


'''simple docstring'''






import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
a_       :     Optional[Any] 							=     logging.get_logger()




@dataclass
class 			a  :
 _lowerCAmelCase			       =						4_2
 _lowerCAmelCase			       =						field(default_factory=__A							)
 _lowerCAmelCase			       =						field(default_factory=__A							)
 _lowerCAmelCase			       =						field(default_factory=__A							)
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				str:
      _a         =				len(list(m.modules()    )    ) == 1 or isinstance(A_   ,					nn.Convad    ) or isinstance(A_   ,					nn.BatchNormad    )
      if has_not_submodules:
           self.traced.append(A_    )
           _a         =				m
 def __call__(			self   ,					__magic_name__    )       ->				Any:



      for name, m in self.module.named_modules():
           self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,					name=A_    )    )    )
      self.module(A_    )
      [x.remove() for x in self.handles]
      return self




 @property
 def 				__UpperCAmelCase		(			self    )       ->				List[str]:
      return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()    )    ) > 0}





class 			a  (  nn.Module							):
 def __init__(			self   ,					__magic_name__    )       ->				Union[str, Any]:
      super().__init__()

      _a         =				[]
      # - 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}'
           _a         =				len(A_    ) + 1
           feature_blocks.append((f'res{block_index}', v)    )

      _a         =				nn.ModuleDict(A_    )




 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				str:
      return get_trunk_forward_outputs(
          A_   ,					out_feat_keys=A_   ,					feature_blocks=self._feature_blocks   ,					)





class 			a  (  __A							):
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]






def 						_A					(lowerCAmelCase__	:List[Any]		) ->	int:




     '''simple docstring'''



     _a         =				RegNetConfig(depths=[2, 7, 17, 1]					,    hidden_sizes=[8, 8, 8, 8]					,    groups_width=8		)
     if "in1k" in model_name:
          _a         =				RegNetForImageClassification(UpperCamelCase__		)
     else:
          _a         =				RegNetModel(UpperCamelCase__		)
     # create from model (with small weights)
     _a         =				FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27					,    group_width=10_10					,    w_a=17_44					,    w_a=6_20.83					,    w_m=2.5_2		)		)		)

     with torch.no_grad():
          _a         =				from_model.eval()
          _a         =				our_model.eval()

          _a         =				torch.randn((1, 3, 32, 32)		)
          # trace both
          _a         =				Tracker(UpperCamelCase__		)
          _a         =				dest_tracker(UpperCamelCase__		).parametrized

          pprint(dest_tracker.nameamodule		)
          _a         =				Tracker(UpperCamelCase__		)
          _a         =				src_tracker(UpperCamelCase__		).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(lowerCAmelCase__	:Optional[Any]		):
          _a         =				OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    _a         =				param
          return params_dict

     _a         =				{}

     _a         =				to_params_dict(UpperCamelCase__		)
     _a         =				to_params_dict(UpperCamelCase__		)

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,    dst_state_dict.items()		):
          _a         =				dest_key
          logger.info(f'{src_key} -> {dest_key}'		)
     # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
     if "in1k" in model_name:
          _a         =				'classifier.1.weight'
          _a         =				'classifier.1.bias'

     return from_to_ours_keys






def 						_A					(lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:Dict = None					,    lowerCAmelCase__	:Union[str, Any] = True		) ->	Union[str, Any]:




     '''simple docstring'''



     _a         =				'imagenet-1k-id2label.json'
     _a         =				10_00

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

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

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

     _a         =				{
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]					,    groups_width=10_10		),
         # finetuned on imagenet
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]					,    groups_width=10_10		),
     }

     # add seer weights logic
     def load_using_classy_vision(lowerCAmelCase__	:List[Any]		) -> Tuple[Dict, Dict]:
          _a         =				torch.hub.load_state_dict_from_url(UpperCamelCase__					,    model_dir=str(UpperCamelCase__		)					,    map_location='cpu'		)
          # check if we have a head, if yes add it
          _a         =				files['classy_state_dict']['base_model']['model']
          return model_state_dict["trunk"], model_state_dict["heads"]

     _a         =				{
         'regnet-y-10b-seer': partial(
             UpperCamelCase__					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'					,    ),
         'regnet-y-10b-seer-in1k': partial(
             UpperCamelCase__					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'					,    ),
     }

     _a         =				get_from_to_our_keys(UpperCamelCase__		)

     if not (save_directory / f'{model_name}.pth').exists():
          logger.info('Loading original state_dict.'		)
          _a   , _a         =				names_to_from_model[model_name]()
          _a         =				from_state_dict_trunk
          if "in1k" in model_name:
               # add the head
               _a         =				{**from_state_dict_trunk, **from_state_dict_head}
          logger.info('Done!'		)

          _a         =				{}

          _a         =				list(from_state_dict.keys()		)
          _a         =				r'\.block.-part.'
          # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
          for key in from_state_dict.keys():
               # remove the weird "block[0,1]-part" from the key
               _a         =				re.sub(UpperCamelCase__					,    ''					,    UpperCamelCase__		)
               # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
               _a         =				from_to_ours_keys[src_key]
               # store the parameter with our key
               _a         =				from_state_dict[key]
               not_used_keys.remove(UpperCamelCase__		)
          # check that all keys have been updated
          assert len(UpperCamelCase__		) == 0, f'Some keys where not used {",".join(UpperCamelCase__		)}'

          logger.info(f'The following keys were not used: {",".join(UpperCamelCase__		)}'		)

          # save our state dict to disk
          torch.save(UpperCamelCase__					,    save_directory / f'{model_name}.pth'		)

          del converted_state_dict
     else:
          logger.info('The state_dict was already stored on disk.'		)
     if push_to_hub:
          logger.info(f'Token is {os.environ["HF_TOKEN"]}'		)
          logger.info('Loading our model.'		)
          # create our model
          _a         =				names_to_config[model_name]
          _a         =				RegNetModel
          if "in1k" in model_name:
               _a         =				RegNetForImageClassification
          _a         =				our_model_func(UpperCamelCase__		)
          # place our model to the meta device (so remove all the weights)
          our_model.to(torch.device('meta'		)		)
          logger.info('Loading state_dict in our model.'		)
          # load state dict
          _a         =				our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              UpperCamelCase__					,    UpperCamelCase__					,    [save_directory / f'{model_name}.pth']		)
          logger.info('Finally, pushing!'		)
          # push it to hub
          our_model.push_to_hub(
              repo_path_or_name=save_directory / model_name					,    commit_message='Add model'					,    output_dir=save_directory / model_name					,    )
          _a         =				3_84
          # we can use the convnext one
          _a         =				AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					,    size=UpperCamelCase__		)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / model_name					,    commit_message='Add image processor'					,    output_dir=save_directory / model_name					,    )


if __name__ == "__main__":
 a_       :     Tuple 							=     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.",
 )

 a_       :     str 							=     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)






"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
