






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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__a:     Union[str, Any]   					=					logging.get_logger()
@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  field(default_factory=__lowercase     )
  _lowerCamelCase    =  field(default_factory=__lowercase     )


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    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		:			Optional[Any]       ,				lowerCamelCase		:			int							)						->	Optional[int]:


         """simple docstring"""




         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  lowerCamelCase       (      self		:			List[str]							)						->	int:


         """simple docstring"""
         return list(filter(lambda lowerCamelCase							: len(list(x.state_dict().keys()							)							) > 0       ,				self.traced							)							)



@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  0
  _lowerCamelCase    =  field(default_factory=__lowercase     )
  _lowerCamelCase    =  field(default_factory=__lowercase     )


  def __call__(      self		:			Any       ,				lowerCamelCase		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    Tracker(self.dest							)(_lowerCAmelCase							).parametrized
         _UpperCAmelCase							=    Tracker(self.src							)(_lowerCAmelCase							).parametrized

         _UpperCAmelCase							=    list(filter(lambda lowerCamelCase							: type(_lowerCAmelCase							) not in self.src_skip       ,				_lowerCAmelCase							)							)
         _UpperCAmelCase							=    list(filter(lambda lowerCamelCase							: type(_lowerCAmelCase							) not in self.dest_skip       ,				_lowerCAmelCase							)							)

         if len(_lowerCAmelCase							) != len(_lowerCAmelCase							):
                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}"""							)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = True )				->						Any:
       print(f"""Converting {name}...""" )
       with torch.no_grad():
              _UpperCAmelCase							=    timm.create_model(UpperCamelCase__   ,      pretrained=UpperCamelCase__ ).eval()
              _UpperCAmelCase							=    ResNetForImageClassification(UpperCamelCase__ ).eval()
              _UpperCAmelCase							=    ModuleTransfer(src=UpperCamelCase__   ,      dest=UpperCamelCase__ )
              _UpperCAmelCase							=    torch.randn((1, 3, 2_2_4, 2_2_4) )
              module_transfer(UpperCamelCase__ )

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

       _UpperCAmelCase							=    f"""resnet{"-".join(name.split("resnet" ) )}"""
       print(UpperCamelCase__ )

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name   ,      commit_message="""Add model"""   ,      use_temp_dir=UpperCamelCase__   ,      )

              # we can use the convnext one
              _UpperCAmelCase							=    AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name   ,      commit_message="""Add image processor"""   ,      use_temp_dir=UpperCamelCase__   ,      )

              print(f"""Pushed {checkpoint_name}""" )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = None   ,      __snake_case = True )				->						Tuple:
       _UpperCAmelCase							=    '''imagenet-1k-id2label.json'''
       _UpperCAmelCase							=    1_0_0_0
       _UpperCAmelCase							=    (1, num_labels)

       _UpperCAmelCase							=    '''huggingface/label-files'''
       _UpperCAmelCase							=    num_labels
       _UpperCAmelCase							=    json.load(open(hf_hub_download(UpperCamelCase__   ,      UpperCamelCase__   ,      repo_type="""dataset""" )   ,      """r""" ) )
       _UpperCAmelCase							=    {int(UpperCamelCase__ ): v for k, v in idalabel.items()}

       _UpperCAmelCase							=    idalabel
       _UpperCAmelCase							=    {v: k for k, v in idalabel.items()}

       _UpperCAmelCase							=    partial(UpperCamelCase__   ,      num_labels=UpperCamelCase__   ,      idalabel=UpperCamelCase__   ,      labelaid=UpperCamelCase__ )

       _UpperCAmelCase							=    {
           '''resnet18''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]   ,      hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]   ,      layer_type="""basic""" ),
           '''resnet26''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]   ,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,      layer_type="""bottleneck""" ),
           '''resnet34''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]   ,      hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]   ,      layer_type="""basic""" ),
           '''resnet50''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]   ,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,      layer_type="""bottleneck""" ),
           '''resnet101''': ImageNetPreTrainedConfig(
               depths=[3, 4, 2_3, 3]   ,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,      layer_type="""bottleneck""" ),
           '''resnet152''': ImageNetPreTrainedConfig(
               depths=[3, 8, 3_6, 3]   ,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,      layer_type="""bottleneck""" ),
       }

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


if __name__ == "__main__":
  __a:     Optional[int]   					=					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 resnet* architecture,'''
          ''' currently: resnet18,26,34,50,101,152. 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.''',
  )

  __a:     Optional[int]   					=					parser.parse_args()
  __a:     Path   					=					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 baseaa
import io
import json
import os
from copy import deepcopy

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





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										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
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   deepcopy(_lowerCAmelCase					)
										elif os.path.exists(_lowerCAmelCase					):
																with io.open(_lowerCAmelCase			, '''r'''			, encoding='''utf-8'''					) as f:
																						__SCREAMING_SNAKE_CASE:				int           =   json.load(_lowerCAmelCase					)
										else:
																try:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   baseaa.urlsafe_baadecode(_lowerCAmelCase					).decode('''utf-8'''					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   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}"""					)

										__SCREAMING_SNAKE_CASE:				str           =   config

										self.set_stage_and_offload()






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										# offload
										__SCREAMING_SNAKE_CASE:				Dict           =   False
										if self.is_zeroa() or self.is_zeroa():
																__SCREAMING_SNAKE_CASE:				str           =   set(['''cpu''', '''nvme''']					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   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:
																						__SCREAMING_SNAKE_CASE:				Dict           =   True






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.config

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

										return config, ds_key






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.find_config_node(_lowerCAmelCase					)
										if config is None:
																return default
										return config.get(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				str           =   ds_key_long.split('''.'''					)
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				Tuple           =   config
																__SCREAMING_SNAKE_CASE:				List[str]           =   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 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else bool(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else not bool(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 2






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._offload





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   engine






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										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			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, device_placement=_lowerCAmelCase			, scaler=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   hasattr(self.optimizer			, '''overflow'''					)






				def 			snake_case_    (  self			, _lowerCAmelCase=None					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




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





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




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





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=0.001			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   params
										__SCREAMING_SNAKE_CASE:				int           =   lr
										__SCREAMING_SNAKE_CASE:				List[str]           =   weight_decay
										__SCREAMING_SNAKE_CASE:				List[Any]           =   kwargs





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   optimizer
										__SCREAMING_SNAKE_CASE:				int           =   total_num_steps
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   warmup_num_steps
										__SCREAMING_SNAKE_CASE:				str           =   kwargs



