

'''simple docstring'''




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_a				: Union[str, Any]									=						logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_a				: Optional[int]									=						direct_transformers_import(Path(__file__).parent)


_a				: Tuple									=						{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}



class        lowercase_	(		a_ ):

  '''simple docstring'''





  __lowerCAmelCase					: Tuple      =   ['feature_extractor', 'tokenizer']
  # Names need to be attr_class for attr in attributes
  __lowerCAmelCase					: List[str]      =   None
  __lowerCAmelCase					: Union[str, Any]      =   None
  __lowerCAmelCase					: Optional[Any]      =   None
  def __init__(     self   , *a_   , **a_     )							->				List[Any]:


    """simple docstring"""
    # Sanitize args and kwargs
    for key in kwargs:
      if key not in self.attributes:
        raise TypeError(F'''Unexpected keyword argument {key}.'''     )
    for arg, attribute_name in zip(a_   , self.attributes     ):
      if attribute_name in kwargs:
        raise TypeError(F'''Got multiple values for argument {attribute_name}.'''     )
      else:
        UpperCAmelCase													=	arg

    if len(a_     ) != len(self.attributes     ):
      raise ValueError(
          F'''This processor requires {len(self.attributes     )} arguments: {', '.join(self.attributes     )}. Got '''
          F'''{len(a_     )} arguments instead.'''     )

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
      UpperCAmelCase													=	getattr(self   , F'''{attribute_name}_class'''     )
      # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
      UpperCAmelCase													=	AUTO_TO_BASE_CLASS_MAPPING.get(a_   , a_     )
      if isinstance(a_   , a_     ):
        UpperCAmelCase													=	tuple(getattr(a_   , a_     ) for n in class_name if n is not None     )
      else:
        UpperCAmelCase													=	getattr(a_   , a_     )

      if not isinstance(a_   , a_     ):
        raise ValueError(
            F'''Received a {type(a_     ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''     )

      setattr(self   , a_   , a_     )
  def __repr__(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	[F'''- {name}: {repr(getattr(self   , a_     )     )}''' for name in self.attributes]
    UpperCAmelCase													=	'\n'.join(a_     )
    return F'''{self.__class__.__name__}:\n{attributes_repr}'''
  def 						snake_case_			(     self   , a_   , a_ = False   , **a_     )							->				Union[str, Any]:


    """simple docstring"""
    os.makedirs(a_   , exist_ok=a_     )

    if push_to_hub:
      UpperCAmelCase													=	kwargs.pop('commit_message'   , a_     )
      UpperCAmelCase													=	kwargs.pop('repo_id'   , save_directory.split(os.path.sep     )[-1]     )
      UpperCAmelCase													=	self._create_repo(a_   , **a_     )
      UpperCAmelCase													=	self._get_files_timestamps(a_     )
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
      UpperCAmelCase													=	[getattr(self   , a_     ) for attribute_name in self.attributes]
      UpperCAmelCase													=	[(a.init_kwargs if isinstance(a_   , a_     ) else a) for a in attrs]
      custom_object_save(self   , a_   , config=a_     )

    for attribute_name in self.attributes:
      UpperCAmelCase													=	getattr(self   , a_     )
      # Include the processor class in the attribute config so this processor can then be reloaded with the
      # `AutoProcessor` API.
      if hasattr(a_   , '_set_processor_class'     ):
        attribute._set_processor_class(self.__class__.__name__     )
      attribute.save_pretrained(a_     )

    if self._auto_class is not None:
      # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
      for attribute_name in self.attributes:
        UpperCAmelCase													=	getattr(self   , a_     )
        if isinstance(a_   , a_     ):
          del attribute.init_kwargs["auto_map"]

    if push_to_hub:
      self._upload_modified_files(
          a_   , a_   , a_   , commit_message=a_   , token=kwargs.get('use_auth_token'     )   , )
  @classmethod
  def 						snake_case_			(     cls   , a_   , a_ = None   , a_ = False   , a_ = False   , a_ = None   , a_ = "main"   , **a_   , )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	cache_dir
    UpperCAmelCase													=	force_download
    UpperCAmelCase													=	local_files_only
    UpperCAmelCase													=	revision

    UpperCAmelCase													=	kwargs.pop('use_auth_token'   , a_     )
    if use_auth_token is not None:
      warnings.warn(
          'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'   , a_     )
      if token is not None:
        raise ValueError(
            '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'     )
      UpperCAmelCase													=	use_auth_token

    if token is not None:
      # change to `token` in a follow-up PR
      UpperCAmelCase													=	token

    UpperCAmelCase													=	cls._get_arguments_from_pretrained(a_   , **a_     )
    return cls(*a_     )
  @classmethod
  def 						snake_case_			(     cls   , a_="AutoProcessor"     )							->				List[str]:


    """simple docstring"""
    if not isinstance(a_   , a_     ):
      UpperCAmelCase													=	auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(a_   , a_     ):
      raise ValueError(F'''{auto_class} is not a valid auto class.'''     )

    UpperCAmelCase													=	auto_class
  @classmethod
  def 						snake_case_			(     cls   , a_   , **a_     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	[]
    for attribute_name in cls.attributes:
      UpperCAmelCase													=	getattr(cls   , F'''{attribute_name}_class'''     )
      if isinstance(a_   , a_     ):
        UpperCAmelCase													=	tuple(getattr(a_   , a_     ) if n is not None else None for n in class_name     )
        UpperCAmelCase													=	kwargs.get('use_fast'   , a_     )
        if use_fast and classes[1] is not None:
          UpperCAmelCase													=	classes[1]
        else:
          UpperCAmelCase													=	classes[0]
      else:
        UpperCAmelCase													=	getattr(a_   , a_     )

      args.append(attribute_class.from_pretrained(a_   , **a_     )     )
    return args





  @property
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	getattr(self   , self.attributes[0]     )
    return getattr(a_   , 'model_input_names'   , a_     )




_a				: Optional[int]									=						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  _a				: str									=						ProcessorMixin.push_to_hub.__doc__.format(
      object='processor', object_class='AutoProcessor', object_files='processor files'
  )





'''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[int] 							=						logging.get_logger()



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: nn.Module
  __lowerCAmelCase					: List[nn.Module]      =   field(default_factory=a )
  __lowerCAmelCase					: list      =   field(default_factory=a )
  __lowerCAmelCase					: Dict[str, nn.Module]      =   field(default_factory=a )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	len(list(m.modules()     )     ) == 1 or isinstance(a_   , nn.Convad     ) or isinstance(a_   , nn.BatchNormad     )
    if has_not_submodules:
      self.traced.append(a_     )
      UpperCAmelCase													=	m
  def __call__(     self   , a_     )							->				List[Any]:


    """simple docstring"""
    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 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}




class        lowercase_	(		nn.Module ):

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Tuple:


    """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(a_     ) + 1
      feature_blocks.append((F'''res{block_index}''', v)     )

    UpperCAmelCase													=	nn.ModuleDict(a_     )





  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    return get_trunk_forward_outputs(
        a_   , out_feat_keys=a_   , feature_blocks=self._feature_blocks   , )




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8						)
  if "in1k" in model_name:
    UpperCAmelCase													=	RegNetForImageClassification(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	RegNetModel(SCREAMING_SNAKE_CASE						)
  # create from model (with small weights)
  UpperCAmelCase													=	FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   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(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	dest_tracker(SCREAMING_SNAKE_CASE						).parametrized

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

  # convert the keys -> module dict to keys -> params
  def to_params_dict(SCREAMING_SNAKE_CASE      :   Tuple						):
    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(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	to_params_dict(SCREAMING_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__		(						SCREAMING_SNAKE_CASE      :   Path       ,   SCREAMING_SNAKE_CASE      :   str = None       ,   SCREAMING_SNAKE_CASE      :   bool = True						):
  UpperCAmelCase													=	'imagenet-1k-id2label.json'
  UpperCAmelCase													=	1000

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

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

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

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
  }

  # add seer weights logic
  def load_using_classy_vision(SCREAMING_SNAKE_CASE      :   str						) -> Tuple[Dict, Dict]:
    UpperCAmelCase													=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,   model_dir=str(SCREAMING_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(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,   ),
      'regnet-y-10b-seer-in1k': partial(
          SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE       ,   ''       ,   SCREAMING_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(SCREAMING_SNAKE_CASE						)
    # check that all keys have been updated
    assert len(SCREAMING_SNAKE_CASE						) == 0, f'''Some keys where not used {','.join(SCREAMING_SNAKE_CASE						)}'''

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

    # save our state dict to disk
    torch.save(SCREAMING_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(SCREAMING_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(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_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=SCREAMING_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__":
  _a				: Dict 							=						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				: Union[str, Any] 							=						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)


