

'''simple docstring'''
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()
_UpperCAmelCase  :		int  =		logging.get_logger()

@dataclass
class 						__magic_name__   :
 UpperCamelCase__     =       42
 UpperCamelCase__     =       field(default_factory=__SCREAMING_SNAKE_CASE )
 UpperCamelCase__     =       field(default_factory=__SCREAMING_SNAKE_CASE )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=len(list(m.modules()   )   ) == 1 or isinstance(snake_case_  , nn.Convad   ) or isinstance(snake_case_  , nn.BatchNormad   )
   if has_not_submodules:
     self.traced.append(snake_case_   )







 def __call__(       self  , snake_case_   ):






   for m in self.module.modules():
     self.handles.append(m.register_forward_hook(self._forward_hook   )   )
   self.module(snake_case_   )
   [x.remove() for x in self.handles]
   return self







 @property
 def 						_A(       self   ):
   return list(filter(lambda snake_case_   : len(list(x.state_dict().keys()   )   ) > 0  , self.traced   )   )

@dataclass
class 						__magic_name__   :
 UpperCamelCase__     =       42
 UpperCamelCase__     =       42
 UpperCamelCase__     =       1
 UpperCamelCase__     =       field(default_factory=__SCREAMING_SNAKE_CASE )
 UpperCamelCase__     =       field(default_factory=__SCREAMING_SNAKE_CASE )
 UpperCamelCase__     =       True







 def __call__(       self  , snake_case_   ):
   lowercase									=Tracker(self.dest   )(snake_case_   ).parametrized
   lowercase									=Tracker(self.src   )(snake_case_   ).parametrized

   lowercase									=list(filter(lambda snake_case_   : type(snake_case_   ) not in self.src_skip  , snake_case_   )   )
   lowercase									=list(filter(lambda snake_case_   : type(snake_case_   ) not in self.dest_skip  , snake_case_   )   )

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

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

class 						__magic_name__   (							nn.Module ):







 def __init__(       self  , snake_case_   ):
   super().__init__()

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

   lowercase									=nn.ModuleDict(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   return get_trunk_forward_outputs(
       snake_case_  , out_feat_keys=snake_case_  , feature_blocks=self._feature_blocks  , )

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def 						_A(       self  , snake_case_   ):
   lowercase									=x.split('''-'''   )
   return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]   )







 def __getitem__(       self  , snake_case_   ):






   if x not in self:
     lowercase									=self.convert_name_to_timm(snake_case_   )
     lowercase									=partial(lambda: (timm.create_model(snake_case_  , pretrained=snake_case_   ).eval(), None)   )

   else:
     lowercase									=super().__getitem__(snake_case_   )

   return val

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __getitem__(       self  , snake_case_   ):






   if "seer" in x and "in1k" not in x:
     lowercase									=RegNetModel
   else:
     lowercase									=RegNetForImageClassification
   return val




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    str     ,					lowercase_						:    List[Tuple[str, str]]	)		->	Union[str, Any]:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    Callable[[], nn.Module]     ,					lowercase_						:    Callable[[], nn.Module]     ,					lowercase_						:    RegNetConfig     ,					lowercase_						:    Path     ,					lowercase_						:    bool = True     ,					)		->	int:




  '''simple docstring'''



  print(f'Converting {name}...'	)
  with torch.no_grad():
    lowercase							, lowercase									=from_model_func()
    lowercase									=our_model_func(A__	).eval()
    lowercase									=ModuleTransfer(src=A__     ,					dest=A__     ,					raise_if_mismatch=A__	)
    lowercase									=torch.randn((1, 3, 2_2_4, 2_2_4)	)
    module_transfer(A__	)

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

  lowercase									=our_model(A__     ,					output_hidden_states=A__	)
  lowercase									=(
      our_outputs.logits if isinstance(A__     ,					A__	) else our_outputs.last_hidden_state
  )

  lowercase									=from_model(A__	)
  lowercase									=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:
    lowercase									=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__     ,					)

    lowercase									=2_2_4 if '''seer''' not in name else 3_8_4
    # we can use the convnext one
    lowercase									=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 	UpperCamelCase   (       lowercase_						:    Path     ,					lowercase_						:    str = None     ,					lowercase_						:    bool = True	)		->	List[Any]:




  '''simple docstring'''



  lowercase									='''imagenet-1k-id2label.json'''
  lowercase									=1_0_0_0
  lowercase									=(1, num_labels)

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

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

  lowercase									=partial(A__     ,					num_labels=A__     ,					idalabel=A__     ,					labelaid=A__	)

  lowercase									={
      '''regnet-x-002''': ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]     ,					hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]     ,					groups_width=8     ,					layer_type='''x'''	),
      '''regnet-x-004''': ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 1_2]     ,					hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]     ,					groups_width=1_6     ,					layer_type='''x'''	),
      '''regnet-x-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]     ,					hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]     ,					groups_width=2_4     ,					layer_type='''x'''	),
      '''regnet-x-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]     ,					hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]     ,					groups_width=1_6     ,					layer_type='''x'''	),
      '''regnet-x-016''': ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 2]     ,					hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]     ,					groups_width=2_4     ,					layer_type='''x'''	),
      '''regnet-x-032''': ImageNetPreTrainedConfig(
          depths=[2, 6, 1_5, 2]     ,					hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]     ,					groups_width=4_8     ,					layer_type='''x'''	),
      '''regnet-x-040''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_4, 2]     ,					hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]     ,					groups_width=4_0     ,					layer_type='''x'''	),
      '''regnet-x-064''': ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 1]     ,					hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]     ,					groups_width=5_6     ,					layer_type='''x'''	),
      '''regnet-x-080''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_5, 1]     ,					hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]     ,					groups_width=1_2_0     ,					layer_type='''x'''	),
      '''regnet-x-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_1, 1]     ,					hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]     ,					groups_width=1_1_2     ,					layer_type='''x'''	),
      '''regnet-x-160''': ImageNetPreTrainedConfig(
          depths=[2, 6, 1_3, 1]     ,					hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]     ,					groups_width=1_2_8     ,					layer_type='''x'''	),
      '''regnet-x-320''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_3, 1]     ,					hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]     ,					groups_width=1_6_8     ,					layer_type='''x'''	),
      # y variant
      '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]     ,					hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]     ,					groups_width=8	),
      '''regnet-y-004''': ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]     ,					hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]     ,					groups_width=8	),
      '''regnet-y-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]     ,					hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]     ,					groups_width=1_6	),
      '''regnet-y-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]     ,					hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]     ,					groups_width=1_6	),
      '''regnet-y-016''': ImageNetPreTrainedConfig(
          depths=[2, 6, 1_7, 2]     ,					hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]     ,					groups_width=2_4	),
      '''regnet-y-032''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_3, 1]     ,					hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]     ,					groups_width=2_4	),
      '''regnet-y-040''': ImageNetPreTrainedConfig(
          depths=[2, 6, 1_2, 2]     ,					hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]     ,					groups_width=6_4	),
      '''regnet-y-064''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_4, 2]     ,					hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]     ,					groups_width=7_2	),
      '''regnet-y-080''': ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 1]     ,					hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]     ,					groups_width=5_6	),
      '''regnet-y-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_1, 1]     ,					hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]     ,					groups_width=1_1_2	),
      '''regnet-y-160''': ImageNetPreTrainedConfig(
          depths=[2, 4, 1_1, 1]     ,					hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]     ,					groups_width=1_1_2	),
      '''regnet-y-320''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1]     ,					hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]     ,					groups_width=2_3_2	),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 1_2, 1]     ,					hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]     ,					groups_width=2_3_2	),
      '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 1_2, 1]     ,					hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]     ,					groups_width=3_2_8	),
      '''regnet-y-1280-seer''': RegNetConfig(
          depths=[2, 7, 1_7, 1]     ,					hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]     ,					groups_width=2_6_4	),
      '''regnet-y-2560-seer''': RegNetConfig(
          depths=[3, 7, 1_6, 1]     ,					hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]     ,					groups_width=6_4_0	),
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]     ,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]     ,					groups_width=1_0_1_0	),
      # finetuned on imagenet
      '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1]     ,					hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]     ,					groups_width=2_3_2	),
      '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1]     ,					hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]     ,					groups_width=3_2_8	),
      '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]     ,					hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]     ,					groups_width=2_6_4	),
      '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[3, 7, 1_6, 1]     ,					hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]     ,					groups_width=6_4_0	),
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]     ,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]     ,					groups_width=1_0_1_0	),
  }

  lowercase									=NameToOurModelFuncMap()
  lowercase									=NameToFromModelFuncMap()
  # add seer weights logic

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

  # pretrained
  lowercase									=partial(
      A__     ,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''     ,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)     ,					)

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

  lowercase									=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()	)     ,					)

  lowercase									=partial(
      A__     ,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''     ,					lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=2_7     ,					group_width=1_0_1_0     ,					w_a=1_7_4_4     ,					w_a=6_2_0.8_3     ,					w_m=2.5_2	)	)	)     ,					)

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

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

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

  lowercase									=partial(
      A__     ,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''     ,					lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=2_7     ,					group_width=1_0_1_0     ,					w_a=1_7_4_4     ,					w_a=6_2_0.8_3     ,					w_m=2.5_2	)	)	)     ,					)

  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__":
     _UpperCAmelCase  :		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.''',
     )

     _UpperCAmelCase  :		int  =		parser.parse_args()

     _UpperCAmelCase  :		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)



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


