




import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
     from ... import PreTrainedTokenizerBase, TensorType

__a          =			logging.get_logger(__name__)

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''vision-encoder-decoder'''
    lowerCAmelCase        =				True





    def __init__(				self       ,**_SCREAMING_SNAKE_CASE  )			->		List[str]:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  f'''A configuraton of type {self.model_type} cannot be instantiated because '''
                  f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''  )

         UpperCAmelCase_      :							Optional[int]       =      kwargs.pop('''encoder'''  )
         UpperCAmelCase_      :							Optional[Any]       =      encoder_config.pop('''model_type'''  )
         UpperCAmelCase_      :							Optional[Any]       =      kwargs.pop('''decoder'''  )
         UpperCAmelCase_      :							Optional[Any]       =      decoder_config.pop('''model_type'''  )

         UpperCAmelCase_      :							Dict       =      AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      True





    @classmethod
    def 							a__				(				cls       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		PretrainedConfig:
         logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
         UpperCAmelCase_      :							Union[str, Any]       =      True
         UpperCAmelCase_      :							Tuple       =      True

         return cls(encoder=encoder_config.to_dict()       ,decoder=decoder_config.to_dict()       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							List[str]       =      copy.deepcopy(self.__dict__  )
         UpperCAmelCase_      :							Optional[int]       =      self.encoder.to_dict()
         UpperCAmelCase_      :							str       =      self.decoder.to_dict()
         UpperCAmelCase_      :							Tuple       =      self.__class__.model_type
         return output


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.11'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-4





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}  )


class   __a(					_a       ):






    """simple docstring"""





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         UpperCAmelCase_      :							Dict       =      OrderedDict()
         UpperCAmelCase_      :							Any       =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         UpperCAmelCase_      :							Any       =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         UpperCAmelCase_      :							Union[str, Any]       =      {0: '''batch''', 1: '''encoder_sequence'''}

         return common_inputs





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,)			->		Mapping[str, Any]:
         import torch

         UpperCAmelCase_      :							Any       =      OrderedDict()

         UpperCAmelCase_      :							Tuple       =      super().generate_dummy_inputs(
             _SCREAMING_SNAKE_CASE       ,batch_size=_SCREAMING_SNAKE_CASE       ,seq_length=_SCREAMING_SNAKE_CASE       ,is_pair=_SCREAMING_SNAKE_CASE       ,framework=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      dummy_input['''input_ids'''].shape
         UpperCAmelCase_      :							Tuple       =      (batch, encoder_sequence, self._config.encoder_hidden_size)
         UpperCAmelCase_      :							int       =      dummy_input.pop('''input_ids'''  )
         UpperCAmelCase_      :							str       =      dummy_input.pop('''attention_mask'''  )
         UpperCAmelCase_      :							List[Any]       =      torch.zeros(_SCREAMING_SNAKE_CASE  )

         return common_inputs


class   __a(					_a       ):






    """simple docstring"""





    @property
    def 							a__				(				self  )			->		None:
         pass





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		OnnxConfig:
         return VisionEncoderDecoderEncoderOnnxConfig(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = "default"  )			->		OnnxConfig:
         UpperCAmelCase_      :							List[Any]       =      encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





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)