import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  json.load(open(a__  ,			"r"      )      )
  _UpperCamelCase				=  GPTNeoConfig(
      hidden_size=config_json["n_embd"]  ,			num_layers=config_json["n_layer"]  ,			num_heads=config_json["n_head"]  ,			attention_types=config_json["attention_types"]  ,			max_position_embeddings=config_json["n_positions"]  ,			resid_dropout=config_json["res_dropout"]  ,			embed_dropout=config_json["embed_dropout"]  ,			attention_dropout=config_json["attn_dropout"]  ,			)
  print(f'Building PyTorch model from configuration: {config}'      )
  _UpperCamelCase				=  GPTNeoForCausalLM(a__      )

  # Load weights from tf checkpoint
  load_tf_weights_in_gpt_neo(a__  ,			a__  ,			a__      )

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'      )
  model.save_pretrained(a__      )


if __name__ == "__main__":
   lowerCamelCase__   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   lowerCamelCase__   = parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





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()
lowerCamelCase__   = logging.get_logger()




@dataclass
class  _UpperCAmelCase       :



 '''simple docstring'''




 __A           = 42
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Tensor				,      lowercase_							:					Tensor				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  len(list(m.modules())) == 1 or isinstance(lowercase_				,      nn.Convad) or isinstance(lowercase_				,      nn.BatchNormad)
   if has_not_submodules:
     self.traced.append(lowercase_)
     _UpperCamelCase				=  m







 def __call__(						self							:					Optional[Any]				,      lowercase_							:					Tensor)		->     List[str]:


   """simple docstring"""







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







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}




class  _UpperCAmelCase       (			nn.Module   ):



 '''simple docstring'''




 def __init__(						self							:					Tuple				,      lowercase_							:					nn.Module)		->     List[str]:


   """simple docstring"""







   super().__init__()

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

   _UpperCamelCase				=  nn.ModuleDict(lowercase_)







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Tensor)		->     int:


   """simple docstring"""







   return get_trunk_forward_outputs(
       lowercase_				,      out_feat_keys=lowercase_				,      feature_blocks=self._feature_blocks				,      )




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """simple docstring"""







   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 							lowerCAmelCase__    (       a__      )	->Dict[str, str]:




  '''simple docstring'''

  _UpperCamelCase				=  RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8      )
  if "in1k" in model_name:
    _UpperCamelCase				=  RegNetForImageClassification(a__      )
  else:
    _UpperCamelCase				=  RegNetModel(a__      )
  # create from model (with small weights)
  _UpperCamelCase				=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			w_a=620.83  ,			w_m=2.52      )      )      )

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

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

    pprint(dest_tracker.nameamodule      )
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  src_tracker(a__      ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(a__      ):
    _UpperCamelCase				=  OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        _UpperCamelCase				=  param
    return params_dict

  _UpperCamelCase				=  {}

  _UpperCamelCase				=  to_params_dict(a__      )
  _UpperCamelCase				=  to_params_dict(a__      )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,			dst_state_dict.items()      ):
    _UpperCamelCase				=  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:
    _UpperCamelCase				=  "classifier.1.weight"
    _UpperCamelCase				=  "classifier.1.bias"

  return from_to_ours_keys


def 							lowerCAmelCase__    (       a__  ,			a__ = None  ,			a__ = True      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  "imagenet-1k-id2label.json"
  _UpperCamelCase				=  1_000

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

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

  _UpperCamelCase				=  partial(a__  ,			num_labels=a__  ,			idalabel=a__  ,			labelaid=a__      )

  _UpperCamelCase				=  {
      "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-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
  }

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

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

  _UpperCamelCase				=  get_from_to_our_keys(a__      )

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

    _UpperCamelCase				=  {}

    _UpperCamelCase				=  list(from_state_dict.keys()      )
    _UpperCamelCase				=  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
      _UpperCamelCase				=  re.sub(a__  ,			""  ,			a__      )
      # 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
      _UpperCamelCase				=  from_to_ours_keys[src_key]
      # store the parameter with our key
      _UpperCamelCase				=  from_state_dict[key]
      not_used_keys.remove(a__      )
    # check that all keys have been updated
    assert len(a__      ) == 0, f'Some keys where not used {",".join(a__      )}'

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

    # save our state dict to disk
    torch.save(a__  ,			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
    _UpperCamelCase				=  names_to_config[model_name]
    _UpperCamelCase				=  RegNetModel
    if "in1k" in model_name:
      _UpperCamelCase				=  RegNetForImageClassification
    _UpperCamelCase				=  our_model_func(a__      )
    # 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
    _UpperCamelCase				=  our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        a__  ,			a__  ,			[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  ,			)
    _UpperCamelCase				=  384
    # we can use the convnext one
    _UpperCamelCase				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,			size=a__      )
    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__":
   lowerCamelCase__   = 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__   = parser.parse_args()

   lowerCamelCase__   = 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)




