



"""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()
__snake_case	:				List[Any]  					=			logging.get_logger()

@dataclass
class       UpperCamelCase  :





     """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 						A__  (					self :							List[str]	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[int] ):
          A__				=   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 )
               A__				=   m





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

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





     @property
     def 						A__  (					self :							List[Any] ):
          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 :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          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(__SCREAMING_SNAKE_CASE ) + 1
               feature_blocks.append((F'''res{block_index}''', v) )

          A__				=   nn.ModuleDict(__SCREAMING_SNAKE_CASE )





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Union[str, Any] ):
          return get_trunk_forward_outputs(
              __SCREAMING_SNAKE_CASE	,      out_feat_keys=__SCREAMING_SNAKE_CASE	,      feature_blocks=self._feature_blocks	,      )

class       UpperCamelCase  (					lowerCAmelCase_				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          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   a_	(			__a      ):
     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=1010		,							w_a=1744		,							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      ):
          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   a_	(			__a		,							__a = None		,							__a = True      ):
     A__				=   '''imagenet-1k-id2label.json'''
     A__				=   1000

     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=[2020, 4040, 1_1110, 2_8280]		,							groups_width=1010      ),
         # finetuned on imagenet
         '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]		,							hidden_sizes=[2020, 4040, 1_1110, 2_8280]		,							groups_width=1010      ),
     }

     # add seer weights logic
     def load_using_classy_vision(__a      ) -> 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__				=   384
          # 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__":
 __snake_case	:				Tuple  					=			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.',
 )

 __snake_case	:				Optional[Any]  					=			parser.parse_args()

 __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 argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case	:				Tuple  					=			16
__snake_case	:				Any  					=			32



def   a_	(			__a		,							__a      ):
     # Initialize accelerator
     if args.with_tracking:
          A__				=   Accelerator(
              cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='''all'''		,							project_dir=args.project_dir      )
     else:
          A__				=   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps		,							'''isdigit'''      ):
          if args.checkpointing_steps == "epoch":
               A__				=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A__				=   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
     else:
          A__				=   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A__				=   config['''lr''']
     A__				=   int(config['''num_epochs''']      )
     A__				=   int(config['''seed''']      )
     A__				=   int(config['''batch_size''']      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A__				=   os.path.split(__a      )[-1].split('''.'''      )[0]
          accelerator.init_trackers(__a		,							__a      )

     A__				=   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
     A__				=   load_dataset('''glue'''		,							'''mrpc'''      )
     A__				=   evaluate.load('''glue'''		,							'''mrpc'''      )

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          A__				=   tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=__a		,							max_length=__a      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          A__				=   datasets.map(
              __a		,							batched=__a		,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,							)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     A__				=   tokenized_datasets.rename_column('''label'''		,							'''labels'''      )

     # If the batch size is too big we use gradient accumulation
     A__				=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__				=   batch_size // MAX_GPU_BATCH_SIZE
          A__				=   MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__				=   128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A__				=   16
          elif accelerator.mixed_precision != "no":
               A__				=   8
          else:
               A__				=   None

          return tokenizer.pad(
              __a		,							padding='''longest'''		,							max_length=__a		,							pad_to_multiple_of=__a		,							return_tensors='''pt'''		,							)

     # Instantiate dataloaders.
     A__				=   DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )
     A__				=   DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A__				=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,							return_dict=__a      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     A__				=   model.to(accelerator.device      )

     # Instantiate optimizer
     A__				=   AdamW(params=model.parameters()		,							lr=__a      )

     # Instantiate scheduler
     A__				=   get_linear_schedule_with_warmup(
         optimizer=__a		,							num_warmup_steps=100		,							num_training_steps=(len(__a      ) * num_epochs) // gradient_accumulation_steps		,							)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     A__	,     A__	,     A__	,     A__	,     A__				=   accelerator.prepare(
         __a		,							__a		,							__a		,							__a		,							__a      )

     # We need to keep track of how many total steps we have iterated over
     A__				=   0
     # We also need to keep track of the stating epoch so files are named properly
     A__				=   0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''      )
               accelerator.load_state(args.resume_from_checkpoint      )
               A__				=   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               A__				=   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               A__				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__				=   os.path.splitext(__a      )[0]

          if "epoch" in training_difference:
               A__				=   int(training_difference.replace('''epoch_'''		,							''''''      )      ) + 1
               A__				=   None
          else:
               A__				=   int(training_difference.replace('''step_'''		,							''''''      )      )
               A__				=   resume_step // len(__a      )
               resume_step -= starting_epoch * len(__a      )

    # Now we train the model
     for epoch in range(__a		,							__a      ):
          model.train()
          if args.with_tracking:
               A__				=   0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A__				=   accelerator.skip_first_batches(__a		,							__a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A__				=   train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               A__				=   model(**__a      )
               A__				=   outputs.loss
               A__				=   loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a		,							__a      ):
                    A__				=   f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A__				=   os.path.join(args.output_dir		,							__a      )
                         accelerator.save_state(__a      )

          model.eval()
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    A__				=   model(**__a      )
               A__				=   outputs.logits.argmax(dim=-1      )
               A__	,     A__				=   accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
               metric.add_batch(
                   predictions=__a		,							references=__a		,							)

          A__				=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,							__a      )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__a      ),
                       '''epoch''': epoch,
                   }		,							step=__a		,							)

          if checkpointing_steps == "epoch":
               A__				=   f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    A__				=   os.path.join(args.output_dir		,							__a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def   a_	(			):
     A__				=   argparse.ArgumentParser(description='''Simple example of training script.'''      )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__a		,							default=__a		,							choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,							help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.'''		,							)
     parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''      )
     parser.add_argument(
         '''--checkpointing_steps'''		,							type=__a		,							default=__a		,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,							)
     parser.add_argument(
         '''--resume_from_checkpoint'''		,							type=__a		,							default=__a		,							help='''If the training should continue from a checkpoint folder.'''		,							)
     parser.add_argument(
         '''--with_tracking'''		,							action='''store_true'''		,							help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,							)
     parser.add_argument(
         '''--output_dir'''		,							type=__a		,							default='''.'''		,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,							)
     parser.add_argument(
         '''--project_dir'''		,							type=__a		,							default='''logs'''		,							help='''Location on where to store experiment tracking logs` and relevent project information'''		,							)
     A__				=   parser.parse_args()
     A__				=   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__a		,							__a      )


if __name__ == "__main__":
 main()



