
'''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				:						Union[str, Any]												= logging.get_logger()






@dataclass
class 				lowerCAmelCase__     :
					lowerCAmelCase_       =  4_2
					lowerCAmelCase_       =  field(default_factory=UpperCamelCase_  )
					lowerCAmelCase_       =  field(default_factory=UpperCamelCase_  )
					lowerCAmelCase_       =  field(default_factory=UpperCamelCase_  )






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										lowercase_			:  Tuple									= len(list(m.modules()	)	) == 1 or isinstance(__UpperCamelCase       ,							nn.Convad	) or isinstance(__UpperCamelCase       ,							nn.BatchNormad	)
										if has_not_submodules:
															self.traced.append(__UpperCamelCase	)
															lowercase_			:  Union[str, Any]									= m






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





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






					@property
					def 			_snake_case					(     self	):


										"""simple docstring"""





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







class 				lowerCAmelCase__     (						nn.Module  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										super().__init__()

										lowercase_			:  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}'''
															lowercase_			:  Any									= len(__UpperCamelCase	) + 1
															feature_blocks.append((F'''res{block_index}''', v)	)

										lowercase_			:  Tuple									= nn.ModuleDict(__UpperCamelCase	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





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







class 				lowerCAmelCase__     (						UpperCamelCase_  ):






					def 			_snake_case					(     self	):


										"""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       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]							):



					"""simple docstring"""





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

					with torch.no_grad():
										lowercase_			:  str									= from_model.eval()
										lowercase_			:  int									= our_model.eval()

										lowercase_			:  Optional[int]									= torch.randn((1, 3, 32, 32)							)
										# trace both
										lowercase_			:  int									= Tracker(snake_case_							)
										lowercase_			:  Optional[int]									= dest_tracker(snake_case_							).parametrized

										pprint(dest_tracker.nameamodule							)
										lowercase_			:  Optional[int]									= Tracker(snake_case_							)
										lowercase_			:  Any									= src_tracker(snake_case_							).parametrized

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

					lowercase_			:  Optional[int]									= {}

					lowercase_			:  int									= to_params_dict(snake_case_							)
					lowercase_			:  int									= to_params_dict(snake_case_							)

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,   dst_state_dict.items()							):
										lowercase_			:  int									= 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:
										lowercase_			:  str									= '''classifier.1.weight'''
										lowercase_			:  Union[str, Any]									= '''classifier.1.bias'''

					return from_to_ours_keys
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Tuple   ,   __SCREAMING_SNAKE_CASE      :				Dict = None   ,   __SCREAMING_SNAKE_CASE      :				List[str] = True							):



					"""simple docstring"""





					lowercase_			:  Tuple									= '''imagenet-1k-id2label.json'''
					lowercase_			:  Any									= 1000

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

					lowercase_			:  int									= idalabel
					lowercase_			:  Tuple									= {v: k for k, v in idalabel.items()}

					lowercase_			:  str									= partial(snake_case_   ,   num_labels=snake_case_   ,   idalabel=snake_case_   ,   labelaid=snake_case_							)

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

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

					lowercase_			:  Tuple									= get_from_to_our_keys(snake_case_							)

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

										lowercase_			:  List[str]									= {}

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

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

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

							_lowercase				:						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

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_lowercase				:						Tuple												= 3_2
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Accelerator   ,   __SCREAMING_SNAKE_CASE      :				int = 16							):



						"""simple docstring"""





						lowercase_			:  int									= AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_			:  int									= load_dataset('''glue'''   ,   '''mrpc'''							)

						def tokenize_function(__SCREAMING_SNAKE_CASE      :				Optional[int]							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_			:  Union[str, Any]									= tokenizer(examples['''sentence1''']   ,   examples['''sentence2''']   ,   truncation=__SCREAMING_SNAKE_CASE   ,   max_length=__SCREAMING_SNAKE_CASE							)
												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():
												lowercase_			:  Optional[Any]									= datasets.map(
												    __SCREAMING_SNAKE_CASE   ,   batched=__SCREAMING_SNAKE_CASE   ,   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
						lowercase_			:  Any									= tokenized_datasets.rename_column('''label'''   ,   '''labels'''							)

						def collate_fn(__SCREAMING_SNAKE_CASE      :				List[Any]							):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase_			:  List[str]									= 16
												elif accelerator.mixed_precision != "no":
																		lowercase_			:  List[Any]									= 8
												else:
																		lowercase_			:  List[Any]									= None

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

						# Instantiate dataloaders.
						lowercase_			:  List[str]									= DataLoader(
						    tokenized_datasets['''train''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= DataLoader(
						    tokenized_datasets['''validation''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase				:						Tuple												= mocked_dataloaders  # noqa: F811
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,   __SCREAMING_SNAKE_CASE							) == "1":
												lowercase_			:  Dict									= 2
						# Initialize accelerator
						lowercase_			:  Any									= Accelerator(cpu=args.cpu   ,   mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_			:  Optional[Any]									= config['''lr''']
						lowercase_			:  Optional[Any]									= int(config['''num_epochs''']							)
						lowercase_			:  str									= int(config['''seed''']							)
						lowercase_			:  Optional[Any]									= int(config['''batch_size''']							)

						lowercase_			:  int									= evaluate.load('''glue'''   ,   '''mrpc'''							)

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(__SCREAMING_SNAKE_CASE							)							)
						def inner_training_loop(__SCREAMING_SNAKE_CASE      :				Any							):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												lowercase_			:  Union[str, Any]									= observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												lowercase_			:  Tuple									= num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(__SCREAMING_SNAKE_CASE							)

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

												# 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).
												lowercase_			:  Optional[int]									= model.to(accelerator.device							)

												# Instantiate optimizer
												lowercase_			:  int									= AdamW(params=model.parameters()   ,   lr=__SCREAMING_SNAKE_CASE							)
												lowercase_		,				lowercase_			:  List[str]									= get_dataloaders(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Instantiate scheduler
												lowercase_			:  str									= get_linear_schedule_with_warmup(
												    optimizer=__SCREAMING_SNAKE_CASE   ,   num_warmup_steps=100   ,   num_training_steps=(len(__SCREAMING_SNAKE_CASE							) * num_epochs)   ,   )

												# 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.
												lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  Any									= accelerator.prepare(
												    __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Now we train the model
												for epoch in range(__SCREAMING_SNAKE_CASE							):
																		model.train()
																		for step, batch in enumerate(__SCREAMING_SNAKE_CASE							):
																								# And perform gradient accumulation
																								with accelerator.accumulate(__SCREAMING_SNAKE_CASE							):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device							)
																														lowercase_			:  List[str]									= model(**__SCREAMING_SNAKE_CASE							)
																														lowercase_			:  Any									= outputs.loss
																														accelerator.backward(__SCREAMING_SNAKE_CASE							)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

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

																		lowercase_			:  Union[str, Any]									= metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F'''epoch {epoch}:'''   ,   __SCREAMING_SNAKE_CASE							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  int									= argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''   ,   type=__SCREAMING_SNAKE_CASE   ,   default=__SCREAMING_SNAKE_CASE   ,   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.'''							)
						lowercase_			:  int									= parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						lowercase_			:  Union[str, Any]									= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
						training_function(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							main()



