




import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD

class   __a(					nn.Module       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				0.0
    lowerCAmelCase        =				1
    lowerCAmelCase        =				1
    lowerCAmelCase        =				True
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				jnp.floataa





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

         for i in range(self.num_layers  ):
              UpperCAmelCase_      :							int       =      self.in_channels if i == 0 else self.out_channels

              UpperCAmelCase_      :							Any       =      FlaxResnetBlockaD(
                  in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
              resnets.append(_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Any       =      FlaxTransformeraDModel(
                  in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
              attentions.append(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      resnets
         UpperCAmelCase_      :							Any       =      attentions

         if self.add_downsample:
              UpperCAmelCase_      :							str       =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype  )





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=True  )			->		int:
         UpperCAmelCase_      :							Dict       =      ()

         for resnet, attn in zip(self.resnets       ,self.attentions  ):
              UpperCAmelCase_      :							Optional[Any]       =      resnet(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Any       =      attn(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )
              output_states += (hidden_states,)

         if self.add_downsample:
              UpperCAmelCase_      :							Optional[Any]       =      self.downsamplers_a(_SCREAMING_SNAKE_CASE  )
              output_states += (hidden_states,)

         return hidden_states, output_states


class   __a(					nn.Module       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				0.0
    lowerCAmelCase        =				1
    lowerCAmelCase        =				True
    lowerCAmelCase        =				jnp.floataa





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Dict       =      []

         for i in range(self.num_layers  ):
              UpperCAmelCase_      :							List[Any]       =      self.in_channels if i == 0 else self.out_channels

              UpperCAmelCase_      :							Any       =      FlaxResnetBlockaD(
                  in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
              resnets.append(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      resnets

         if self.add_downsample:
              UpperCAmelCase_      :							Tuple       =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype  )





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=True  )			->		List[Any]:
         UpperCAmelCase_      :							Optional[Any]       =      ()

         for resnet in self.resnets:
              UpperCAmelCase_      :							List[str]       =      resnet(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )
              output_states += (hidden_states,)

         if self.add_downsample:
              UpperCAmelCase_      :							Optional[Any]       =      self.downsamplers_a(_SCREAMING_SNAKE_CASE  )
              output_states += (hidden_states,)

         return hidden_states, output_states


class   __a(					nn.Module       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				0.0
    lowerCAmelCase        =				1
    lowerCAmelCase        =				1
    lowerCAmelCase        =				True
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				jnp.floataa





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							str       =      []
         UpperCAmelCase_      :							Any       =      []

         for i in range(self.num_layers  ):
              UpperCAmelCase_      :							Dict       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
              UpperCAmelCase_      :							Union[str, Any]       =      self.prev_output_channel if i == 0 else self.out_channels

              UpperCAmelCase_      :							Union[str, Any]       =      FlaxResnetBlockaD(
                  in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
              resnets.append(_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Tuple       =      FlaxTransformeraDModel(
                  in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
              attentions.append(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      resnets
         UpperCAmelCase_      :							List[Any]       =      attentions

         if self.add_upsample:
              UpperCAmelCase_      :							List[str]       =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype  )





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=True  )			->		Optional[int]:
         for resnet, attn in zip(self.resnets       ,self.attentions  ):
              # pop res hidden states
              UpperCAmelCase_      :							str       =      res_hidden_states_tuple[-1]
              UpperCAmelCase_      :							List[str]       =      res_hidden_states_tuple[:-1]
              UpperCAmelCase_      :							Optional[int]       =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1  )

              UpperCAmelCase_      :							int       =      resnet(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      attn(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )

         if self.add_upsample:
              UpperCAmelCase_      :							Any       =      self.upsamplers_a(_SCREAMING_SNAKE_CASE  )

         return hidden_states


class   __a(					nn.Module       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				0.0
    lowerCAmelCase        =				1
    lowerCAmelCase        =				True
    lowerCAmelCase        =				jnp.floataa





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							List[Any]       =      []

         for i in range(self.num_layers  ):
              UpperCAmelCase_      :							Any       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
              UpperCAmelCase_      :							Optional[Any]       =      self.prev_output_channel if i == 0 else self.out_channels

              UpperCAmelCase_      :							Union[str, Any]       =      FlaxResnetBlockaD(
                  in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
              resnets.append(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      resnets

         if self.add_upsample:
              UpperCAmelCase_      :							Optional[Any]       =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype  )





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=True  )			->		Tuple:
         for resnet in self.resnets:
              # pop res hidden states
              UpperCAmelCase_      :							List[Any]       =      res_hidden_states_tuple[-1]
              UpperCAmelCase_      :							Dict       =      res_hidden_states_tuple[:-1]
              UpperCAmelCase_      :							str       =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1  )

              UpperCAmelCase_      :							Dict       =      resnet(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )

         if self.add_upsample:
              UpperCAmelCase_      :							Optional[Any]       =      self.upsamplers_a(_SCREAMING_SNAKE_CASE  )

         return hidden_states


class   __a(					nn.Module       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				0.0
    lowerCAmelCase        =				1
    lowerCAmelCase        =				1
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				jnp.floataa





    def 							a__				(				self  )			->		Union[str, Any]:
         # there is always at least one resnet
         UpperCAmelCase_      :							Union[str, Any]       =      [
             FlaxResnetBlockaD(
                 in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
         ]

         UpperCAmelCase_      :							Optional[int]       =      []

         for _ in range(self.num_layers  ):
              UpperCAmelCase_      :							Optional[Any]       =      FlaxTransformeraDModel(
                  in_channels=self.in_channels       ,n_heads=self.num_attention_heads       ,d_head=self.in_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
              attentions.append(_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							List[str]       =      FlaxResnetBlockaD(
                  in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
              resnets.append(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      resnets
         UpperCAmelCase_      :							Tuple       =      attentions





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=True  )			->		int:
         UpperCAmelCase_      :							List[str]       =      self.resnets[0](_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         for attn, resnet in zip(self.attentions       ,self.resnets[1:]  ):
              UpperCAmelCase_      :							int       =      attn(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      resnet(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,deterministic=_SCREAMING_SNAKE_CASE  )

         return hidden_states





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)