


"""simple docstring"""



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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def     __lowerCAmelCase		(		lowercase    :    Any					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Tuple    =       torch.exp(lowercase					)
    snake_case       : Union[str, Any]    =       torch.sum(lowercase		,				dim=1					)  # sum of exp(x_i)
    snake_case       : List[Any]    =       torch.sum(x * exp_x		,				dim=1					)  # sum of x_i * exp(x_i)
    return torch.log(lowercase					) - B / A







class _lowerCAmelCase							( nn.Module      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       super().__init__()
       snake_case       : Tuple    =       config.output_attentions
       snake_case       : Optional[int]    =       config.output_hidden_states
       snake_case       : List[Any]    =       nn.ModuleList([BertLayer(UpperCamelCase__		) for _ in range(config.num_hidden_layers		)]		)
       snake_case       : Union[str, Any]    =       nn.ModuleList([BertHighway(UpperCamelCase__		) for _ in range(config.num_hidden_layers		)]		)

       snake_case       : Optional[int]    =       [-1 for _ in range(config.num_hidden_layers		)]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       if (type(UpperCamelCase__		) is float) or (type(UpperCamelCase__		) is int):
           for i in range(len(self.early_exit_entropy		)		):
               snake_case       : List[Any]    =       x
       else:
           snake_case       : Dict    =       x







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Dict    =       pooler.state_dict()
       for highway in self.highway:
           for name, param in highway.pooler.state_dict().items():
               param.copy_(loaded_model[name]		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      )      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[Any]    =       ()
       snake_case       : int    =       ()
       snake_case       : Tuple    =       ()
       for i, layer_module in enumerate(self.layer		):
           if self.output_hidden_states:
               snake_case       : List[Any]    =       all_hidden_states + (hidden_states,)

           snake_case       : Dict    =       layer_module(
               UpperCamelCase__		,      UpperCamelCase__		,      head_mask[i]		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Union[str, Any]    =       layer_outputs[0]

           if self.output_attentions:
               snake_case       : Any    =       all_attentions + (layer_outputs[1],)

           snake_case       : List[Any]    =       (hidden_states,)
           if self.output_hidden_states:
               snake_case       : Any    =       current_outputs + (all_hidden_states,)
           if self.output_attentions:
               snake_case       : Union[str, Any]    =       current_outputs + (all_attentions,)

           snake_case       : Union[str, Any]    =       self.highway[i](UpperCamelCase__		)
           # logits, pooled_output

           if not self.training:
               snake_case       : Tuple    =       highway_exit[0]
               snake_case       : Optional[int]    =       entropy(UpperCamelCase__		)
               snake_case       : Any    =       highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
               snake_case       : Union[str, Any]    =       all_highway_exits + (highway_exit,)

               if highway_entropy < self.early_exit_entropy[i]:
                   snake_case       : List[str]    =       (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                   raise HighwayException(UpperCamelCase__		,      i + 1		)
           else:
               snake_case       : List[Any]    =       all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
           snake_case       : List[Any]    =       all_hidden_states + (hidden_states,)

       snake_case       : Dict    =       (hidden_states,)
       if self.output_hidden_states:
           snake_case       : str    =       outputs + (all_hidden_states,)
       if self.output_attentions:
           snake_case       : Union[str, Any]    =       outputs + (all_attentions,)

       snake_case       : List[str]    =       outputs + (all_highway_exits,)
       return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''       ,				snake_case_       ,				)
class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		)
       snake_case       : str    =       config

       snake_case       : Optional[int]    =       BertEmbeddings(UpperCamelCase__		)
       snake_case       : str    =       DeeBertEncoder(UpperCamelCase__		)
       snake_case       : Union[str, Any]    =       BertPooler(UpperCamelCase__		)

       self.init_weights()







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       self.encoder.init_highway_pooler(self.pooler		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       return self.embeddings.word_embeddings







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : str    =       value







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       for layer, heads in heads_to_prune.items():
           self.encoder.layer[layer].attention.prune_heads(UpperCamelCase__		)







   @add_start_docstrings_to_model_forward(UpperCamelCase__		)
   def 					lowerCamelCase						(      self		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      )      ->						int:

       '''simple docstring'''




       if input_ids is not None and inputs_embeds is not None:
           raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
       elif input_ids is not None:
           snake_case       : List[str]    =       input_ids.size()
       elif inputs_embeds is not None:
           snake_case       : Any    =       inputs_embeds.size()[:-1]
       else:
           raise ValueError("You have to specify either input_ids or inputs_embeds"		)

       snake_case       : str    =       input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
           snake_case       : List[str]    =       torch.ones(UpperCamelCase__		,      device=UpperCamelCase__		)
       if encoder_attention_mask is None:
           snake_case       : str    =       torch.ones(UpperCamelCase__		,      device=UpperCamelCase__		)
       if token_type_ids is None:
           snake_case       : Any    =       torch.zeros(UpperCamelCase__		,      dtype=torch.long		,      device=UpperCamelCase__		)

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       snake_case       : torch.Tensor    =       self.get_extended_attention_mask(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if encoder_attention_mask.dim() == 3:
           snake_case       : Optional[int]    =       encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
           snake_case       : List[Any]    =       encoder_attention_mask[:, None, None, :]

       snake_case       : Union[str, Any]    =       encoder_extended_attention_mask.to(
           dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
       snake_case       : List[str]    =       (1.0 - encoder_extended_attention_mask) * -10000.0

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       snake_case       : Optional[Any]    =       self.get_head_mask(UpperCamelCase__		,      self.config.num_hidden_layers		)

       snake_case       : str    =       self.embeddings(
           input_ids=UpperCamelCase__		,      position_ids=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      inputs_embeds=UpperCamelCase__		)
       snake_case       : int    =       self.encoder(
           UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      head_mask=UpperCamelCase__		,      encoder_hidden_states=UpperCamelCase__		,      encoder_attention_mask=UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       encoder_outputs[0]
       snake_case       : Tuple    =       self.pooler(UpperCamelCase__		)

       snake_case       : Tuple    =       (
           sequence_output,
           pooled_output,
       ) + encoder_outputs[
           1:
       ]  # add hidden_states and attentions if they are here
       return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : Tuple    =       message
       snake_case       : Union[str, Any]    =       exit_layer  # start from 1!







class _lowerCAmelCase							( nn.Module      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       super().__init__()
       snake_case       : Any    =       BertPooler(UpperCamelCase__		)
       snake_case       : Optional[Any]    =       nn.Dropout(config.hidden_dropout_prob		)
       snake_case       : Optional[int]    =       nn.Linear(config.hidden_size		,      config.num_labels		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : List[Any]    =       encoder_outputs[0]
       snake_case       : Any    =       self.pooler(UpperCamelCase__		)
       # "return" pooler_output

       # BertModel
       snake_case       : Tuple    =       (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       snake_case       : Dict    =       bmodel_output[1]

       snake_case       : Tuple    =       self.dropout(UpperCamelCase__		)
       snake_case       : List[str]    =       self.classifier(UpperCamelCase__		)

       return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''       ,				snake_case_       ,				)
class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		)
       snake_case       : List[str]    =       config.num_labels
       snake_case       : Any    =       config.num_hidden_layers

       snake_case       : Union[str, Any]    =       DeeBertModel(UpperCamelCase__		)
       snake_case       : Dict    =       nn.Dropout(config.hidden_dropout_prob		)
       snake_case       : str    =       nn.Linear(config.hidden_size		,      self.config.num_labels		)

       self.init_weights()







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

       '''simple docstring'''




       snake_case       : Any    =       self.num_layers
       try:
           snake_case       : List[Any]    =       self.bert(
               UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      token_type_ids=UpperCamelCase__		,      position_ids=UpperCamelCase__		,      head_mask=UpperCamelCase__		,      inputs_embeds=UpperCamelCase__		,      )
           # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

           snake_case       : Union[str, Any]    =       outputs[1]

           snake_case       : List[str]    =       self.dropout(UpperCamelCase__		)
           snake_case       : int    =       self.classifier(UpperCamelCase__		)
           snake_case       : Optional[int]    =       (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
           snake_case       : int    =       e.message
           snake_case       : Dict    =       e.exit_layer
           snake_case       : int    =       outputs[0]

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

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

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

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









"""simple docstring"""



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

    if model_size == "tiny":
        snake_case       : str    =       96
        snake_case       : List[Any]    =       (2, 2, 6, 2)
        snake_case       : Union[str, Any]    =       (3, 6, 12, 24)
    elif model_size == "small":
        snake_case       : int    =       96
        snake_case       : Optional[Any]    =       (2, 2, 18, 2)
        snake_case       : Any    =       (3, 6, 12, 24)
    elif model_size == "base":
        snake_case       : List[str]    =       128
        snake_case       : int    =       (2, 2, 18, 2)
        snake_case       : Any    =       (4, 8, 16, 32)
    else:
        snake_case       : List[str]    =       192
        snake_case       : str    =       (2, 2, 18, 2)
        snake_case       : Any    =       (6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





    if "patch_embed.proj" in name:
        snake_case       : int    =       name.replace("patch_embed.proj"		,				"embeddings.patch_embeddings.projection"					)
    if "patch_embed.norm" in name:
        snake_case       : Union[str, Any]    =       name.replace("patch_embed.norm"		,				"embeddings.norm"					)
    if "layers" in name:
        snake_case       : Optional[Any]    =       "encoder." + name
    if "attn.proj" in name:
        snake_case       : List[Any]    =       name.replace("attn.proj"		,				"attention.output.dense"					)
    if "attn" in name:
        snake_case       : List[str]    =       name.replace("attn"		,				"attention.self"					)
    if "norm1" in name:
        snake_case       : List[str]    =       name.replace("norm1"		,				"layernorm_before"					)
    if "norm2" in name:
        snake_case       : Optional[int]    =       name.replace("norm2"		,				"layernorm_after"					)
    if "mlp.fc1" in name:
        snake_case       : int    =       name.replace("mlp.fc1"		,				"intermediate.dense"					)
    if "mlp.fc2" in name:
        snake_case       : Tuple    =       name.replace("mlp.fc2"		,				"output.dense"					)

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case       : List[Any]    =       key.split("."					)
            snake_case       : Dict    =       int(key_split[1]					)
            snake_case       : Dict    =       int(key_split[3]					)
            snake_case       : str    =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case       : Dict    =       val[:dim, :]
                snake_case       : Tuple    =       val[
                    dim : dim * 2, :
                ]
                snake_case       : List[str]    =       val[-dim:, :]
            else:
                snake_case       : Dict    =       val[
                    :dim
                ]
                snake_case       : List[str]    =       val[
                    dim : dim * 2
                ]
                snake_case       : Union[str, Any]    =       val[
                    -dim:
                ]
        else:
            snake_case       : Any    =       val

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

    assert torch.allclose(lowercase		,				lowercase		,				atol=1e-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





