






"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					:				List[Any]					    =						logging.get_logger()






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     List[nn.Module]						= field(default_factory=__snake_case		)
    SCREAMING_SNAKE_CASE						:     list						= field(default_factory=__snake_case		)






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Tensor       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		Optional[int]:
      lowerCamelCase_         =						len(list(m.modules()					)					) == 1 or isinstance(__SCREAMING_SNAKE_CASE       ,						nn.Convad					) or isinstance(__SCREAMING_SNAKE_CASE       ,						nn.BatchNormad					)
      if has_not_submodules:
        self.traced.append(__SCREAMING_SNAKE_CASE					)






    def __call__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		Tuple:
      for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook					)					)
      self.module(__SCREAMING_SNAKE_CASE					)
      [x.remove() for x in self.handles]
      return self






    @property
    def    UpperCamelCase       (       self     :							List[str]					)     ->		int:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __SCREAMING_SNAKE_CASE					: len(list(x.state_dict().keys()					)					) > 0       ,						self.traced					)					)






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     int						= 0
    SCREAMING_SNAKE_CASE						:     List						= field(default_factory=__snake_case		)
    SCREAMING_SNAKE_CASE						:     List						= field(default_factory=__snake_case		)






    def __call__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		str:
      lowerCamelCase_         =						Tracker(self.dest					)(__SCREAMING_SNAKE_CASE					).parametrized
      lowerCamelCase_         =						Tracker(self.src					)(__SCREAMING_SNAKE_CASE					).parametrized

      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: type(__SCREAMING_SNAKE_CASE					) not in self.src_skip       ,						__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: type(__SCREAMING_SNAKE_CASE					) not in self.dest_skip       ,						__SCREAMING_SNAKE_CASE					)					)

      if len(__SCREAMING_SNAKE_CASE					) != len(__SCREAMING_SNAKE_CASE					):
        raise Exception(
            F'''Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE					)} operations while'''
            F''' destination module has {len(__SCREAMING_SNAKE_CASE					)}.'''					)

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







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		ResNetConfig		,				_lowerCamelCase       :		Path		,				_lowerCamelCase       :		bool = True )      ->				Tuple:
  print(F'''Converting {name}...''' )
  with torch.no_grad():
    lowerCamelCase_         =						timm.create_model(_lowerCamelCase		,				pretrained=_lowerCamelCase ).eval()
    lowerCamelCase_         =						ResNetForImageClassification(_lowerCamelCase ).eval()
    lowerCamelCase_         =						ModuleTransfer(src=_lowerCamelCase		,				dest=_lowerCamelCase )
    lowerCamelCase_         =						torch.randn((1, 3, 224, 224) )
    module_transfer(_lowerCamelCase )

  assert torch.allclose(from_model(_lowerCamelCase )		,				our_model(_lowerCamelCase ).logits ), "The model logits don't match the original one."

  lowerCamelCase_         =						F'''resnet{"-".join(name.split("resnet" ) )}'''
  print(_lowerCamelCase )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,				commit_message='Add model'		,				use_temp_dir=_lowerCamelCase		,				)

    # we can use the convnext one
    lowerCamelCase_         =						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,				commit_message='Add image processor'		,				use_temp_dir=_lowerCamelCase		,				)

    print(F'''Pushed {checkpoint_name}''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		Path		,				_lowerCamelCase       :		str = None		,				_lowerCamelCase       :		bool = True )      ->				Tuple:
  lowerCamelCase_         =						'imagenet-1k-id2label.json'
  lowerCamelCase_         =						1000
  lowerCamelCase_         =						(1, num_labels)

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

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

  lowerCamelCase_         =						partial(_lowerCamelCase		,				num_labels=_lowerCamelCase		,				idalabel=_lowerCamelCase		,				labelaid=_lowerCamelCase )

  lowerCamelCase_         =						{
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,				hidden_sizes=[64, 128, 256, 512]		,				layer_type='basic' ),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck' ),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,				hidden_sizes=[64, 128, 256, 512]		,				layer_type='basic' ),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck' ),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck' ),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck' ),
  }

  if model_name:
    convert_weight_and_push(_lowerCamelCase		,				names_to_config[model_name]		,				_lowerCamelCase		,				_lowerCamelCase )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
  return config, expected_shape


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				List[str]					    =						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 resnet* architecture,'''
          ''' currently: resnet18,26,34,50,101,152. 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.''',
  )

  _SCREAMING_SNAKE_CASE					:				Dict					    =						parser.parse_args()
  _SCREAMING_SNAKE_CASE					:				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)












"""simple docstring"""





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_SCREAMING_SNAKE_CASE					:				Any					    =						importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
  from .safilesystem import SaFileSystem  # noqa: F401

_SCREAMING_SNAKE_CASE					:				List[compression.BaseCompressedFileFileSystem]					    =						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
  if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
    warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
  fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				str:
  if "://" in dataset_path:
    lowerCamelCase_         =						dataset_path.split('://' )[1]
  return dataset_path







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem )      ->				bool:
  if fs is not None and fs.protocol != "file":
    return True
  else:
    return False







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		str )      ->				int:
  lowerCamelCase_         =						not is_remote_filesystem(_lowerCamelCase )
  if is_local:
    # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
    shutil.move(fs._strip_protocol(_lowerCamelCase )		,				fs._strip_protocol(_lowerCamelCase ) )
  else:
    fs.mv(_lowerCamelCase		,				_lowerCamelCase		,				recursive=_lowerCamelCase )







def      lowerCamelCase__       (							)      ->				None:
  if hasattr(fsspec.asyn		,				'reset_lock' ):
    # for future fsspec>2022.05.0
    fsspec.asyn.reset_lock()
  else:
    lowerCamelCase_         =						None
    lowerCamelCase_         =						None
    lowerCamelCase_         =						threading.Lock()




