






"""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()
lowerCAmelCase_		:			Optional[Any]				   =					logging.get_logger()





@dataclass
class      UpperCamelCase_       :
  _A				:    Tuple =		42
  _A				:    Dict =		field(default_factory=a_     )
  _A				:    int =		field(default_factory=a_     )
  _A				:    List[Any] =		field(default_factory=a_     )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	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__ )
                UpperCAmelCase					=	m



  def __call__(					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





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



  @property
  def 						UpperCamelCase_ (					self )	->	Union[str, 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     ):



  def __init__(					self       ,						snake_case__ )	->	Union[str, Any]:



         """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(snake_case__ ) + 1
                feature_blocks.append((f'''res{block_index}''', v) )

         UpperCAmelCase					=	nn.ModuleDict(snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Any:



         """simple docstring"""





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





class      UpperCamelCase_       (				a_     ):



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """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				(					lowerCAmelCase						):







       '''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(_snake_case						)
       else:
              UpperCAmelCase					=	RegNetModel(_snake_case						)
       # create from model (with small weights)
       UpperCAmelCase					=	FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27		,					group_width=1010		,					w_a=1744		,					w_a=6_20.83		,					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(_snake_case						)
              UpperCAmelCase					=	dest_tracker(_snake_case						).parametrized

              pprint(dest_tracker.nameamodule						)
              UpperCAmelCase					=	Tracker(_snake_case						)
              UpperCAmelCase					=	src_tracker(_snake_case						).parametrized

       # convert the keys -> module dict to keys -> params
       def to_params_dict(lowerCAmelCase						):
              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(_snake_case						)
       UpperCAmelCase					=	to_params_dict(_snake_case						)

       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				(					lowerCAmelCase		,					lowerCAmelCase = None		,					lowerCAmelCase = True						):







       '''simple docstring'''
       UpperCAmelCase					=	"""imagenet-1k-id2label.json"""
       UpperCAmelCase					=	1000

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

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

       UpperCAmelCase					=	partial(_snake_case		,					num_labels=_snake_case		,					idalabel=_snake_case		,					labelaid=_snake_case						)

       UpperCAmelCase					=	{
           """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]		,					hidden_sizes=[2020, 4040, 11110, 28280]		,					groups_width=1010						),
           # finetuned on imagenet
           """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]		,					hidden_sizes=[2020, 4040, 11110, 28280]		,					groups_width=1010						),
       }

       # add seer weights logic
       def load_using_classy_vision(lowerCAmelCase						) -> Tuple[Dict, Dict]:
              UpperCAmelCase					=	torch.hub.load_state_dict_from_url(_snake_case		,					model_dir=str(_snake_case						)		,					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(
               _snake_case		,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""		,					),
           """regnet-y-10b-seer-in1k""": partial(
               _snake_case		,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""		,					),
       }

       UpperCAmelCase					=	get_from_to_our_keys(_snake_case						)

       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(_snake_case		,					""""""		,					_snake_case						)
                     # 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(_snake_case						)
              # check that all keys have been updated
              assert len(_snake_case						) == 0, F'''Some keys where not used {','.join(_snake_case						)}'''

              logger.info(F'''The following keys were not used: {','.join(_snake_case						)}'''						)

              # save our state dict to disk
              torch.save(_snake_case		,					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(_snake_case						)
              # 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(
                  _snake_case		,					_snake_case		,					[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=_snake_case						)
              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_		:			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 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_		:			Union[str, Any]				   =					parser.parse_args()

 lowerCAmelCase_		:			Union[str, Any]				   =					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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_xglm import XGLMTokenizer
else:
  _UpperCamelCase     			=  None


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase     			=  {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

_UpperCamelCase     			=  {
    """facebook/xglm-564M""": 2048,
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=			['''input_ids''', '''attention_mask''']
    SCREAMING_SNAKE_CASE		=			XGLMTokenizer






    def __init__( self	,A=None	,A=None	,A="<s>"	,A="</s>"	,A="</s>"	,A="<s>"	,A="<unk>"	,A="<pad>"	,**A	,):
          # Compatibility with the original tokenizer
          UpperCAmelCase							=       7
          UpperCAmelCase							=       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

          UpperCAmelCase							=       kwargs.get("""additional_special_tokens"""	,[]					)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              A	,tokenizer_file=A	,bos_token=A	,eos_token=A	,sep_token=A	,cls_token=A	,unk_token=A	,pad_token=A	,**A	,)

          UpperCAmelCase							=       vocab_file
          UpperCAmelCase							=       False if not self.vocab_file else True






    def 				_UpperCamelCase							( self	,A	,A = None					):

          if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
          UpperCAmelCase							=       [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






    def 				_UpperCamelCase							( self	,A	,A = None					):
          UpperCAmelCase							=       [self.sep_token_id]

          if token_ids_a is None:
                return len(sep + token_ids_a					) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]






    def 				_UpperCamelCase							( self	,A	,A = None					):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""					)

          if not os.path.isdir(A					):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
                return
          UpperCAmelCase							=       os.path.join(
              A	,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

          if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
                copyfile(self.vocab_file	,A					)

          return (out_vocab_file,)



