




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase    :       Any        =							logging.get_logger()





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					42
   UpperCamelCase       					=					field(default_factory=__snake_case						)
   UpperCamelCase       					=					field(default_factory=__snake_case						)



   def 	__magic_name__   (							self							:					str,		__A							:					List[str],		__A							:					Tensor,		__A							:					Tensor						):
     UpperCAmelCase	:							Dict						=			len(list(m.modules()						)						) == 1 or isinstance(lowerCamelCase_,		nn.Convad						) or isinstance(lowerCamelCase_,		nn.BatchNormad						)
     if has_not_submodules:
       self.traced.append(lowerCamelCase_						)



   def __call__(							self							:					Tuple,		__A							:					Tensor						):
     for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook						)						)
     self.module(lowerCamelCase_						)
     [x.remove() for x in self.handles]
     return self





   @property
   def 	__magic_name__   (							self							:					List[str]						):
     # check the len of the state_dict keys to see if we have learnable params
     return list(filter(lambda __A						: len(list(x.state_dict().keys()						)						) > 0,		self.traced						)						)






@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					42
   UpperCamelCase       					=					42
   UpperCamelCase       					=					1
   UpperCamelCase       					=					field(default_factory=__snake_case						)
   UpperCamelCase       					=					field(default_factory=__snake_case						)
   UpperCamelCase       					=					True



   def __call__(							self							:					Tuple,		__A							:					Tensor						):
     UpperCAmelCase	:							List[str]						=			Tracker(self.dest						)(lowerCamelCase_						).parametrized
     UpperCAmelCase	:							Any						=			Tracker(self.src						)(lowerCamelCase_						).parametrized

     UpperCAmelCase	:							int						=			list(filter(lambda __A						: type(lowerCamelCase_						) not in self.src_skip,		lowerCamelCase_						)						)
     UpperCAmelCase	:							Optional[int]						=			list(filter(lambda __A						: type(lowerCamelCase_						) not in self.dest_skip,		lowerCamelCase_						)						)

     if len(lowerCamelCase_						) != len(lowerCamelCase_						) and self.raise_if_mismatch:
       raise Exception(
           F'''Numbers of operations are different. Source module has {len(lowerCamelCase_						)} operations while'''
           F''' destination module has {len(lowerCamelCase_						)}.'''						)

     for dest_m, src_m in zip(lowerCamelCase_,		lowerCamelCase_						):
       dest_m.load_state_dict(src_m.state_dict()						)
       if self.verbose == 1:
         print(F'''Transfered from={src_m} to={dest_m}'''						)






class  __UpperCAmelCase					(	nn.Module						):



   def __init__(							self							:					List[str],		__A							:					nn.Module						):
     super().__init__()

     UpperCAmelCase	:							List[Tuple[str, nn.Module]]						=			[]
     # - get the stem
     feature_blocks.append(('''conv1''', model.stem)						)
     # - get all the feature blocks
     for k, v in model.trunk_output.named_children():
       assert k.startswith('''block'''						), F'''Unexpected layer name {k}'''
       UpperCAmelCase	:							Tuple						=			len(lowerCamelCase_						) + 1
       feature_blocks.append((F'''res{block_index}''', v)						)

     UpperCAmelCase	:							Optional[int]						=			nn.ModuleDict(lowerCamelCase_						)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Tensor						):
     return get_trunk_forward_outputs(
         lowerCamelCase_,		out_feat_keys=lowerCamelCase_,		feature_blocks=self._feature_blocks,		)






class  __UpperCAmelCase					(	__snake_case						):



   def 	__magic_name__   (							self							:					Dict,		__A							:					str						):
     UpperCAmelCase	:							str						=			x.split('''-'''						)
     return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)





   def __getitem__(							self							:					Union[str, Any],		__A							:					str						):
     # default to timm!
     if x not in self:
       UpperCAmelCase	:							str						=			self.convert_name_to_timm(lowerCamelCase_						)
       UpperCAmelCase	:							List[str]						=			partial(lambda: (timm.create_model(lowerCamelCase_,		pretrained=lowerCamelCase_						).eval(), None)						)

     else:
       UpperCAmelCase	:							Optional[int]						=			super().__getitem__(lowerCamelCase_						)

     return val






class  __UpperCAmelCase					(	__snake_case						):



   def __getitem__(							self							:					Union[str, Any],		__A							:					str						):
     if "seer" in x and "in1k" not in x:
       UpperCAmelCase	:							Tuple						=			RegNetModel
     else:
       UpperCAmelCase	:							Union[str, Any]						=			RegNetForImageClassification
     return val






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[Tuple[str, str]]      )  ->						str:




  for from_key, to_key in keys:
    UpperCAmelCase	:							int						=			from_state_dict[from_key].clone()
    print(f'''Copied key={from_key} to={to_key}'''      )
  return to_state_dict

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Callable[[], nn.Module]    ,			UpperCAmelCase							:      Callable[[], nn.Module]    ,			UpperCAmelCase							:      RegNetConfig    ,			UpperCAmelCase							:      Path    ,			UpperCAmelCase							:      bool = True    ,			)  ->						Optional[Any]:
  print(f'''Converting {name}...'''      )
  with torch.no_grad():
    UpperCAmelCase	:							Any						=			from_model_func()
    UpperCAmelCase	:							str						=			our_model_func(_a      ).eval()
    UpperCAmelCase	:							List[Any]						=			ModuleTransfer(src=_a    ,			dest=_a    ,			raise_if_mismatch=_a      )
    UpperCAmelCase	:							List[str]						=			torch.randn((1, 3, 224, 224)      )
    module_transfer(_a      )

  if from_state_dict is not None:
    UpperCAmelCase	:							List[str]						=			[]
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      UpperCAmelCase	:							List[Any]						=			[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
    UpperCAmelCase	:							str						=			manually_copy_vissl_head(_a    ,			our_model.state_dict()    ,			_a      )
    our_model.load_state_dict(_a      )

  UpperCAmelCase	:							Union[str, Any]						=			our_model(_a    ,			output_hidden_states=_a      )
  UpperCAmelCase	:							int						=			(
      our_outputs.logits if isinstance(_a    ,			_a      ) else our_outputs.last_hidden_state
  )

  UpperCAmelCase	:							Optional[int]						=			from_model(_a      )
  UpperCAmelCase	:							List[Any]						=			from_output[-1] if type(_a      ) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    UpperCAmelCase	:							Union[str, Any]						=			our_outputs.hidden_states[-1]

  assert torch.allclose(_a    ,			_a      ), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name    ,			commit_message='''Add model'''    ,			use_temp_dir=_a    ,			)

    UpperCAmelCase	:							Union[str, Any]						=			224 if """seer""" not in name else 384
    # we can use the convnext one
    UpperCAmelCase	:							Optional[int]						=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    ,			size=_a      )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name    ,			commit_message='''Add image processor'''    ,			use_temp_dir=_a    ,			)

    print(f'''Pushed {name}'''      )

def       a__					(		UpperCAmelCase							:      Path    ,			UpperCAmelCase							:      str = None    ,			UpperCAmelCase							:      bool = True      )  ->						Any:
  UpperCAmelCase	:							Optional[int]						=			"""imagenet-1k-id2label.json"""
  UpperCAmelCase	:							List[Any]						=			1_000
  UpperCAmelCase	:							Any						=			(1, num_labels)

  UpperCAmelCase	:							Tuple						=			"""huggingface/label-files"""
  UpperCAmelCase	:							List[Any]						=			num_labels
  UpperCAmelCase	:							List[str]						=			json.load(open(cached_download(hf_hub_url(_a    ,			_a    ,			repo_type='''dataset'''      )      )    ,			'''r'''      )      )
  UpperCAmelCase	:							Union[str, Any]						=			{int(_a      ): v for k, v in idalabel.items()}

  UpperCAmelCase	:							Tuple						=			idalabel
  UpperCAmelCase	:							Any						=			{v: k for k, v in idalabel.items()}

  UpperCAmelCase	:							Union[str, Any]						=			partial(_a    ,			num_labels=_a    ,			idalabel=_a    ,			labelaid=_a      )

  UpperCAmelCase	:							List[Any]						=			{
      """regnet-x-002""": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]    ,			hidden_sizes=[24, 56, 152, 368]    ,			groups_width=8    ,			layer_type='''x'''      ),
      """regnet-x-004""": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]    ,			hidden_sizes=[32, 64, 160, 384]    ,			groups_width=16    ,			layer_type='''x'''      ),
      """regnet-x-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]    ,			hidden_sizes=[48, 96, 240, 528]    ,			groups_width=24    ,			layer_type='''x'''      ),
      """regnet-x-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]    ,			hidden_sizes=[64, 128, 288, 672]    ,			groups_width=16    ,			layer_type='''x'''      ),
      """regnet-x-016""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]    ,			hidden_sizes=[72, 168, 408, 912]    ,			groups_width=24    ,			layer_type='''x'''      ),
      """regnet-x-032""": ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]    ,			hidden_sizes=[96, 192, 432, 1_008]    ,			groups_width=48    ,			layer_type='''x'''      ),
      """regnet-x-040""": ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]    ,			hidden_sizes=[80, 240, 560, 1_360]    ,			groups_width=40    ,			layer_type='''x'''      ),
      """regnet-x-064""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]    ,			hidden_sizes=[168, 392, 784, 1_624]    ,			groups_width=56    ,			layer_type='''x'''      ),
      """regnet-x-080""": ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]    ,			hidden_sizes=[80, 240, 720, 1_920]    ,			groups_width=120    ,			layer_type='''x'''      ),
      """regnet-x-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]    ,			hidden_sizes=[224, 448, 896, 2_240]    ,			groups_width=112    ,			layer_type='''x'''      ),
      """regnet-x-160""": ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]    ,			hidden_sizes=[256, 512, 896, 2_048]    ,			groups_width=128    ,			layer_type='''x'''      ),
      """regnet-x-320""": ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]    ,			hidden_sizes=[336, 672, 1_344, 2_520]    ,			groups_width=168    ,			layer_type='''x'''      ),
      # y variant
      """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,			hidden_sizes=[24, 56, 152, 368]    ,			groups_width=8      ),
      """regnet-y-004""": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]    ,			hidden_sizes=[48, 104, 208, 440]    ,			groups_width=8      ),
      """regnet-y-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]    ,			hidden_sizes=[48, 112, 256, 608]    ,			groups_width=16      ),
      """regnet-y-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]    ,			hidden_sizes=[64, 128, 320, 768]    ,			groups_width=16      ),
      """regnet-y-016""": ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]    ,			hidden_sizes=[48, 120, 336, 888]    ,			groups_width=24      ),
      """regnet-y-032""": ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]    ,			hidden_sizes=[72, 216, 576, 1_512]    ,			groups_width=24      ),
      """regnet-y-040""": ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]    ,			hidden_sizes=[128, 192, 512, 1_088]    ,			groups_width=64      ),
      """regnet-y-064""": ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]    ,			hidden_sizes=[144, 288, 576, 1_296]    ,			groups_width=72      ),
      """regnet-y-080""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]    ,			hidden_sizes=[168, 448, 896, 2_016]    ,			groups_width=56      ),
      """regnet-y-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]    ,			hidden_sizes=[224, 448, 896, 2_240]    ,			groups_width=112      ),
      """regnet-y-160""": ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]    ,			hidden_sizes=[224, 448, 1_232, 3_024]    ,			groups_width=112      ),
      """regnet-y-320""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1_392, 3_712]    ,			groups_width=232      ),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1_392, 3_712]    ,			groups_width=232      ),
      """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,			hidden_sizes=[328, 984, 1_968, 4_920]    ,			groups_width=328      ),
      """regnet-y-1280-seer""": RegNetConfig(
          depths=[2, 7, 17, 1]    ,			hidden_sizes=[528, 1_056, 2_904, 7_392]    ,			groups_width=264      ),
      """regnet-y-2560-seer""": RegNetConfig(
          depths=[3, 7, 16, 1]    ,			hidden_sizes=[640, 1_696, 2_544, 5_088]    ,			groups_width=640      ),
      """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]    ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,			groups_width=1_010      ),
      # finetuned on imagenet
      """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1_392, 3_712]    ,			groups_width=232      ),
      """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]    ,			hidden_sizes=[328, 984, 1_968, 4_920]    ,			groups_width=328      ),
      """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]    ,			hidden_sizes=[528, 1_056, 2_904, 7_392]    ,			groups_width=264      ),
      """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]    ,			hidden_sizes=[640, 1_696, 2_544, 5_088]    ,			groups_width=640      ),
      """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]    ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,			groups_width=1_010      ),
  }

  UpperCAmelCase	:							List[Any]						=			NameToOurModelFuncMap()
  UpperCAmelCase	:							Union[str, Any]						=			NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Callable[[], nn.Module]      ) -> Tuple[nn.Module, Dict]:
    UpperCAmelCase	:							Optional[Any]						=			torch.hub.load_state_dict_from_url(_a    ,			model_dir=str(_a      )    ,			map_location='''cpu'''      )
    UpperCAmelCase	:							Union[str, Any]						=			model_func()
    # check if we have a head, if yes add it
    UpperCAmelCase	:							Optional[Any]						=			files["""classy_state_dict"""]["""base_model"""]["""model"""]
    UpperCAmelCase	:							Optional[Any]						=			model_state_dict["""trunk"""]
    model.load_state_dict(_a      )
    return model.eval(), model_state_dict["heads"]

  # pretrained
  UpperCAmelCase	:							List[Any]						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )    ,			)

  UpperCAmelCase	:							str						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )    ,			)

  UpperCAmelCase	:							Tuple						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )    ,			)

  UpperCAmelCase	:							Optional[int]						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''    ,			lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27    ,			group_width=1_010    ,			w_a=1_744    ,			w_a=620.83    ,			w_m=2.52      )      )      )    ,			)

  # IN1K finetuned
  UpperCAmelCase	:							Dict						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )    ,			)

  UpperCAmelCase	:							Dict						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )    ,			)

  UpperCAmelCase	:							Any						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''    ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )    ,			)

  UpperCAmelCase	:							List[Any]						=			partial(
      _a    ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''    ,			lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27    ,			group_width=1_010    ,			w_a=1_744    ,			w_a=620.83    ,			w_m=2.52      )      )      )    ,			)

  if model_name:
    convert_weight_and_push(
        _a    ,			names_to_from_model_map[model_name]    ,			names_to_ours_model_map[model_name]    ,			names_to_config[model_name]    ,			_a    ,			_a    ,			)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          _a    ,			names_to_from_model_map[model_name]    ,			names_to_ours_model_map[model_name]    ,			_a    ,			_a    ,			_a    ,			)
  return config, expected_shape


if __name__ == "__main__":
     _lowerCamelCase    :       List[Any]        =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default=None,
         type=str,
         help=(
             "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
             " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
         ),
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=Path,
         required=True,
         help="Path to the output PyTorch model directory.",
     )
     parser.add_argument(
         "--push_to_hub",
         default=True,
         type=bool,
         required=False,
         help="If True, push model and image processor to the hub.",
     )

     _lowerCamelCase    :       Optional[int]        =							parser.parse_args()

     _lowerCamelCase    :       str        =							args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





