


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()
lowercase_           =	logging.get_logger()


@dataclass
class        A_					:




      '''simple docstring'''


      __snake_case					       =   42
      __snake_case					       =   field(default_factory=__UpperCamelCase    )
      __snake_case					       =   field(default_factory=__UpperCamelCase    )
      __snake_case					       =   field(default_factory=__UpperCamelCase    )




      def      _snake_case	(				self:       Tuple						,      a:       List[str]						,      a:       Tensor						,      a:       Tensor						,      a:       str				):
       __lowerCamelCase		:							int							=						len(list(m.modules()				)				) == 1 or isinstance(a						,      nn.Convad				) or isinstance(a						,      nn.BatchNormad				)
       if has_not_submodules:
        self.traced.append(a				)
        __lowerCamelCase		:							Any							=						m




      def __call__(				self:       Any						,      a:       Tensor				):
       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:       str				):
       # 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        A_					(       nn.Module    ):




      '''simple docstring'''


      def __init__(				self:       Optional[Any]						,      a:       nn.Module				):
       super().__init__()

       __lowerCamelCase		:							List[Tuple[str, nn.Module]]							=						[]
       # - 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}'
        __lowerCamelCase		:							Tuple							=						len(a				) + 1
        feature_blocks.append((F'res{block_index}', v)				)

       __lowerCamelCase		:							Any							=						nn.ModuleDict(a				)




      def      _snake_case	(				self:       int						,      a:       Tensor				):
       return get_trunk_forward_outputs(
           a						,      out_feat_keys=a						,      feature_blocks=self._feature_blocks						,      )



class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def      _snake_case	(				self:       str				):
       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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple							=						RegNetConfig(depths=[2, 7, 17, 1]			,       hidden_sizes=[8, 8, 8, 8]			,       groups_width=8						)
 if "in1k" in model_name:
  __lowerCamelCase		:							str							=						RegNetForImageClassification(SCREAMING_SNAKE_CASE__						)
 else:
  __lowerCamelCase		:							Optional[int]							=						RegNetModel(SCREAMING_SNAKE_CASE__						)
 # create from model (with small weights)
 __lowerCamelCase		:							int							=						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():
  __lowerCamelCase		:							Dict							=						from_model.eval()
  __lowerCamelCase		:							Optional[Any]							=						our_model.eval()

  __lowerCamelCase		:							int							=						torch.randn((1, 3, 32, 32)						)
  # trace both
  __lowerCamelCase		:							Any							=						Tracker(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Union[str, Any]							=						dest_tracker(SCREAMING_SNAKE_CASE__						).parametrized

  pprint(dest_tracker.nameamodule						)
  __lowerCamelCase		:							int							=						Tracker(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							List[Any]							=						src_tracker(SCREAMING_SNAKE_CASE__						).parametrized

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

 __lowerCamelCase		:							Tuple							=						{}

 __lowerCamelCase		:							Tuple							=						to_params_dict(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict							=						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()						):
  __lowerCamelCase		:							List[Any]							=						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:
  __lowerCamelCase		:							Optional[Any]							=						'classifier.1.weight'
  __lowerCamelCase		:							str							=						'classifier.1.bias'

 return from_to_ours_keys
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__ = None			,       SCREAMING_SNAKE_CASE__ = True						):
 __lowerCamelCase		:							List[Any]							=						'imagenet-1k-id2label.json'
 __lowerCamelCase		:							Tuple							=						1_000

 __lowerCamelCase		:							List[str]							=						'huggingface/label-files'
 __lowerCamelCase		:							str							=						num_labels
 __lowerCamelCase		:							Dict							=						json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       repo_type='dataset'						)						)			,       'r'						)						)
 __lowerCamelCase		:							Tuple							=						{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}

 __lowerCamelCase		:							Tuple							=						idalabel
 __lowerCamelCase		:							List[str]							=						{v: k for k, v in idalabel.items()}

 __lowerCamelCase		:							Union[str, Any]							=						partial(SCREAMING_SNAKE_CASE__			,       num_labels=SCREAMING_SNAKE_CASE__			,       idalabel=SCREAMING_SNAKE_CASE__			,       labelaid=SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Union[str, Any]							=						{
     '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(SCREAMING_SNAKE_CASE__						) -> Tuple[Dict, Dict]:
  __lowerCamelCase		:							Any							=						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
  __lowerCamelCase		:							Optional[int]							=						files['classy_state_dict']['base_model']['model']
  return model_state_dict["trunk"], model_state_dict["heads"]

 __lowerCamelCase		:							Dict							=						{
     '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'			,       ),
 }

 __lowerCamelCase		:							List[str]							=						get_from_to_our_keys(SCREAMING_SNAKE_CASE__						)

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

  __lowerCamelCase		:							Tuple							=						{}

  __lowerCamelCase		:							int							=						list(from_state_dict.keys()						)
  __lowerCamelCase		:							Any							=						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
   __lowerCamelCase		:							Union[str, Any]							=						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
   __lowerCamelCase		:							Dict							=						from_to_ours_keys[src_key]
   # store the parameter with our key
   __lowerCamelCase		:							int							=						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
  __lowerCamelCase		:							Any							=						names_to_config[model_name]
  __lowerCamelCase		:							Tuple							=						RegNetModel
  if "in1k" in model_name:
   __lowerCamelCase		:							Union[str, Any]							=						RegNetForImageClassification
  __lowerCamelCase		:							List[Any]							=						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
  __lowerCamelCase		:							Union[str, Any]							=						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			,       )
  __lowerCamelCase		:							str							=						384
  # we can use the convnext one
  __lowerCamelCase		:							List[Any]							=						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__":
       lowercase_           =	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.',
       )

       lowercase_           =	parser.parse_args()

       lowercase_           =	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 typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowercase_           =	datasets.utils.logging.get_logger(__name__)


class        A_					(       folder_based_builder.FolderBasedBuilderConfig    ):




      '''simple docstring'''


      __snake_case					       =   None
      __snake_case					       =   None



class        A_					(       folder_based_builder.FolderBasedBuilder    ):




      '''simple docstring'''


      __snake_case					       =   datasets.Image()
      __snake_case					       =   """image"""
      __snake_case					       =   ImageFolderConfig
      __snake_case					       =   42  # definition at the bottom of the script
      __snake_case					       =   ImageClassification(image_column="""image"""					,      label_column="""label"""    )



lowercase_           =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
lowercase_           =	IMAGE_EXTENSIONS

