





"""simple docstring"""





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

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




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Optional[int]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        if isinstance(__lowercase						,				__lowercase ):
          # 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    :						List[Any]       =						deepcopy(__lowercase )
        elif os.path.exists(__lowercase ):
          with io.open(__lowercase						,				'''r'''						,				encoding='''utf-8''' ) as f:
            __UpperCAmelCase    :						str       =						json.load(__lowercase )
        else:
          try:
            __UpperCAmelCase    :						int       =						baseaa.urlsafe_baadecode(__lowercase ).decode('''utf-8''' )
            __UpperCAmelCase    :						Optional[int]       =						json.loads(__lowercase )
          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    :						int       =						config

        self.set_stage_and_offload()

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self.get_value('''zero_optimization.stage'''						,				-1 )

        # offload
        __UpperCAmelCase    :						Any       =						False
        if self.is_zeroa() or self.is_zeroa():
          __UpperCAmelCase    :						Optional[Any]       =						set(['''cpu''', '''nvme'''] )
          __UpperCAmelCase    :						Optional[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    :						Tuple       =						True

      def       A_					(							self       :      List[Any]						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.config

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

        return config, ds_key

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Dict						,				__lowercase       :      List[str]=None ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						self.find_config_node(__lowercase )
        if config is None:
          return default
        return config.get(__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Optional[Any]						,				__lowercase       :      Optional[int]=False ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.config

        # find the config node of interest if it exists
        __UpperCAmelCase    :						List[str]       =						ds_key_long.split('''.''' )
        for node in nodes:
          __UpperCAmelCase    :						Dict       =						config
          __UpperCAmelCase    :						Union[str, Any]       =						config.get(__lowercase )
          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(__lowercase )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self.get_value(__lowercase )
        return False if value is None else bool(__lowercase )

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						self.get_value(__lowercase )
        return False if value is None else not bool(__lowercase )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return self._stage == 2

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        return self._stage == 3







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return self._offload




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      List[Any]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						engine







      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int]						,				**__lowercase       :      List[Any] ):


        '''simple docstring'''




        self.engine.backward(__lowercase						,				**__lowercase )

        # 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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      str ):


        '''simple docstring'''




        super().__init__(__lowercase						,				device_placement=__lowercase						,				scaler=__lowercase )
        __UpperCAmelCase    :						Dict       =						hasattr(self.optimizer						,				'''overflow''' )

      def       A_					(							self       :      List[Any]						,				__lowercase       :      str=None ):


        '''simple docstring'''




        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        if self.__has_overflow__:
          return self.optimizer.overflow
        return False




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      int						,				__lowercase       :      List[str]						,				__lowercase       :      Any ):


        '''simple docstring'''




        super().__init__(__lowercase						,				__lowercase )







      def       A_					(							self       :      str ):


        '''simple docstring'''




        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Tuple						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Union[str, Any]=0.0_0_1						,				__lowercase       :      Dict=0						,				**__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						params
        __UpperCAmelCase    :						Optional[int]       =						lr
        __UpperCAmelCase    :						List[Any]       =						weight_decay
        __UpperCAmelCase    :						Any       =						kwargs




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      List[str]						,				__lowercase       :      List[Any]						,				__lowercase       :      Optional[Any]=None						,				__lowercase       :      List[Any]=0						,				**__lowercase       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						optimizer
        __UpperCAmelCase    :						List[Any]       =						total_num_steps
        __UpperCAmelCase    :						List[str]       =						warmup_num_steps
        __UpperCAmelCase    :						Tuple       =						kwargs






"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						SwinvaConfig()
  __UpperCAmelCase    :						Any       =						swinva_name.split('''_'''  )

  __UpperCAmelCase    :						Optional[Any]       =						name_split[1]
  if "to" in name_split[3]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[3][-3:]  )
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						int(name_split[3]  )
  if "to" in name_split[2]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[2][-2:]  )
  else:
    __UpperCAmelCase    :						str       =						int(name_split[2][6:]  )

  if model_size == "tiny":
    __UpperCAmelCase    :						Union[str, Any]       =						96
    __UpperCAmelCase    :						str       =						(2, 2, 6, 2)
    __UpperCAmelCase    :						Union[str, Any]       =						(3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase    :						List[Any]       =						96
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase    :						Any       =						1_28
    __UpperCAmelCase    :						int       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						Dict       =						(4, 8, 16, 32)
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						1_92
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __UpperCAmelCase    :						Optional[int]       =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __UpperCAmelCase    :						int       =						2_18_41
    __UpperCAmelCase    :						Optional[Any]       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-22k-id2label.json'''
    __UpperCAmelCase    :						Any       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						List[Any]       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Optional[Any]       =						idalabel
    __UpperCAmelCase    :						Optional[int]       =						{v: k for k, v in idalabel.items()}

  else:
    __UpperCAmelCase    :						List[Any]       =						10_00
    __UpperCAmelCase    :						str       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Tuple       =						'''imagenet-1k-id2label.json'''
    __UpperCAmelCase    :						Optional[int]       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						str       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Any       =						idalabel
    __UpperCAmelCase    :						List[Any]       =						{v: k for k, v in idalabel.items()}

  __UpperCAmelCase    :						Optional[int]       =						img_size
  __UpperCAmelCase    :						int       =						num_classes
  __UpperCAmelCase    :						Tuple       =						embed_dim
  __UpperCAmelCase    :						str       =						depths
  __UpperCAmelCase    :						Optional[Any]       =						num_heads
  __UpperCAmelCase    :						Optional[int]       =						window_size

  return config


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  if "patch_embed.proj" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''patch_embed.proj'''  ,			'''embeddings.patch_embeddings.projection'''  )
  if "patch_embed.norm" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''patch_embed.norm'''  ,			'''embeddings.norm'''  )
  if "layers" in name:
    __UpperCAmelCase    :						Optional[int]       =						'''encoder.''' + name
  if "attn.proj" in name:
    __UpperCAmelCase    :						Optional[Any]       =						name.replace('''attn.proj'''  ,			'''attention.output.dense'''  )
  if "attn" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''attn'''  ,			'''attention.self'''  )
  if "norm1" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''norm1'''  ,			'''layernorm_before'''  )
  if "norm2" in name:
    __UpperCAmelCase    :						Dict       =						name.replace('''norm2'''  ,			'''layernorm_after'''  )
  if "mlp.fc1" in name:
    __UpperCAmelCase    :						str       =						name.replace('''mlp.fc1'''  ,			'''intermediate.dense'''  )
  if "mlp.fc2" in name:
    __UpperCAmelCase    :						int       =						name.replace('''mlp.fc2'''  ,			'''output.dense'''  )
  if "q_bias" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''q_bias'''  ,			'''query.bias'''  )
  if "k_bias" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''k_bias'''  ,			'''key.bias'''  )
  if "v_bias" in name:
    __UpperCAmelCase    :						str       =						name.replace('''v_bias'''  ,			'''value.bias'''  )
  if "cpb_mlp" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''cpb_mlp'''  ,			'''continuous_position_bias_mlp'''  )
  if name == "norm.weight":
    __UpperCAmelCase    :						int       =						'''layernorm.weight'''
  if name == "norm.bias":
    __UpperCAmelCase    :						Optional[Any]       =						'''layernorm.bias'''

  if "head" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''head'''  ,			'''classifier'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						'''swinv2.''' + name

  return name


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase    :						Any       =						orig_state_dict.pop(UpperCAmelCase_  )

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

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

  return orig_state_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

  __UpperCAmelCase    :						Any       =						get_swinva_config(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						SwinvaForImageClassification(UpperCAmelCase_  )
  model.eval()

  __UpperCAmelCase    :						int       =						convert_state_dict(timm_model.state_dict()  ,			UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

  __UpperCAmelCase    :						List[Any]       =						timm_model(inputs['''pixel_values''']  )
  __UpperCAmelCase    :						Tuple       =						model(**UpperCAmelCase_  ).logits

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

  print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(UpperCAmelCase_  )

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(UpperCAmelCase_  )

  model.push_to_hub(
      repo_path_or_name=Path(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			organization='''nandwalritik'''  ,			commit_message='''Add model'''  ,			)


if __name__ == "__main__":
  lowercase__				:List[Any]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swinv2_name',
      default='swinv2_tiny_patch4_window8_256',
      type=str,
      help='Name of the Swinv2 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.'
  )

  lowercase__				:List[Any]				   =       parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)