




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				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      torch.exp(_lowercase				)
     UpperCAmelCase_      :							str       =      torch.sum(_lowercase			,						dim=1				)  # sum of exp(x_i)
     UpperCAmelCase_      :							int       =      torch.sum(x * exp_x			,						dim=1				)  # sum of x_i * exp(x_i)
     return torch.log(_lowercase				) - B / A

class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         super().__init__()
         UpperCAmelCase_      :							List[Any]       =      config.output_attentions
         UpperCAmelCase_      :							Tuple       =      config.output_hidden_states
         UpperCAmelCase_      :							Optional[Any]       =      nn.ModuleList([BertLayer(_SCREAMING_SNAKE_CASE  ) for _ in range(config.num_hidden_layers  )]  )
         UpperCAmelCase_      :							str       =      nn.ModuleList([BertHighway(_SCREAMING_SNAKE_CASE  ) for _ in range(config.num_hidden_layers  )]  )

         UpperCAmelCase_      :							Dict       =      [-1 for _ in range(config.num_hidden_layers  )]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         if (type(_SCREAMING_SNAKE_CASE  ) is float) or (type(_SCREAMING_SNAKE_CASE  ) is int):
              for i in range(len(self.early_exit_entropy  )  ):
                   UpperCAmelCase_      :							Optional[int]       =      x
         else:
              UpperCAmelCase_      :							int       =      x





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							List[str]       =      pooler.state_dict()
         for highway in self.highway:
              for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,)			->		List[str]:
         UpperCAmelCase_      :							List[str]       =      ()
         UpperCAmelCase_      :							Optional[Any]       =      ()
         UpperCAmelCase_      :							List[str]       =      ()
         for i, layer_module in enumerate(self.layer  ):
              if self.output_hidden_states:
                   UpperCAmelCase_      :							str       =      all_hidden_states + (hidden_states,)

              UpperCAmelCase_      :							List[Any]       =      layer_module(
                  _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,head_mask[i]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Any       =      layer_outputs[0]

              if self.output_attentions:
                   UpperCAmelCase_      :							str       =      all_attentions + (layer_outputs[1],)

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

              UpperCAmelCase_      :							Optional[Any]       =      self.highway[i](_SCREAMING_SNAKE_CASE  )
              # logits, pooled_output

              if not self.training:
                   UpperCAmelCase_      :							Dict       =      highway_exit[0]
                   UpperCAmelCase_      :							Union[str, Any]       =      entropy(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							str       =      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   UpperCAmelCase_      :							Any       =      all_highway_exits + (highway_exit,)

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

        # Add last layer
         if self.output_hidden_states:
              UpperCAmelCase_      :							int       =      all_hidden_states + (hidden_states,)

         UpperCAmelCase_      :							Optional[Any]       =      (hidden_states,)
         if self.output_hidden_states:
              UpperCAmelCase_      :							Optional[Any]       =      outputs + (all_hidden_states,)
         if self.output_attentions:
              UpperCAmelCase_      :							Dict       =      outputs + (all_attentions,)

         UpperCAmelCase_      :							Union[str, Any]       =      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). '''    ,						_a    ,						)
class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         super().__init__(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      config

         UpperCAmelCase_      :							Dict       =      BertEmbeddings(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      DeeBertEncoder(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      BertPooler(_SCREAMING_SNAKE_CASE  )

         self.init_weights()





    def 							a__				(				self  )			->		List[Any]:
         self.encoder.init_highway_pooler(self.pooler  )





    def 							a__				(				self  )			->		str:
         return self.embeddings.word_embeddings





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      value





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(_SCREAMING_SNAKE_CASE  )





    @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE  )
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Optional[int]:
         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:
              UpperCAmelCase_      :							str       =      input_ids.size()
         elif inputs_embeds is not None:
              UpperCAmelCase_      :							str       =      inputs_embeds.size()[:-1]
         else:
              raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

         UpperCAmelCase_      :							Any       =      input_ids.device if input_ids is not None else inputs_embeds.device

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

         # 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.
         UpperCAmelCase_      :							torch.Tensor       =      self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         # 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:
              UpperCAmelCase_      :							Optional[Any]       =      encoder_attention_mask[:, None, :, :]
         if encoder_attention_mask.dim() == 2:
              UpperCAmelCase_      :							str       =      encoder_attention_mask[:, None, None, :]

         UpperCAmelCase_      :							Optional[Any]       =      encoder_extended_attention_mask.to(
             dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
         UpperCAmelCase_      :							Any       =      (1.0 - encoder_extended_attention_mask) * -1_00_00.0

         # 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]
         UpperCAmelCase_      :							Optional[Any]       =      self.get_head_mask(_SCREAMING_SNAKE_CASE       ,self.config.num_hidden_layers  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.embeddings(
             input_ids=_SCREAMING_SNAKE_CASE       ,position_ids=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,inputs_embeds=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.encoder(
             _SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,head_mask=_SCREAMING_SNAKE_CASE       ,encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							List[str]       =      encoder_outputs[0]
         UpperCAmelCase_      :							Any       =      self.pooler(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      (
             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   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							List[str]       =      message
         UpperCAmelCase_      :							Dict       =      exit_layer  # start from 1!


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__()
         UpperCAmelCase_      :							Optional[Any]       =      BertPooler(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      nn.Dropout(config.hidden_dropout_prob  )
         UpperCAmelCase_      :							Optional[Any]       =      nn.Linear(config.hidden_size       ,config.num_labels  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         # Pooler
         UpperCAmelCase_      :							List[Any]       =      encoder_outputs[0]
         UpperCAmelCase_      :							List[str]       =      self.pooler(_SCREAMING_SNAKE_CASE  )
         # "return" pooler_output

         # BertModel
         UpperCAmelCase_      :							List[str]       =      (pooler_input, pooler_output) + encoder_outputs[1:]
         # "return" bmodel_output

         # Dropout and classification
         UpperCAmelCase_      :							Optional[Any]       =      bmodel_output[1]

         UpperCAmelCase_      :							Dict       =      self.dropout(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      self.classifier(_SCREAMING_SNAKE_CASE  )

         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. '''    ,						_a    ,						)
class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         super().__init__(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      config.num_labels
         UpperCAmelCase_      :							Optional[int]       =      config.num_hidden_layers

         UpperCAmelCase_      :							Any       =      DeeBertModel(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      nn.Dropout(config.hidden_dropout_prob  )
         UpperCAmelCase_      :							str       =      nn.Linear(config.hidden_size       ,self.config.num_labels  )

         self.init_weights()





    @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE  )
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=-1       ,_SCREAMING_SNAKE_CASE=False       ,)			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[Any]       =      self.num_layers
         try:
              UpperCAmelCase_      :							Union[str, Any]       =      self.bert(
                  _SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,position_ids=_SCREAMING_SNAKE_CASE       ,head_mask=_SCREAMING_SNAKE_CASE       ,inputs_embeds=_SCREAMING_SNAKE_CASE       ,)
              # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

              UpperCAmelCase_      :							Any       =      outputs[1]

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

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

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

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





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				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

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

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

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

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      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__":
     __a          =			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.'
     )

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