





"""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 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				int        =XCLIPTextConfig()

  # derive patch size from model name
  lowerCamelCase__:				Tuple        =model_name.find("patch"		)
  lowerCamelCase__:				Any        =int(model_name[start_idx + len("patch"		) : start_idx + len("patch"		) + 2]		)
  lowerCamelCase__:				Any        =XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE__     ,   num_frames=SCREAMING_SNAKE_CASE__		)

  if "large" in model_name:
    lowerCamelCase__:				Optional[Any]        =768
    lowerCamelCase__:				int        =3072
    lowerCamelCase__:				str        =12

    lowerCamelCase__:				Any        =1024
    lowerCamelCase__:				List[Any]        =4096
    lowerCamelCase__:				List[Any]        =16
    lowerCamelCase__:				str        =24
    lowerCamelCase__:				List[str]        =768
    lowerCamelCase__:				Optional[Any]        =3072

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

  lowerCamelCase__:				int        =XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__		)

  if "large" in model_name:
    lowerCamelCase__:				List[str]        =768

  return config





def 					lowerCAmelCase_			(	__a		)					->		List[str]:







  """simple docstring"""





  if name == "token_embedding.weight":
    lowerCamelCase__:				Union[str, Any]        =name.replace("token_embedding.weight"     ,   "text_model.embeddings.token_embedding.weight"		)
  if name == "positional_embedding":
    lowerCamelCase__:				List[Any]        =name.replace("positional_embedding"     ,   "text_model.embeddings.position_embedding.weight"		)
  if "ln_1" in name:
    lowerCamelCase__:				str        =name.replace("ln_1"     ,   "layer_norm1"		)
  if "ln_2" in name:
    lowerCamelCase__:				Optional[Any]        =name.replace("ln_2"     ,   "layer_norm2"		)
  if "c_fc" in name:
    lowerCamelCase__:				List[Any]        =name.replace("c_fc"     ,   "fc1"		)
  if "c_proj" in name:
    lowerCamelCase__:				int        =name.replace("c_proj"     ,   "fc2"		)
  if name.startswith("transformer.resblocks"		):
    lowerCamelCase__:				List[Any]        =name.replace("transformer.resblocks"     ,   "text_model.encoder.layers"		)
  if "attn.out_proj" in name and "message" not in name:
    lowerCamelCase__:				List[Any]        =name.replace("attn.out_proj"     ,   "self_attn.out_proj"		)
  if "ln_final" in name:
    lowerCamelCase__:				Tuple        =name.replace("ln_final"     ,   "text_model.final_layer_norm"		)
  # visual encoder
  if name == "visual.class_embedding":
    lowerCamelCase__:				str        =name.replace("visual.class_embedding"     ,   "vision_model.embeddings.class_embedding"		)
  if name == "visual.positional_embedding":
    lowerCamelCase__:				int        =name.replace("visual.positional_embedding"     ,   "vision_model.embeddings.position_embedding.weight"		)
  if name.startswith("visual.transformer.resblocks"		):
    lowerCamelCase__:				Union[str, Any]        =name.replace("visual.transformer.resblocks"     ,   "vision_model.encoder.layers"		)
  if "visual.conv1" in name:
    lowerCamelCase__:				List[str]        =name.replace("visual.conv1"     ,   "vision_model.embeddings.patch_embedding"		)
  if "visual.ln_pre" in name:
    lowerCamelCase__:				Tuple        =name.replace("visual.ln_pre"     ,   "vision_model.pre_layernorm"		)
  if "visual.ln_post" in name:
    lowerCamelCase__:				Dict        =name.replace("visual.ln_post"     ,   "vision_model.post_layernorm"		)
  if "visual.proj" in name:
    lowerCamelCase__:				List[str]        =name.replace("visual.proj"     ,   "visual_projection.weight"		)
  if "text_projection" in name:
    lowerCamelCase__:				Tuple        =name.replace("text_projection"     ,   "text_projection.weight"		)
  # things on top
  if "prompts_visual_proj" in name:
    lowerCamelCase__:				Any        =name.replace("prompts_visual_proj"     ,   "prompts_visual_projection"		)
  if "prompts_visual_ln" in name:
    lowerCamelCase__:				Any        =name.replace("prompts_visual_ln"     ,   "prompts_visual_layernorm"		)
  # mit
  if name == "mit.positional_embedding":
    lowerCamelCase__:				int        =name.replace("positional"     ,   "position"		)
  if name.startswith("mit.resblocks"		):
    lowerCamelCase__:				Union[str, Any]        =name.replace("mit.resblocks"     ,   "mit.encoder.layers"		)
  # prompts generator
  if name.startswith("prompts_generator.norm"		):
    lowerCamelCase__:				List[Any]        =name.replace("prompts_generator.norm"     ,   "prompts_generator.layernorm"		)

  return name





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[int]:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:				Dict        =orig_state_dict.pop(SCREAMING_SNAKE_CASE__		)

    if "attn.in_proj" in key:
      lowerCamelCase__:				Optional[int]        =key.split("."		)
      if key.startswith("visual"		):
        lowerCamelCase__:				str        =key_split[3]
        lowerCamelCase__:				Union[str, Any]        =config.vision_config.hidden_size
        if "message_attn" in key:
          if "weight" in key:
            lowerCamelCase__:				Dict        =val[
                :dim, :
            ]
            lowerCamelCase__:				Tuple        =val[
                dim : dim * 2, :
            ]
            lowerCamelCase__:				Any        =val[
                -dim:, :
            ]
          else:
            lowerCamelCase__:				Optional[Any]        =val[
                :dim
            ]
            lowerCamelCase__:				int        =val[
                dim : dim * 2
            ]
            lowerCamelCase__:				List[Any]        =val[
                -dim:
            ]
        else:
          if "weight" in key:
            lowerCamelCase__:				Any        =val[
                :dim, :
            ]
            lowerCamelCase__:				Dict        =val[
                dim : dim * 2, :
            ]
            lowerCamelCase__:				str        =val[
                -dim:, :
            ]
          else:
            lowerCamelCase__:				Dict        =val[:dim]
            lowerCamelCase__:				Tuple        =val[
                dim : dim * 2
            ]
            lowerCamelCase__:				Optional[int]        =val[-dim:]
      elif key.startswith("mit"		):
        lowerCamelCase__:				Union[str, Any]        =key_split[2]
        lowerCamelCase__:				List[str]        =config.vision_config.mit_hidden_size
        if "weight" in key:
          lowerCamelCase__:				Tuple        =val[:dim, :]
          lowerCamelCase__:				List[Any]        =val[dim : dim * 2, :]
          lowerCamelCase__:				Tuple        =val[-dim:, :]
        else:
          lowerCamelCase__:				Optional[int]        =val[:dim]
          lowerCamelCase__:				int        =val[dim : dim * 2]
          lowerCamelCase__:				int        =val[-dim:]
      else:
        lowerCamelCase__:				int        =key_split[2]
        lowerCamelCase__:				Any        =config.text_config.hidden_size
        if "weight" in key:
          lowerCamelCase__:				Any        =val[:dim, :]
          lowerCamelCase__:				Any        =val[
              dim : dim * 2, :
          ]
          lowerCamelCase__:				Optional[int]        =val[-dim:, :]
        else:
          lowerCamelCase__:				Union[str, Any]        =val[:dim]
          lowerCamelCase__:				str        =val[
              dim : dim * 2
          ]
          lowerCamelCase__:				Tuple        =val[-dim:]
    else:
      lowerCamelCase__:				List[Any]        =rename_key(SCREAMING_SNAKE_CASE__		)
      if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
        lowerCamelCase__:				Any        =val.T
      lowerCamelCase__:				int        =val

  return orig_state_dict





def 					lowerCAmelCase_			(	__a		)					->		List[Any]:







  """simple docstring"""





  if num_frames == 8:
    lowerCamelCase__:				Optional[Any]        ='''eating_spaghetti_8_frames.npy'''
  elif num_frames == 16:
    lowerCamelCase__:				List[Any]        ='''eating_spaghetti.npy'''
  elif num_frames == 32:
    lowerCamelCase__:				Optional[Any]        ='''eating_spaghetti_32_frames.npy'''
  lowerCamelCase__:				Tuple        =hf_hub_download(
      repo_id="hf-internal-testing/spaghetti-video"     ,   filename=SCREAMING_SNAKE_CASE__     ,   repo_type="dataset"     ,   )
  lowerCamelCase__:				List[Any]        =np.load(SCREAMING_SNAKE_CASE__		)
  return list(SCREAMING_SNAKE_CASE__		)





def 					lowerCAmelCase_			(	__a     ,   __a=None     ,   __a=False		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				List[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''',
  }

  lowerCamelCase__:				Tuple        =model_to_url[model_name]
  lowerCamelCase__:				List[str]        =8
  if "16-frames" in model_name:
    lowerCamelCase__:				Tuple        =16
  elif "shot" in model_name:
    lowerCamelCase__:				Optional[int]        =32

  lowerCamelCase__:				Optional[int]        =get_xclip_config(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__		)
  lowerCamelCase__:				Union[str, Any]        =XCLIPModel(SCREAMING_SNAKE_CASE__		)
  model.eval()

  if "drive" in checkpoint_url:
    lowerCamelCase__:				List[Any]        ='''pytorch_model.bin'''
    gdown.cached_download(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   quiet=SCREAMING_SNAKE_CASE__		)
    lowerCamelCase__:				str        =torch.load(SCREAMING_SNAKE_CASE__     ,   map_location="cpu"		)['''model''']
  else:
    lowerCamelCase__:				List[str]        =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__		)['''model''']

  lowerCamelCase__:				Dict        =convert_state_dict(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__		)

  lowerCamelCase__:				Union[str, Any]        =XCLIPModel(SCREAMING_SNAKE_CASE__		)
  lowerCamelCase__:				Dict        =model.load_state_dict(SCREAMING_SNAKE_CASE__     ,   strict=SCREAMING_SNAKE_CASE__		)
  assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
  model.eval()

  lowerCamelCase__:				Optional[Any]        =336 if model_name == '''xclip-large-patch14-16-frames''' else 224
  lowerCamelCase__:				Union[str, Any]        =VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE__		)
  lowerCamelCase__:				Optional[Any]        =CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"		)
  lowerCamelCase__:				Union[str, Any]        =CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"		)
  lowerCamelCase__:				str        =XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE__     ,   tokenizer=SCREAMING_SNAKE_CASE__		)

  lowerCamelCase__:				Tuple        =prepare_video(SCREAMING_SNAKE_CASE__		)
  lowerCamelCase__:				List[Any]        =processor(
      text=["playing sports", "eating spaghetti", "go shopping"]     ,   videos=SCREAMING_SNAKE_CASE__     ,   return_tensors="pt"     ,   padding=SCREAMING_SNAKE_CASE__		)

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

  with torch.no_grad():
    lowerCamelCase__:				Optional[Any]        =model(**SCREAMING_SNAKE_CASE__		)

  # Verify outputs
  lowerCamelCase__:				Tuple        =outputs.logits_per_video
  lowerCamelCase__:				str        =logits_per_video.softmax(dim=1		)
  print("Probs:"     ,   SCREAMING_SNAKE_CASE__		)
  # kinetics-400
  if model_name == "xclip-base-patch32":
    lowerCamelCase__:				Optional[int]        =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":
    lowerCamelCase__:				Optional[Any]        =torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]		)
  elif model_name == "xclip-base-patch16":
    lowerCamelCase__:				List[Any]        =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":
    lowerCamelCase__:				Optional[int]        =torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]		)
  elif model_name == "xclip-large-patch14":
    lowerCamelCase__:				Dict        =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":
    lowerCamelCase__:				Tuple        =torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]		)
  # kinetics-600
  elif model_name == "xclip-base-patch16-kinetics-600":
    lowerCamelCase__:				Dict        =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":
    lowerCamelCase__:				Dict        =torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]		)
  elif model_name == "xclip-large-patch14-kinetics-600":
    lowerCamelCase__:				str        =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":
    lowerCamelCase__:				Tuple        =torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-4-shot":
    lowerCamelCase__:				Any        =torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-8-shot":
    lowerCamelCase__:				Union[str, Any]        =torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-16-shot":
    lowerCamelCase__:				List[str]        =torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]		)
  elif model_name == "xclip-base-patch16-ucf-2-shot":
    lowerCamelCase__:				List[str]        =torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]		)
  elif model_name == "xclip-base-patch16-ucf-4-shot":
    lowerCamelCase__:				int        =torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]		)
  elif model_name == "xclip-base-patch16-ucf-8-shot":
    lowerCamelCase__:				Optional[Any]        =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":
    lowerCamelCase__:				Optional[Any]        =torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]		)
  # zero shot
  elif model_name == "xclip-base-patch16-zero-shot":
    lowerCamelCase__:				int        =torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]		)
  else:
    raise ValueError(F"""Model name {model_name} not supported"""		)
  assert torch.allclose(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   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(SCREAMING_SNAKE_CASE__		)

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A     =    logging.get_logger(__name__)

__A     =    {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "distilbert"
 lowercase_		=      {
     "hidden_size": "dim",
     "num_attention_heads": "n_heads",
     "num_hidden_layers": "n_layers",
 }

 def __init__(self    :       Any	,			UpperCAmelCase_    :       str=30_522	,			UpperCAmelCase_    :       Union[str, Any]=512	,			UpperCAmelCase_    :       int=False	,			UpperCAmelCase_    :       Optional[Any]=6	,			UpperCAmelCase_    :       Optional[Any]=12	,			UpperCAmelCase_    :       Any=768	,			UpperCAmelCase_    :       List[Any]=4 * 768	,			UpperCAmelCase_    :       Tuple=0.1	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Any="gelu"	,			UpperCAmelCase_    :       int=0.02	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       Optional[int]=0.2	,			UpperCAmelCase_    :       int=0	,			**UpperCAmelCase_    :       List[Any]	,			)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				int        =vocab_size
   lowerCamelCase__:				Any        =max_position_embeddings
   lowerCamelCase__:				Optional[int]        =sinusoidal_pos_embds
   lowerCamelCase__:				str        =n_layers
   lowerCamelCase__:				str        =n_heads
   lowerCamelCase__:				str        =dim
   lowerCamelCase__:				Optional[Any]        =hidden_dim
   lowerCamelCase__:				Dict        =dropout
   lowerCamelCase__:				Optional[Any]        =attention_dropout
   lowerCamelCase__:				int        =activation
   lowerCamelCase__:				Dict        =initializer_range
   lowerCamelCase__:				Optional[Any]        =qa_dropout
   lowerCamelCase__:				int        =seq_classif_dropout
   super().__init__(**UpperCAmelCase_	,			pad_token_id=UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:				Dict        ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:				Optional[int]        ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ])

