





import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
   from comet_ml import OfflineExperiment

if is_tensorboard_available():
   import struct

   import tensorboard.compat.proto.event_pba as event_pba

lowerCamelCase_					:						Optional[int]      = logging.getLogger(__name__)

@require_tensorboard
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  'test_project_with_config'
        with tempfile.TemporaryDirectory() as dirpath:
          A_  :	Any    						=  Accelerator(log_with='tensorboard' ,				project_dir=lowercase__  )
          A_  :	Any    						=  {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
          accelerator.init_trackers(lowercase__ ,				lowercase__  )
          accelerator.end_training()
          for child in Path(F"""{dirpath}/{project_name}"""  ).glob('*/**'  ):
            A_  :	Union[str, Any]    						=  list(filter(lambda snake_case_  : x.is_file() ,				child.iterdir()  )  )[0]
          self.assertNotEqual(str(lowercase__  ) ,				''  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  'test_project_with_log'
        with tempfile.TemporaryDirectory() as dirpath:
          A_  :	int    						=  Accelerator(log_with='tensorboard' ,				project_dir=lowercase__  )
          accelerator.init_trackers(lowercase__  )
          A_  :	int    						=  {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
          accelerator.log(lowercase__ ,				step=0  )
          accelerator.end_training()
          # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
          # Names are randomly generated each time
          A_  :	Union[str, Any]    						=  list(filter(lambda snake_case_  : x.is_file() ,				Path(F"""{dirpath}/{project_name}"""  ).iterdir()  )  )[0]
          self.assertNotEqual(str(lowercase__  ) ,				''  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  'test_project_with_log'
        with tempfile.TemporaryDirectory() as dirpath:
          A_  :	Optional[int]    						=  Accelerator(log_with='tensorboard' ,				project_dir=lowercase__  )
          accelerator.init_trackers(lowercase__  )
          A_  :	int    						=  {'tensor': torch.tensor(1  )}
          accelerator.log(lowercase__ ,				step=0  )
          accelerator.end_training()
          # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
          # Names are randomly generated each time
          A_  :	Any    						=  list(filter(lambda snake_case_  : x.is_file() ,				Path(F"""{dirpath}/{project_name}"""  ).iterdir()  )  )[0]
          # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
          with open(lowercase__ ,				'rb'  ) as f:
            A_  :	Optional[int]    						=  f.read()
          A_  :	str    						=  False
          while data:
            A_  :	Tuple    						=  struct.unpack('Q' ,				data[:8]  )

            A_  :	Dict    						=  data[1_2 : 1_2 + int(header[0]  )]  # 8+4
            A_  :	Tuple    						=  data[1_2 + int(header[0]  ) + 4 :]
            A_  :	Optional[Any]    						=  event_pba.Event()

            event.ParseFromString(lowercase__  )
            if event.HasField('summary'  ):
              for value in event.summary.value:
                if value.simple_value == 1.0 and value.tag == "tensor":
                  A_  :	List[str]    						=  True
          self.assertTrue(lowercase__ ,				'Converted tensor was not found in the log file!'  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        with self.assertRaisesRegex(lowercase__ ,				'Logging with `tensorboard` requires a `logging_dir`'  ):
          A_  :	Optional[int]    						=  Accelerator(log_with='tensorboard'  )
        with tempfile.TemporaryDirectory() as dirpath:
          A_  :	Optional[Any]    						=  Accelerator(log_with='tensorboard' ,				project_dir=lowercase__  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  ProjectConfiguration(total_limit=3_0  )
        with tempfile.TemporaryDirectory() as dirpath:
          A_  :	Tuple    						=  Accelerator(log_with='tensorboard' ,				project_dir=lowercase__ ,				project_config=lowercase__  )


@require_wandb
@mock.patch.dict(os.environ      ,       {"""WANDB_MODE""": """offline"""}		)
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       UpperCAmelCase__		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        super().setUp()
        # wandb let's us override where logs are stored to via the WANDB_DIR env var
        self.add_mocks(mock.patch.dict(os.environ ,				{'WANDB_DIR': self.tmpdir}  )  )

      @staticmethod
      def  lowerCamelCase_      (					snake_case_ ,				snake_case_ ,				snake_case_ = True  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  RF"""{section} ([\S\s]*?)\n\n"""
        if record:
          A_  :	int    						=  RF"""Record: {pattern}"""
        A_  :	int    						=  re.findall(lowercase__ ,				lowercase__  )[0]
        # A config
        if section == "config" or section == "history":
          A_  :	Optional[Any]    						=  re.findall(R'"([a-zA-Z0-9_.,]+)' ,				lowercase__  )
          return {key: val for key, val in zip(cleaned_record[0::2] ,				cleaned_record[1::2]  )}
        # Everything else
        else:
          return dict(re.findall(R'(\w+): "([^\s]+)"' ,				lowercase__  )  )

      @skip
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  'test_project_with_config'
        A_  :	List[Any]    						=  Accelerator(log_with='wandb'  )
        A_  :	Optional[int]    						=  {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
        A_  :	Optional[Any]    						=  {'wandb': {'tags': ['my_tag']}}
        accelerator.init_trackers(lowercase__ ,				lowercase__ ,				lowercase__  )
        A_  :	int    						=  {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
        accelerator.log(lowercase__ ,				step=0  )
        accelerator.end_training()
        # The latest offline log is stored at wandb/latest-run/*.wandb
        for child in Path(F"""{self.tmpdir}/wandb/latest-run"""  ).glob('*'  ):
          if child.is_file() and child.suffix == ".wandb":
            A_  :	Any    						=  subprocess.check_output(
                ['wandb', 'sync', '--view', '--verbose', str(lowercase__  )] ,				env=os.environ.copy()  ).decode('utf8' ,				'ignore'  )
            break

        # Check HPS through careful parsing and cleaning
        A_  :	Dict    						=  self.parse_log(lowercase__ ,				'config'  )
        self.assertEqual(logged_items['num_iterations'] ,				'12'  )
        self.assertEqual(logged_items['learning_rate'] ,				'0.01'  )
        self.assertEqual(logged_items['some_boolean'] ,				'false'  )
        self.assertEqual(logged_items['some_string'] ,				'some_value'  )
        self.assertEqual(logged_items['some_string'] ,				'some_value'  )

        # Run tags
        A_  :	Any    						=  self.parse_log(lowercase__ ,				'run' ,				lowercase__  )
        self.assertEqual(logged_items['tags'] ,				'my_tag'  )

        # Actual logging
        A_  :	Any    						=  self.parse_log(lowercase__ ,				'history'  )
        self.assertEqual(logged_items['total_loss'] ,				'0.1'  )
        self.assertEqual(logged_items['iteration'] ,				'1'  )
        self.assertEqual(logged_items['my_text'] ,				'some_value'  )
        self.assertEqual(logged_items['_step'] ,				'0'  )





def 						UpperCAmelCase__		(					self			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	List[str]    						=  run_name
  A_  :	Optional[int]    						=  OfflineExperiment(project_name=SCREAMING_SNAKE_CASE_			, offline_directory=SCREAMING_SNAKE_CASE_ )
  logger.info(f"""Initialized offline CometML project {self.run_name}""" )
  logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!' )

@require_comet_ml
@mock.patch.object(UpperCAmelCase__      ,       """__init__"""      ,       UpperCAmelCase__		)
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      @staticmethod
      def  lowerCamelCase_      (					snake_case_ ,				snake_case_ ,				snake_case_ = False  ):

        """simple docstring"""

        for log in log_list:
          A_  :	Optional[int]    						=  json.loads(lowercase__  )['payload']
          if is_param and "param" in j.keys():
            if j["param"]["paramName"] == key:
              return j["param"]["paramValue"]
          if "log_other" in j.keys():
            if j["log_other"]["key"] == key:
              return j["log_other"]["val"]
          if "metric" in j.keys():
            if j["metric"]["metricName"] == key:
              return j["metric"]["metricValue"]

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        with tempfile.TemporaryDirectory() as d:
          A_  :	Dict    						=  CometMLTracker('test_project_with_config' ,				lowercase__  )
          A_  :	Any    						=  Accelerator(log_with=lowercase__  )
          A_  :	List[Any]    						=  {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
          accelerator.init_trackers(lowercase__ ,				lowercase__  )
          accelerator.end_training()
          A_  :	Dict    						=  os.listdir(lowercase__  )[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          A_  :	Optional[int]    						=  os.path.join(lowercase__ ,				lowercase__  )
          A_  :	List[str]    						=  zipfile.ZipFile(lowercase__ ,				'r'  )
          A_  :	Optional[int]    						=  archive.open('messages.json'  ).read().decode('utf-8'  )
        A_  :	Tuple    						=  log.split('\n'  )[:-1]
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'num_iterations' ,				lowercase__  ) ,				1_2  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'learning_rate' ,				lowercase__  ) ,				0.01  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'some_boolean' ,				lowercase__  ) ,				lowercase__  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'some_string' ,				lowercase__  ) ,				'some_value'  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        with tempfile.TemporaryDirectory() as d:
          A_  :	Dict    						=  CometMLTracker('test_project_with_config' ,				lowercase__  )
          A_  :	Tuple    						=  Accelerator(log_with=lowercase__  )
          accelerator.init_trackers(lowercase__  )
          A_  :	List[Any]    						=  {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
          accelerator.log(lowercase__ ,				step=0  )
          accelerator.end_training()
          A_  :	Tuple    						=  os.listdir(lowercase__  )[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          A_  :	List[Any]    						=  os.path.join(lowercase__ ,				lowercase__  )
          A_  :	Optional[int]    						=  zipfile.ZipFile(lowercase__ ,				'r'  )
          A_  :	int    						=  archive.open('messages.json'  ).read().decode('utf-8'  )
        A_  :	Tuple    						=  log.split('\n'  )[:-1]
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'curr_step' ,				lowercase__  ) ,				0  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'total_loss'  ) ,				0.1  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'iteration'  ) ,				1  )
        self.assertEqual(self.get_value_from_key(lowercase__ ,				'my_text'  ) ,				'some_value'  )


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			str        =       [
          "total_loss",
          "iteration",
          "my_text",
          "learning_rate",
          "num_iterations",
          "some_boolean",
          "some_string",
      ]

      lowercase_				:			Optional[int]        =       "my_custom_tracker"
      lowercase_				:			Union[str, Any]        =       False

      def __init__(					self ,				snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  open(F"""{dir}/log.csv""" ,				'w+'  )
        A_  :	Optional[int]    						=  csv.DictWriter(self.f ,				fieldnames=self._col_names  )
        self.writer.writeheader()

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.writer

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        logger.info('Call init'  )
        self.writer.writerow(lowercase__  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        logger.info('Call log'  )
        self.writer.writerow(lowercase__  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self.f.close()


class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        with tempfile.TemporaryDirectory() as d:
          A_  :	Dict    						=  MyCustomTracker(lowercase__  )
          A_  :	Union[str, Any]    						=  Accelerator(log_with=lowercase__  )
          A_  :	int    						=  {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
          accelerator.init_trackers('Some name' ,				lowercase__  )
          accelerator.end_training()
          with open(F"""{d}/log.csv""" ,				'r'  ) as f:
            A_  :	str    						=  csv.DictReader(lowercase__  )
            A_  :	int    						=  next(lowercase__  )
            A_  :	Optional[Any]    						=  {
                'total_loss': '',
                'iteration': '',
                'my_text': '',
                'learning_rate': '0.01',
                'num_iterations': '12',
                'some_boolean': 'False',
                'some_string': 'some_value',
            }
            self.assertDictEqual(lowercase__ ,				lowercase__  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        with tempfile.TemporaryDirectory() as d:
          A_  :	int    						=  MyCustomTracker(lowercase__  )
          A_  :	str    						=  Accelerator(log_with=lowercase__  )
          accelerator.init_trackers('Some name'  )
          A_  :	List[str]    						=  {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
          accelerator.log(lowercase__ ,				step=0  )
          accelerator.end_training()
          with open(F"""{d}/log.csv""" ,				'r'  ) as f:
            A_  :	List[Any]    						=  csv.DictReader(lowercase__  )
            A_  :	Tuple    						=  next(lowercase__  )
            A_  :	Optional[Any]    						=  {
                'total_loss': '0.1',
                'iteration': '1',
                'my_text': 'some_value',
                'learning_rate': '',
                'num_iterations': '',
                'some_boolean': '',
                'some_string': '',
            }
            self.assertDictEqual(lowercase__ ,				lowercase__  )






"""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_					:						Dict      = logging.get_logger()

@dataclass
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			nn.Module
      lowercase_				:			List[nn.Module]        =       field(default_factory=UpperCAmelCase__		)
      lowercase_				:			list        =       field(default_factory=UpperCAmelCase__		)
      lowercase_				:			Dict[str, nn.Module]        =       field(default_factory=UpperCAmelCase__		)

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Any    						=  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_  )
          A_  :	List[str]    						=  m

      def __call__(					self ,				snake_case_  ):

        """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  lowerCamelCase_      (					self  ):

        """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 ,				snake_case_  ):

        """simple docstring"""

        super().__init__()

        A_  :	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}"""
          A_  :	str    						=  len(snake_case_  ) + 1
          feature_blocks.append((F"""res{block_index}""", v)  )

        A_  :	Dict    						=  nn.ModuleDict(snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

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


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """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 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  RegNetConfig(depths=[2, 7, 17, 1]			, hidden_sizes=[8, 8, 8, 8]			, groups_width=8 )
  if "in1k" in model_name:
    A_  :	Any    						=  RegNetForImageClassification(_UpperCAmelCase )
  else:
    A_  :	str    						=  RegNetModel(_UpperCAmelCase )
  # create from model (with small weights)
  A_  :	Dict    						=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27			, group_width=1010			, w_a=1744			, w_a=620.83			, w_m=2.52 ) ) )

  with torch.no_grad():
    A_  :	Union[str, Any]    						=  from_model.eval()
    A_  :	Optional[int]    						=  our_model.eval()

    A_  :	Tuple    						=  torch.randn((1, 3, 32, 32) )
    # trace both
    A_  :	int    						=  Tracker(_UpperCAmelCase )
    A_  :	Any    						=  dest_tracker(_UpperCAmelCase ).parametrized

    pprint(dest_tracker.nameamodule )
    A_  :	Union[str, Any]    						=  Tracker(_UpperCAmelCase )
    A_  :	Tuple    						=  src_tracker(_UpperCAmelCase ).parametrized

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

  A_  :	Dict    						=  {}

  A_  :	Optional[Any]    						=  to_params_dict(_UpperCAmelCase )
  A_  :	List[str]    						=  to_params_dict(_UpperCAmelCase )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()			, dst_state_dict.items() ):
    A_  :	Optional[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:
    A_  :	int    						=  'classifier.1.weight'
    A_  :	Tuple    						=  'classifier.1.bias'

  return from_to_ours_keys





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = None			, _UpperCAmelCase = True ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  'imagenet-1k-id2label.json'
  A_  :	Dict    						=  1000

  A_  :	List[Any]    						=  'huggingface/label-files'
  A_  :	Optional[int]    						=  num_labels
  A_  :	Union[str, Any]    						=  json.load(open(cached_download(hf_hub_url(_UpperCAmelCase			, _UpperCAmelCase			, repo_type='dataset' ) )			, 'r' ) )
  A_  :	Any    						=  {int(_UpperCAmelCase ): v for k, v in idalabel.items()}

  A_  :	List[Any]    						=  idalabel
  A_  :	List[Any]    						=  {v: k for k, v in idalabel.items()}

  A_  :	int    						=  partial(_UpperCAmelCase			, num_labels=_UpperCAmelCase			, idalabel=_UpperCAmelCase			, labelaid=_UpperCAmelCase )

  A_  :	Dict    						=  {
      '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(_UpperCAmelCase ) -> Tuple[Dict, Dict]:
    A_  :	Any    						=  torch.hub.load_state_dict_from_url(_UpperCAmelCase			, model_dir=str(_UpperCAmelCase )			, map_location='cpu' )
    # check if we have a head, if yes add it
    A_  :	Optional[Any]    						=  files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  A_  :	Optional[Any]    						=  {
      'regnet-y-10b-seer': partial(
          _UpperCAmelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'			, ),
      'regnet-y-10b-seer-in1k': partial(
          _UpperCAmelCase			, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'			, ),
  }

  A_  :	List[str]    						=  get_from_to_our_keys(_UpperCAmelCase )

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

    A_  :	List[str]    						=  {}

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

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

    # save our state dict to disk
    torch.save(_UpperCAmelCase			, 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
    A_  :	Dict    						=  names_to_config[model_name]
    A_  :	Tuple    						=  RegNetModel
    if "in1k" in model_name:
      A_  :	Union[str, Any]    						=  RegNetForImageClassification
    A_  :	Union[str, Any]    						=  our_model_func(_UpperCAmelCase )
    # 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
    A_  :	Optional[Any]    						=  our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        _UpperCAmelCase			, _UpperCAmelCase			, [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			, )
    A_  :	Tuple    						=  384
    # we can use the convnext one
    A_  :	int    						=  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'			, size=_UpperCAmelCase )
    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_					:						Any      = parser.parse_args()

   lowerCamelCase_					:						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)