




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         if isinstance(__lowerCamelCase       ,__lowerCamelCase  ):
              # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
              # modified it, it will not be accepted here again, since `auto` values would have been overridden
              UpperCAmelCase_      :							Optional[Any]       =      deepcopy(__lowerCamelCase  )
         elif os.path.exists(__lowerCamelCase  ):
              with io.open(__lowerCamelCase       ,'''r'''       ,encoding='''utf-8'''  ) as f:
                   UpperCAmelCase_      :							Any       =      json.load(__lowerCamelCase  )
         else:
              try:
                   UpperCAmelCase_      :							Any       =      baseaa.urlsafe_baadecode(__lowerCamelCase  ).decode('''utf-8'''  )
                   UpperCAmelCase_      :							Optional[int]       =      json.loads(__lowerCamelCase  )
              except (UnicodeDecodeError, AttributeError, ValueError):
                   raise ValueError(
                       f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''  )

         UpperCAmelCase_      :							List[Any]       =      config

         self.set_stage_and_offload()





    def 							a__				(				self  )			->		List[Any]:
         # zero stage - this is done as early as possible, before model is created, to allow
         # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
         # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
         UpperCAmelCase_      :							Any       =      self.get_value('''zero_optimization.stage'''       ,-1  )

         # offload
         UpperCAmelCase_      :							Optional[Any]       =      False
         if self.is_zeroa() or self.is_zeroa():
              UpperCAmelCase_      :							int       =      set(['''cpu''', '''nvme''']  )
              UpperCAmelCase_      :							Any       =      set(
                  [
                      self.get_value('''zero_optimization.offload_optimizer.device'''  ),
                      self.get_value('''zero_optimization.offload_param.device'''  ),
                  ]  )
              if len(offload_devices & offload_devices_valid  ) > 0:
                   UpperCAmelCase_      :							Any       =      True





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

         # find the config node of interest if it exists
         UpperCAmelCase_      :							List[Any]       =      ds_key_long.split('''.'''  )
         UpperCAmelCase_      :							Optional[Any]       =      nodes.pop()
         for node in nodes:
              UpperCAmelCase_      :							Optional[int]       =      config.get(__lowerCamelCase  )
              if config is None:
                   return None, ds_key

         return config, ds_key





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		Union[str, Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Union[str, Any]       =      self.find_config_node(__lowerCamelCase  )
         if config is None:
              return default
         return config.get(__lowerCamelCase       ,__lowerCamelCase  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		Any:
         UpperCAmelCase_      :							Tuple       =      self.config

         # find the config node of interest if it exists
         UpperCAmelCase_      :							Union[str, Any]       =      ds_key_long.split('''.'''  )
         for node in nodes:
              UpperCAmelCase_      :							List[Any]       =      config
              UpperCAmelCase_      :							Union[str, Any]       =      config.get(__lowerCamelCase  )
              if config is None:
                   if must_exist:
                        raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''  )
                   else:
                        return

        # if found remove it
         if parent_config is not None:
              parent_config.pop(__lowerCamelCase  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[Any]       =      self.get_value(__lowerCamelCase  )
         return False if value is None else bool(__lowerCamelCase  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Tuple       =      self.get_value(__lowerCamelCase  )
         return False if value is None else not bool(__lowerCamelCase  )





    def 							a__				(				self  )			->		List[Any]:
         return self._stage == 2





    def 							a__				(				self  )			->		Optional[Any]:
         return self._stage == 3





    def 							a__				(				self  )			->		Tuple:
         return self._offload


class   __a:






    """simple docstring"""





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         # runs backpropagation and handles mixed precision
         self.engine.backward(__lowerCamelCase       ,**__lowerCamelCase  )

         # Deepspeed's `engine.step` performs the following operations:
         # - gradient accumulation check
         # - gradient clipping
         # - optimizer step
         # - zero grad
         # - checking overflow
         # - lr_scheduler step (only if engine.lr_scheduler is not None)
         self.engine.step()
         # and this plugin overrides the above calls with no-ops when Accelerate runs under
         # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
         # training loop that works transparently under many training regimes.


class   __a(					UpperCamelCase__       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         super().__init__(__lowerCamelCase       ,device_placement=__lowerCamelCase       ,scaler=__lowerCamelCase  )
         UpperCAmelCase_      :							Optional[int]       =      hasattr(self.optimizer       ,'''overflow'''  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=None  )			->		str:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





    def 							a__				(				self  )			->		Optional[int]:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





    @property
    def 							a__				(				self  )			->		Tuple:
         if self.__has_overflow__:
              return self.optimizer.overflow
         return False


class   __a(					UpperCamelCase__       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         super().__init__(__lowerCamelCase       ,__lowerCamelCase  )





    def 							a__				(				self  )			->		Dict:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=0.0_01       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[Any]       =      params
         UpperCAmelCase_      :							Tuple       =      lr
         UpperCAmelCase_      :							Optional[Any]       =      weight_decay
         UpperCAmelCase_      :							str       =      kwargs


class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[Any]       =      optimizer
         UpperCAmelCase_      :							Optional[int]       =      total_num_steps
         UpperCAmelCase_      :							Tuple       =      warmup_num_steps
         UpperCAmelCase_      :							Dict       =      kwargs





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)