






"""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()
lowercase_    					=					logging.get_logger()


@dataclass
class  snake_case		:


   '''simple docstring'''

   A_						:     nn.Module
   A_						:     List[nn.Module]									=			field(default_factory=_lowerCAmelCase						)
   A_						:     list									=			field(default_factory=_lowerCAmelCase						)
   A_						:     Dict[str, nn.Module]									=			field(default_factory=_lowerCAmelCase						)


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Tensor,	_lowerCamelCase			:					Tensor,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     __A						      =			len(list(m.modules()  )  ) == 1 or isinstance(_lowerCamelCase,	nn.Convad  ) or isinstance(_lowerCamelCase,	nn.BatchNormad  )
     if has_not_submodules:
       self.traced.append(_lowerCamelCase  )
       __A						      =			m


   def __call__(     self			:					List[str],	_lowerCamelCase			:					Tensor  ):

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






   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''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  snake_case		(							nn.Module						):


   '''simple docstring'''

   def __init__(     self			:					Union[str, Any],	_lowerCamelCase			:					nn.Module  ):

     '''simple docstring'''
     super().__init__()

     __A						      =			[]
     # - 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						      =			len(_lowerCamelCase  ) + 1
       feature_blocks.append((f'res{block_index}', v)  )

     __A						      =			nn.ModuleDict(_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Tensor  ):

     '''simple docstring'''
     return get_trunk_forward_outputs(
         _lowerCamelCase,	out_feat_keys=_lowerCamelCase,	feature_blocks=self._feature_blocks,	)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''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    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			RegNetConfig(depths=[2, 7, 1_7, 1]		,	hidden_sizes=[8, 8, 8, 8]		,	groups_width=8			)
  if "in1k" in model_name:
    __A						      =			RegNetForImageClassification(__UpperCamelCase			)
  else:
    __A						      =			RegNetModel(__UpperCamelCase			)
  # create from model (with small weights)
  __A						      =			FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=2_7		,	group_width=1_0_1_0		,	w_a=1_7_4_4		,	w_a=620.83		,	w_m=2.52			)			)			)

  with torch.no_grad():
    __A						      =			from_model.eval()
    __A						      =			our_model.eval()

    __A						      =			torch.randn((1, 3, 3_2, 3_2)			)
    # trace both
    __A						      =			Tracker(__UpperCamelCase			)
    __A						      =			dest_tracker(__UpperCamelCase			).parametrized

    pprint(dest_tracker.nameamodule			)
    __A						      =			Tracker(__UpperCamelCase			)
    __A						      =			src_tracker(__UpperCamelCase			).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(__UpperCamelCase			):
    __A						      =			OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        __A						      =			param
    return params_dict

  __A						      =			{}

  __A						      =			to_params_dict(__UpperCamelCase			)
  __A						      =			to_params_dict(__UpperCamelCase			)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,	dst_state_dict.items()			):
    __A						      =			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						      =			'''classifier.1.weight'''
    __A						      =			'''classifier.1.bias'''

  return from_to_ours_keys



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




  """simple docstring"""







  __A						      =			'''imagenet-1k-id2label.json'''
  __A						      =			1_0_0_0

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

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

  __A						      =			partial(__UpperCamelCase		,	num_labels=__UpperCamelCase		,	idalabel=__UpperCamelCase		,	labelaid=__UpperCamelCase			)

  __A						      =			{
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]		,	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,	groups_width=1_0_1_0			),
      # finetuned on imagenet
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]		,	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,	groups_width=1_0_1_0			),
  }

  # add seer weights logic
  def load_using_classy_vision(__UpperCamelCase			) -> Tuple[Dict, Dict]:
    __A						      =			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						      =			files['''classy_state_dict''']['''base_model''']['''model''']
    return model_state_dict["trunk"], model_state_dict["heads"]

  __A						      =			{
      '''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						      =			get_from_to_our_keys(__UpperCamelCase			)

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

    __A						      =			{}

    __A						      =			list(from_state_dict.keys()			)
    __A						      =			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						      =			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						      =			from_to_ours_keys[src_key]
      # store the parameter with our key
      __A						      =			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						      =			names_to_config[model_name]
    __A						      =			RegNetModel
    if "in1k" in model_name:
      __A						      =			RegNetForImageClassification
    __A						      =			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						      =			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						      =			3_8_4
    # we can use the convnext one
    __A						      =			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__":
       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)












"""simple docstring"""


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


lowercase_    					=					logging.get_logger(__name__)


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       from transformers import Trainer


       class  snake_case		(							_lowerCAmelCase						):


          '''simple docstring'''

          def __init__(     self			:					List[Any],	_lowerCamelCase			:					int = 1_01  ):

            '''simple docstring'''
            __A						      =			length


          def __len__(     self			:					Optional[int]  ):

            '''simple docstring'''
            return self.length






          def __getitem__(     self			:					List[str],	_lowerCamelCase			:					Union[str, Any]  ):

            '''simple docstring'''
            return i





       class  snake_case		:


          '''simple docstring'''

          def __call__(     self			:					Tuple,	_lowerCamelCase			:					Dict  ):

            '''simple docstring'''
            return {"input_ids": torch.tensor(_lowerCamelCase  ), "labels": torch.tensor(_lowerCamelCase  )}





       class  snake_case		(							nn.Module						):


          '''simple docstring'''

          def __init__(     self			:					List[str]  ):

            '''simple docstring'''
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __A						      =			nn.Linear(1_20,	80  )






          def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Optional[Any]=None  ):

            '''simple docstring'''
            if labels is not None:
              return torch.tensor(0.0,	device=input_ids.device  ), input_ids
            else:
              return input_ids



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			HfArgumentParser((TrainingArguments,)			)
  sys.argv += ["--output_dir", "./examples"]
  __A						      =			parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f'Process rank: {training_args.local_rank}, device: {training_args.device}, '
      f'tpu_num_cores: {training_args.tpu_num_cores}'		,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
    __A						      =			DummyDataset(__UpperCamelCase			)

    def compute_metrics(__UpperCamelCase			) -> Dict:
      __A						      =			list(range(len(__UpperCamelCase			)			)			)
      __A						      =			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    __A						      =			Trainer(
        model=DummyModel()		,	args=__UpperCamelCase		,	data_collator=DummyDataCollator()		,	eval_dataset=__UpperCamelCase		,	compute_metrics=__UpperCamelCase		,	)
    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			2

    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			None

  logger.info('''🔥 All distributed tests successful'''			)



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  main()


if __name__ == "__main__":
       main()




