

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 	=   logging.get_logger()







@dataclass
class 		__magic_name__     :

   '''simple docstring'''




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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       Dict:






     """simple docstring"""






     a__    =len(list(m.modules()			)			) == 1 or isinstance(lowercase_,       nn.Convad			) or isinstance(lowercase_,       nn.BatchNormad			)
     if has_not_submodules:
       self.traced.append(lowercase_			)
       a__    =m



   def __call__(							self,       lowercase_			)				->       str:






     """simple docstring"""






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



   @property
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()			)			) > 0}







class 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       Union[str, Any]:






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

     a__    =nn.ModuleDict(lowercase_			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[Any]:






     """simple docstring"""






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







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """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      UpperCAmelCase__							(     _A :					str				):





  '''simple docstring'''







  a__    =RegNetConfig(depths=[2, 7, 17, 1]      , hidden_sizes=[8, 8, 8, 8]      , groups_width=8				)
  if "in1k" in model_name:
    a__    =RegNetForImageClassification(_A				)
  else:
    a__    =RegNetModel(_A				)
  # create from model (with small weights)
  a__    =FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27      , group_width=10_10      , w_a=17_44      , 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, 32, 32)				)
    # trace both
    a__    =Tracker(_A				)
    a__    =dest_tracker(_A				).parametrized

    pprint(dest_tracker.nameamodule				)
    a__    =Tracker(_A				)
    a__    =src_tracker(_A				).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(_A :					Any				):
    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(_A				)
  a__    =to_params_dict(_A				)

  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      UpperCAmelCase__							(     _A :					Path      , _A :					str = None      , _A :					bool = True				):





  '''simple docstring'''







  a__    ='''imagenet-1k-id2label.json'''
  a__    =10_00

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

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

  a__    =partial(_A      , num_labels=_A      , idalabel=_A      , labelaid=_A				)

  a__    ={
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]      , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      , groups_width=10_10				),
      # finetuned on imagenet
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]      , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      , groups_width=10_10				),
  }

  # add seer weights logic
  def load_using_classy_vision(_A :					str				) -> Tuple[Dict, Dict]:
    a__    =torch.hub.load_state_dict_from_url(_A      , model_dir=str(_A				)      , 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(
          _A      , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''      , ),
      '''regnet-y-10b-seer-in1k''': partial(
          _A      , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''      , ),
  }

  a__    =get_from_to_our_keys(_A				)

  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(_A      , ''''''      , _A				)
      # 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(_A				)
    # check that all keys have been updated
    assert len(_A				) == 0, F"""Some keys where not used {",".join(_A				)}"""

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

    # save our state dict to disk
    torch.save(_A      , 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(_A				)
    # 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(
        _A      , _A      , [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_84
    # we can use the convnext one
    a__    =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''      , size=_A				)
    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 	=   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 	=   parser.parse_args()

       lowerCamelCase 	=   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)



import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =Dataset.from_dict({'''i''': range(17				)}				)
  a__    =len(_A				)
  a__    =3
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(_A				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =3
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




@pytest.mark.parametrize('''shards_per_node'''      , [1, 2, 3]				)
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  def gen(_A :					List[str]				):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17				))

  a__    =3
  a__    =shards_per_node * world_size
  a__    ={'''shards''': [F"""shard_{shard_idx}.txt""" for shard_idx in range(_A				)]}
  a__    =IterableDataset.from_generator(_A      , gen_kwargs=_A				)
  a__    =len(list(_A				)				)
  assert full_ds.n_shards == world_size * shards_per_node
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =2
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)

  a__    =split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle(seed=42				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle()

  a__    =split_dataset_by_node(full_ds.shuffle(seed=42				)      , rank=0      , world_size=_A				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(full_ds.shuffle()      , rank=0      , world_size=_A				)




@pytest.mark.parametrize('''streaming'''      , [False, True]				)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  a__    =2
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)




@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''      , [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]      , )
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					List[str]      , _A :					str				):





  '''simple docstring'''







  a__    =True
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)
