lowercase_ ,				lowercase_						)
              print(f"""\nCreated folder {target_model_path}"""						)

       torch.save(lowercase_ ,				os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       print('''\nPruned model saved! See you later!'''						)


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--pruning_method""",
      choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
      type=str,
      required=True,
      help=(
          """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
          """ sigmoied_threshold = Soft movement pruning)"""
      ),
  )
  parser.add_argument(
      """--threshold""",
      type=float,
      required=False,
      help=(
          """For `magnitude` and `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(
      """--model_name_or_path""",
      type=str,
      required=True,
      help="""Folder containing the model that was previously fine-pruned""",
  )
  parser.add_argument(
      """--target_model_path""",
      default=None,
      type=str,
      required=False,
      help="""Folder containing the model that was previously fine-pruned""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


