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       A__   (						snake_case_			:		Any							,					snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: str=   XCLIPTextConfig()

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

 if "large" in model_name:
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   768
  SCREAMING_SNAKE_CASE__: Dict=   3_072
  SCREAMING_SNAKE_CASE__: str=   12

  SCREAMING_SNAKE_CASE__: Any=   1_024
  SCREAMING_SNAKE_CASE__: Optional[int]=   4_096
  SCREAMING_SNAKE_CASE__: Optional[int]=   16
  SCREAMING_SNAKE_CASE__: Tuple=   24
  SCREAMING_SNAKE_CASE__: List[str]=   768
  SCREAMING_SNAKE_CASE__: int=   3_072

 if model_name == "xclip-large-patch14-16-frames":
  SCREAMING_SNAKE_CASE__: Optional[int]=   336

 SCREAMING_SNAKE_CASE__: Optional[int]=   XCLIPConfig.from_text_vision_configs(snake_case_							,					snake_case_   )

 if "large" in model_name:
  SCREAMING_SNAKE_CASE__: Dict=   768

 return config







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

 return name







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		int   ):
 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE__: List[Any]=   orig_state_dict.pop(snake_case_   )

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

 return orig_state_dict







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







def       A__   (						snake_case_			:		str							,					snake_case_			:		Tuple=None							,					snake_case_			:		List[str]=False   ):
 SCREAMING_SNAKE_CASE__: List[str]=   {
     # 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__: int=   model_to_url[model_name]
 SCREAMING_SNAKE_CASE__: Optional[int]=   8
 if "16-frames" in model_name:
  SCREAMING_SNAKE_CASE__: Optional[Any]=   16
 elif "shot" in model_name:
  SCREAMING_SNAKE_CASE__: List[Any]=   32

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   get_xclip_config(snake_case_							,					snake_case_   )
 SCREAMING_SNAKE_CASE__: List[Any]=   XCLIPModel(snake_case_   )
 model.eval()

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

 SCREAMING_SNAKE_CASE__: Optional[Any]=   convert_state_dict(snake_case_							,					snake_case_   )

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

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

 SCREAMING_SNAKE_CASE__: Tuple=   prepare_video(snake_case_   )
 SCREAMING_SNAKE_CASE__: Dict=   processor(
     text=['''playing sports''', '''eating spaghetti''', '''go shopping''']							,					videos=snake_case_							,					return_tensors='''pt'''							,					padding=snake_case_   )

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

 with torch.no_grad():
  SCREAMING_SNAKE_CASE__: List[Any]=   model(**snake_case_   )

 # Verify outputs
 SCREAMING_SNAKE_CASE__: int=   outputs.logits_per_video
 SCREAMING_SNAKE_CASE__: Optional[int]=   logits_per_video.softmax(dim=1   )
 print('''Probs:'''							,					snake_case_   )
 # kinetics-400
 if model_name == "xclip-base-patch32":
  SCREAMING_SNAKE_CASE__: List[Any]=   torch.tensor([[0.00_19, 0.99_51, 0.00_30]]   )
 elif model_name == "xclip-base-patch32-16-frames":
  SCREAMING_SNAKE_CASE__: List[str]=   torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]   )
 elif model_name == "xclip-base-patch16":
  SCREAMING_SNAKE_CASE__: str=   torch.tensor([[0.00_83, 0.96_81, 0.02_36]]   )
 elif model_name == "xclip-base-patch16-16-frames":
  SCREAMING_SNAKE_CASE__: List[Any]=   torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]   )
 elif model_name == "xclip-large-patch14":
  SCREAMING_SNAKE_CASE__: Any=   torch.tensor([[0.00_62, 0.98_64, 0.00_75]]   )
 elif model_name == "xclip-large-patch14-16-frames":
  SCREAMING_SNAKE_CASE__: Dict=   torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]   )
 # kinetics-600
 elif model_name == "xclip-base-patch16-kinetics-600":
  SCREAMING_SNAKE_CASE__: int=   torch.tensor([[0.05_55, 0.89_14, 0.05_31]]   )
 elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
  SCREAMING_SNAKE_CASE__: List[Any]=   torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]   )
 elif model_name == "xclip-large-patch14-kinetics-600":
  SCREAMING_SNAKE_CASE__: List[Any]=   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__: List[str]=   torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]   )
 elif model_name == "xclip-base-patch16-hmdb-4-shot":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]   )
 elif model_name == "xclip-base-patch16-hmdb-8-shot":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]   )
 elif model_name == "xclip-base-patch16-hmdb-16-shot":
  SCREAMING_SNAKE_CASE__: str=   torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]   )
 elif model_name == "xclip-base-patch16-ucf-2-shot":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]   )
 elif model_name == "xclip-base-patch16-ucf-4-shot":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]   )
 elif model_name == "xclip-base-patch16-ucf-8-shot":
  SCREAMING_SNAKE_CASE__: Optional[int]=   torch.tensor([[0.00_27, 0.99_04, 0.00_70]]   )
 elif model_name == "xclip-base-patch16-ucf-16-shot":
  SCREAMING_SNAKE_CASE__: Any=   torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]   )
 # zero shot
 elif model_name == "xclip-base-patch16-zero-shot":
  SCREAMING_SNAKE_CASE__: str=   torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]   )
 else:
  raise ValueError(F'Model name {model_name} not supported'   )
 assert torch.allclose(snake_case_							,					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(snake_case_   )

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


if __name__ == "__main__":
      lowercase_  : Optional[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.'
      )

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


'''simple docstring'''

from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= RobertaConfig
      UpperCAmelCase_   :   Tuple   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	RobertaEmbeddings(lowerCamelCase_				)
         self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= RobertaConfig
      UpperCAmelCase_   :   Optional[Any]   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	config.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	config.num_hidden_layers

         UpperCAmelCase__				:					str 					=	DeeRobertaModel(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Dropout(config.hidden_dropout_prob				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(config.hidden_size       ,self.config.num_labels				)




      @add_start_docstrings_to_model_forward(lowerCamelCase_				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=-1       ,lowerCamelCase_=False       ,)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_layers
         try:
            UpperCAmelCase__				:					Union[str, Any] 					=	self.roberta(
                lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,position_ids=lowerCamelCase_       ,head_mask=lowerCamelCase_       ,inputs_embeds=lowerCamelCase_       ,)

            UpperCAmelCase__				:					List[Any] 					=	outputs[1]

            UpperCAmelCase__				:					Tuple 					=	self.dropout(lowerCamelCase_				)
            UpperCAmelCase__				:					Any 					=	self.classifier(lowerCamelCase_				)
            UpperCAmelCase__				:					Optional[int] 					=	(logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
            UpperCAmelCase__				:					int 					=	e.message
            UpperCAmelCase__				:					Optional[int] 					=	e.exit_layer
            UpperCAmelCase__				:					Optional[int] 					=	outputs[0]

         if not self.training:
            UpperCAmelCase__				:					Dict 					=	entropy(lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	[]
            UpperCAmelCase__				:					str 					=	[]
         if labels is not None:
            if self.num_labels == 1:
               #  We are doing regression
               UpperCAmelCase__				:					str 					=	MSELoss()
               UpperCAmelCase__				:					Union[str, Any] 					=	loss_fct(logits.view(-1				)       ,labels.view(-1				)				)
            else:
               UpperCAmelCase__				:					Any 					=	CrossEntropyLoss()
               UpperCAmelCase__				:					Optional[Any] 					=	loss_fct(logits.view(-1       ,self.num_labels				)       ,labels.view(-1				)				)

            # work with highway exits
            UpperCAmelCase__				:					List[str] 					=	[]
            for highway_exit in outputs[-1]:
               UpperCAmelCase__				:					Optional[int] 					=	highway_exit[0]
               if not self.training:
                  highway_logits_all.append(lowerCamelCase_				)
                  highway_entropy.append(highway_exit[2]				)
               if self.num_labels == 1:
                  #  We are doing regression
                  UpperCAmelCase__				:					Any 					=	MSELoss()
                  UpperCAmelCase__				:					Optional[Any] 					=	loss_fct(highway_logits.view(-1				)       ,labels.view(-1				)				)
               else:
                  UpperCAmelCase__				:					Any 					=	CrossEntropyLoss()
                  UpperCAmelCase__				:					int 					=	loss_fct(highway_logits.view(-1       ,self.num_labels				)       ,labels.view(-1				)				)
               highway_losses.append(lowerCamelCase_				)

            if train_highway:
               UpperCAmelCase__				:					Dict 					=	(sum(highway_losses[:-1]				),) + outputs
               # exclude the final highway, of course
            else:
               UpperCAmelCase__				:					Dict 					=	(loss,) + outputs
         if not self.training:
            UpperCAmelCase__				:					Tuple 					=	outputs + ((original_entropy, highway_entropy), exit_layer)
            if output_layer >= 0:
               UpperCAmelCase__				:					int 					=	(
                   (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
               )  # use the highway of the last layer

         return outputs  # (loss), logits, (hidden_states), (attentions), entropy




