
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase										=		{"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
     "UniSpeechForCTC",
     "UniSpeechForPreTraining",
     "UniSpeechForSequenceClassification",
     "UniSpeechModel",
     "UniSpeechPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech import (
      UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechForCTC,
      UniSpeechForPreTraining,
      UniSpeechForSequenceClassification,
      UniSpeechModel,
      UniSpeechPreTrainedModel,
  )

else:
 import sys

 __lowerCamelCase										=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




"""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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      XCLIPTextConfig()

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

 if "large" in model_name:
  __magic_name__     =      768
  __magic_name__     =      3072
  __magic_name__     =      12

  __magic_name__     =      1024
  __magic_name__     =      4096
  __magic_name__     =      16
  __magic_name__     =      24
  __magic_name__     =      768
  __magic_name__     =      3072

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

 __magic_name__     =      XCLIPConfig.from_text_vision_configs(__UpperCamelCase      ,   __UpperCamelCase						)

 if "large" in model_name:
  __magic_name__     =      768

 return config





def 	lowercase      (   __UpperCamelCase						) ->      Dict:
 # text encoder
 if name == "token_embedding.weight":
  __magic_name__     =      name.replace('''token_embedding.weight'''      ,   '''text_model.embeddings.token_embedding.weight'''						)
 if name == "positional_embedding":
  __magic_name__     =      name.replace('''positional_embedding'''      ,   '''text_model.embeddings.position_embedding.weight'''						)
 if "ln_1" in name:
  __magic_name__     =      name.replace('''ln_1'''      ,   '''layer_norm1'''						)
 if "ln_2" in name:
  __magic_name__     =      name.replace('''ln_2'''      ,   '''layer_norm2'''						)
 if "c_fc" in name:
  __magic_name__     =      name.replace('''c_fc'''      ,   '''fc1'''						)
 if "c_proj" in name:
  __magic_name__     =      name.replace('''c_proj'''      ,   '''fc2'''						)
 if name.startswith('''transformer.resblocks'''						):
  __magic_name__     =      name.replace('''transformer.resblocks'''      ,   '''text_model.encoder.layers'''						)
 if "attn.out_proj" in name and "message" not in name:
  __magic_name__     =      name.replace('''attn.out_proj'''      ,   '''self_attn.out_proj'''						)
 if "ln_final" in name:
  __magic_name__     =      name.replace('''ln_final'''      ,   '''text_model.final_layer_norm'''						)
 # visual encoder
 if name == "visual.class_embedding":
  __magic_name__     =      name.replace('''visual.class_embedding'''      ,   '''vision_model.embeddings.class_embedding'''						)
 if name == "visual.positional_embedding":
  __magic_name__     =      name.replace('''visual.positional_embedding'''      ,   '''vision_model.embeddings.position_embedding.weight'''						)
 if name.startswith('''visual.transformer.resblocks'''						):
  __magic_name__     =      name.replace('''visual.transformer.resblocks'''      ,   '''vision_model.encoder.layers'''						)
 if "visual.conv1" in name:
  __magic_name__     =      name.replace('''visual.conv1'''      ,   '''vision_model.embeddings.patch_embedding'''						)
 if "visual.ln_pre" in name:
  __magic_name__     =      name.replace('''visual.ln_pre'''      ,   '''vision_model.pre_layernorm'''						)
 if "visual.ln_post" in name:
  __magic_name__     =      name.replace('''visual.ln_post'''      ,   '''vision_model.post_layernorm'''						)
 if "visual.proj" in name:
  __magic_name__     =      name.replace('''visual.proj'''      ,   '''visual_projection.weight'''						)
 if "text_projection" in name:
  __magic_name__     =      name.replace('''text_projection'''      ,   '''text_projection.weight'''						)
 # things on top
 if "prompts_visual_proj" in name:
  __magic_name__     =      name.replace('''prompts_visual_proj'''      ,   '''prompts_visual_projection'''						)
 if "prompts_visual_ln" in name:
  __magic_name__     =      name.replace('''prompts_visual_ln'''      ,   '''prompts_visual_layernorm'''						)
 # mit
 if name == "mit.positional_embedding":
  __magic_name__     =      name.replace('''positional'''      ,   '''position'''						)
 if name.startswith('''mit.resblocks'''						):
  __magic_name__     =      name.replace('''mit.resblocks'''      ,   '''mit.encoder.layers'''						)
 # prompts generator
 if name.startswith('''prompts_generator.norm'''						):
  __magic_name__     =      name.replace('''prompts_generator.norm'''      ,   '''prompts_generator.layernorm'''						)

 return name





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 for key in orig_state_dict.copy().keys():
  __magic_name__     =      orig_state_dict.pop(__UpperCamelCase						)

  if "attn.in_proj" in key:
   __magic_name__     =      key.split('''.'''						)
   if key.startswith('''visual'''						):
    __magic_name__     =      key_split[3]
    __magic_name__     =      config.vision_config.hidden_size
    if "message_attn" in key:
     if "weight" in key:
      __magic_name__     =      val[
          :dim, :
      ]
      __magic_name__     =      val[
          dim : dim * 2, :
      ]
      __magic_name__     =      val[
          -dim:, :
      ]
     else:
      __magic_name__     =      val[
          :dim
      ]
      __magic_name__     =      val[
          dim : dim * 2
      ]
      __magic_name__     =      val[
          -dim:
      ]
    else:
     if "weight" in key:
      __magic_name__     =      val[
          :dim, :
      ]
      __magic_name__     =      val[
          dim : dim * 2, :
      ]
      __magic_name__     =      val[
          -dim:, :
      ]
     else:
      __magic_name__     =      val[:dim]
      __magic_name__     =      val[
          dim : dim * 2
      ]
      __magic_name__     =      val[-dim:]
   elif key.startswith('''mit'''						):
    __magic_name__     =      key_split[2]
    __magic_name__     =      config.vision_config.mit_hidden_size
    if "weight" in key:
     __magic_name__     =      val[:dim, :]
     __magic_name__     =      val[dim : dim * 2, :]
     __magic_name__     =      val[-dim:, :]
    else:
     __magic_name__     =      val[:dim]
     __magic_name__     =      val[dim : dim * 2]
     __magic_name__     =      val[-dim:]
   else:
    __magic_name__     =      key_split[2]
    __magic_name__     =      config.text_config.hidden_size
    if "weight" in key:
     __magic_name__     =      val[:dim, :]
     __magic_name__     =      val[
         dim : dim * 2, :
     ]
     __magic_name__     =      val[-dim:, :]
    else:
     __magic_name__     =      val[:dim]
     __magic_name__     =      val[
         dim : dim * 2
     ]
     __magic_name__     =      val[-dim:]
  else:
   __magic_name__     =      rename_key(__UpperCamelCase						)
   if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
    __magic_name__     =      val.T
   __magic_name__     =      val

 return orig_state_dict





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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=None      ,   __UpperCamelCase=False						) ->      Tuple:
 __magic_name__     =      {
     # 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''',
 }

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

 __magic_name__     =      get_xclip_config(__UpperCamelCase      ,   __UpperCamelCase						)
 __magic_name__     =      XCLIPModel(__UpperCamelCase						)
 model.eval()

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

 __magic_name__     =      convert_state_dict(__UpperCamelCase      ,   __UpperCamelCase						)

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

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

 __magic_name__     =      prepare_video(__UpperCamelCase						)
 __magic_name__     =      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():
  __magic_name__     =      model(**__UpperCamelCase						)

 # Verify outputs
 __magic_name__     =      outputs.logits_per_video
 __magic_name__     =      logits_per_video.softmax(dim=1						)
 print('''Probs:'''      ,   __UpperCamelCase						)
 # kinetics-400
 if model_name == "xclip-base-patch32":
  __magic_name__     =      torch.tensor([[0.0_019, 0.9_951, 0.0_030]]						)
 elif model_name == "xclip-base-patch32-16-frames":
  __magic_name__     =      torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
 elif model_name == "xclip-base-patch16":
  __magic_name__     =      torch.tensor([[0.0_083, 0.9_681, 0.0_236]]						)
 elif model_name == "xclip-base-patch16-16-frames":
  __magic_name__     =      torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
 elif model_name == "xclip-large-patch14":
  __magic_name__     =      torch.tensor([[0.0_062, 0.9_864, 0.0_075]]						)
 elif model_name == "xclip-large-patch14-16-frames":
  __magic_name__     =      torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
 # kinetics-600
 elif model_name == "xclip-base-patch16-kinetics-600":
  __magic_name__     =      torch.tensor([[0.0_555, 0.8_914, 0.0_531]]						)
 elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
  __magic_name__     =      torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
 elif model_name == "xclip-large-patch14-kinetics-600":
  __magic_name__     =      torch.tensor([[0.0_036, 0.9_920, 0.0_045]]						)
 # few shot
 elif model_name == "xclip-base-patch16-hmdb-2-shot":
  __magic_name__     =      torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-4-shot":
  __magic_name__     =      torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-8-shot":
  __magic_name__     =      torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
 elif model_name == "xclip-base-patch16-hmdb-16-shot":
  __magic_name__     =      torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-2-shot":
  __magic_name__     =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-4-shot":
  __magic_name__     =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
 elif model_name == "xclip-base-patch16-ucf-8-shot":
  __magic_name__     =      torch.tensor([[0.0_027, 0.9_904, 0.0_070]]						)
 elif model_name == "xclip-base-patch16-ucf-16-shot":
  __magic_name__     =      torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
 # zero shot
 elif model_name == "xclip-base-patch16-zero-shot":
  __magic_name__     =      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__":
 __lowerCamelCase										=		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										=		parser.parse_args()
 convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

