


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=	logging.get_logger(__name__)


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Union[str, Any]		     =					'''huggingface/label-files'''
  A_      :				Tuple		     =					'''imagenet-1k-id2label.json'''
  A_      :				int		     =					json.load(open(hf_hub_download(snake_case_ ,			snake_case_ ,			repo_type='''dataset'''       ) ,			'''r'''       )       )
  A_      :				List[str]		     =					{int(snake_case_       ): v for k, v in idalabel.items()}
  A_      :				Union[str, Any]		     =					{v: k for k, v in idalabel.items()}

  A_      :				int		     =					'''std_conv''' if '''bit''' in model_name else False

  # note that when using BiT as backbone for ViT-hybrid checkpoints,
  # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
  # config.conv_layer = "std_conv_same"
  A_      :				Any		     =					BitConfig(
      conv_layer=snake_case_ ,			num_labels=1_000 ,			idalabel=snake_case_ ,			labelaid=snake_case_ ,			)

  return config


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):

  if "stem.conv" in name:
    A_      :				Dict		     =					name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''       )
  if "blocks" in name:
    A_      :				Optional[Any]		     =					name.replace('''blocks''' ,			'''layers'''       )
  if "head.fc" in name:
    A_      :				Any		     =					name.replace('''head.fc''' ,			'''classifier.1'''       )
  if name.startswith('''norm'''       ):
    A_      :				Union[str, Any]		     =					'''bit.''' + name
  if "bit" not in name and "classifier" not in name:
    A_      :				Dict		     =					'''bit.encoder.''' + name

  return name


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Any		     =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  A_      :				Any		     =					Image.open(requests.get(snake_case_ ,			stream=snake_case_       ).raw       )
  return im


@torch.no_grad()
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False       ):
  A_      :				str		     =					get_config(snake_case_       )

  # load original model from timm
  A_      :				Optional[Any]		     =					create_model(snake_case_ ,			pretrained=snake_case_       )
  timm_model.eval()

  # load state_dict of original model
  A_      :				Optional[Any]		     =					timm_model.state_dict()
  for key in state_dict.copy().keys():
    A_      :				Optional[int]		     =					state_dict.pop(snake_case_       )
    A_      :				Any		     =					val.squeeze() if '''head''' in key else val

  # load HuggingFace model
  A_      :				Dict		     =					BitForImageClassification(snake_case_       )
  model.eval()
  model.load_state_dict(snake_case_       )

  # create image processor
  A_      :				List[Any]		     =					create_transform(**resolve_data_config({} ,			model=snake_case_       )       )
  A_      :				Union[str, Any]		     =					transform.transforms

  A_      :				Any		     =					{
      '''bilinear''': PILImageResampling.BILINEAR,
      '''bicubic''': PILImageResampling.BICUBIC,
      '''nearest''': PILImageResampling.NEAREST,
  }

  A_      :				List[str]		     =					BitImageProcessor(
      do_resize=snake_case_ ,			size={'''shortest_edge''': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=snake_case_ ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,			do_normalize=snake_case_ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

  A_      :				Tuple		     =					prepare_img()
  A_      :				List[str]		     =					transform(snake_case_       ).unsqueeze(0       )
  A_      :				Any		     =					processor(snake_case_ ,			return_tensors='''pt'''       ).pixel_values

  # verify pixel values
  assert torch.allclose(snake_case_ ,			snake_case_       )

  # verify logits
  with torch.no_grad():
    A_      :				Dict		     =					model(snake_case_       )
    A_      :				Optional[int]		     =					outputs.logits

  print('''Logits:''' ,			logits[0, :3]       )
  print('''Predicted class:''' ,			model.config.idalabel[logits.argmax(-1       ).item()]       )
  A_      :				Optional[int]		     =					timm_model(snake_case_       )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(snake_case_ ,			outputs.logits ,			atol=1e-3       )
  print('''Looks ok!'''       )

  if pytorch_dump_folder_path is not None:
    Path(snake_case_       ).mkdir(exist_ok=snake_case_       )
    print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(snake_case_       )
    processor.save_pretrained(snake_case_       )

  if push_to_hub:
    print(f'''Pushing model {model_name} and processor to the hub'''       )
    model.push_to_hub(f'''ybelkada/{model_name}'''       )
    processor.push_to_hub(f'''ybelkada/{model_name}'''       )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""resnetv2_50x1_bitm""",
         type=str,
         help="""Name of the BiT timm model you\'d like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
         help="""Whether to push the model to the hub.""",
     )

     UpperCamelCase								=	parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

import argparse
import gc
import json
import os

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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_A			:      Optional[Any]					      =				16
_A			:      Union[str, Any]					      =				32







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     str:






 '''simple docstring'''

 return int(x / 2**20      )







class 				_lowercase     :


 '''simple docstring'''






 def __enter__(      self   :	List[Any]							)  ->    int:
  gc.collect()
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  __lowerCAmelCase											=						torch.cuda.memory_allocated()
  return self





 def __exit__(      self   :	Tuple      ,       *SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Optional[Any]:
  gc.collect()
  torch.cuda.empty_cache()
  __lowerCAmelCase											=						torch.cuda.memory_allocated()
  __lowerCAmelCase											=						torch.cuda.max_memory_allocated()
  __lowerCAmelCase											=						bamb(self.end - self.begin							)
  __lowerCAmelCase											=						bamb(self.peak - self.begin							)
  # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def      UpperCamelCase_						(					snake_case_       :       Accelerator						,		snake_case_       :       int = 16						,		snake_case_       :       str = "bert-base-cased"						,		snake_case_       :       int = 3_20						,		snake_case_       :       int = 1_60						,		)					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained(snake_case_      )
 __lowerCAmelCase											=						load_dataset(
     """glue"""						,		"""mrpc"""						,		split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""}      )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""idx""", """sentence1""", """sentence2"""]						,		load_from_cache_file=snake_case_      )

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

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(
     tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=snake_case_      )
 __lowerCAmelCase											=						DataLoader(
     tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=snake_case_      )

 return train_dataloader, eval_dataloader







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase											=						config["""lr"""]
 __lowerCAmelCase											=						int(config["""num_epochs"""]      )
 __lowerCAmelCase											=						int(config["""seed"""]      )
 __lowerCAmelCase											=						int(config["""batch_size"""]      )
 __lowerCAmelCase											=						args.model_name_or_path

 set_seed(snake_case_      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						get_dataloaders(snake_case_						,		snake_case_						,		snake_case_						,		args.n_train						,		args.n_val      )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCAmelCase											=						AutoModelForSequenceClassification.from_pretrained(snake_case_						,		return_dict=snake_case_      )

 # Instantiate optimizer
 __lowerCAmelCase											=						(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 __lowerCAmelCase											=						optimizer_cls(params=model.parameters()						,		lr=snake_case_      )

 if accelerator.state.deepspeed_plugin is not None:
  __lowerCAmelCase											=						accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  __lowerCAmelCase											=						1
 __lowerCAmelCase											=						(len(snake_case_      ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __lowerCAmelCase											=						get_linear_schedule_with_warmup(
      optimizer=snake_case_						,		num_warmup_steps=0						,		num_training_steps=snake_case_						,		)
 else:
  __lowerCAmelCase											=						DummyScheduler(snake_case_						,		total_num_steps=snake_case_						,		warmup_num_steps=0      )

 # 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.
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						accelerator.prepare(
     snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_      )

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

 # Now we train the model
 __lowerCAmelCase											=						{}
 for epoch in range(snake_case_						,		snake_case_      ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(snake_case_      ):
    __lowerCAmelCase											=						model(**snake_case_      )
    __lowerCAmelCase											=						outputs.loss
    __lowerCAmelCase											=						loss / gradient_accumulation_steps
    accelerator.backward(snake_case_      )
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

    overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin      )      )      )
  accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used      )      )
  accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked      )      )
  accelerator.print(
      """Total Peak Memory consumed during the train (max): {}""".format(
          tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
  __lowerCAmelCase											=						tracemalloc.peaked + bamb(tracemalloc.begin      )
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir						,		"""peak_memory_utilization.json"""      )						,		"""w"""      ) as f:
   json.dump(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""      )
 parser.add_argument(
     """--model_name_or_path"""						,		type=snake_case_						,		default="""bert-base-cased"""						,		help="""Path to pretrained model or model identifier from huggingface.co/models."""						,		required=snake_case_						,		)
 parser.add_argument(
     """--output_dir"""						,		type=snake_case_						,		default="""."""						,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,		)
 parser.add_argument(
     """--peak_memory_upper_bound"""						,		type=snake_case_						,		default=snake_case_						,		help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""						,		)
 parser.add_argument(
     """--n_train"""						,		type=snake_case_						,		default=3_20						,		help="""Number of training examples to use."""						,		)
 parser.add_argument(
     """--n_val"""						,		type=snake_case_						,		default=1_60						,		help="""Number of validation examples to use."""						,		)
 parser.add_argument(
     """--num_epochs"""						,		type=snake_case_						,		default=1						,		help="""Number of train epochs."""						,		)
 __lowerCAmelCase											=						parser.parse_args()
 __lowerCAmelCase											=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
 training_function(snake_case_						,		snake_case_      )


if __name__ == "__main__":
 main()






