'''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: Tuple          =   16
__a: List[str]          =   32
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return int(x / 2**20     )



class 			UpperCAmelCase  :





       '''simple docstring'''



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


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


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16		,     UpperCAmelCase = "bert-base-cased"		,     UpperCAmelCase = 320		,     UpperCAmelCase = 160		,     ):
 lowercase__				:    Any  =					AutoTokenizer.from_pretrained(lowerCamelCase__     )
 lowercase__				:    List[str]  =					load_dataset(
     '''glue'''		,     '''mrpc'''		,     split={'''train''': F"""train[:{n_train}]""", '''validation''': F"""validation[:{n_val}]"""}     )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 lowercase__				:    str  =					datasets.map(
     lowerCamelCase__		,     batched=lowerCamelCase__		,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,     load_from_cache_file=lowerCamelCase__     )

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

 def collate_fn(UpperCAmelCase     ):
  # 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(lowerCamelCase__		,     padding='''max_length'''		,     max_length=128		,     return_tensors='''pt'''     )
  return tokenizer.pad(lowerCamelCase__		,     padding='''longest'''		,     return_tensors='''pt'''     )

 # Instantiate dataloaders.
 lowercase__				:    List[str]  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=lowerCamelCase__		,     collate_fn=lowerCamelCase__		,     batch_size=lowerCamelCase__     )
 lowercase__				:    Dict  =					DataLoader(
     tokenized_datasets['''validation''']		,     shuffle=lowerCamelCase__		,     collate_fn=lowerCamelCase__		,     batch_size=lowerCamelCase__     )

 return train_dataloader, eval_dataloader
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    str  =					Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    Union[str, Any]  =					config["""lr"""]
 lowercase__				:    Optional[Any]  =					int(config['''num_epochs''']     )
 lowercase__				:    List[Any]  =					int(config['''seed''']     )
 lowercase__				:    List[str]  =					int(config['''batch_size''']     )
 lowercase__				:    List[Any]  =					args.model_name_or_path

 set_seed(lowerCamelCase__     )
 lowercase__				:    str  =					get_dataloaders(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     args.n_train		,     args.n_val     )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowercase__				:    Dict  =					AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__		,     return_dict=lowerCamelCase__     )

 # Instantiate optimizer
 lowercase__				:    Union[str, Any]  =					(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 lowercase__				:    Dict  =					optimizer_cls(params=model.parameters()		,     lr=lowerCamelCase__     )

 if accelerator.state.deepspeed_plugin is not None:
  lowercase__				:    Optional[int]  =					accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  lowercase__				:    Union[str, Any]  =					1
 lowercase__				:    Dict  =					(len(lowerCamelCase__     ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  lowercase__				:    int  =					get_linear_schedule_with_warmup(
      optimizer=lowerCamelCase__		,     num_warmup_steps=0		,     num_training_steps=lowerCamelCase__		,     )
 else:
  lowercase__				:    str  =					DummyScheduler(lowerCamelCase__		,     total_num_steps=lowerCamelCase__		,     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.
 lowercase__				:    Tuple  =					accelerator.prepare(
     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__     )

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

 # Now we train the model
 lowercase__				:    List[Any]  =					{}
 for epoch in range(lowerCamelCase__		,     lowerCamelCase__     ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowerCamelCase__     ):
    lowercase__				:    List[str]  =					model(**lowerCamelCase__     )
    lowercase__				:    List[Any]  =					outputs.loss
    lowercase__				:    Dict  =					loss / gradient_accumulation_steps
    accelerator.backward(lowerCamelCase__     )
    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     )     )     )
  lowercase__				:    List[str]  =					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(lowerCamelCase__		,     lowerCamelCase__     )
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''     )
 parser.add_argument(
     '''--model_name_or_path'''		,     type=lowerCamelCase__		,     default='''bert-base-cased'''		,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''		,     required=lowerCamelCase__		,     )
 parser.add_argument(
     '''--output_dir'''		,     type=lowerCamelCase__		,     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=lowerCamelCase__		,     default=lowerCamelCase__		,     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=lowerCamelCase__		,     default=320		,     help='''Number of training examples to use.'''		,     )
 parser.add_argument(
     '''--n_val'''		,     type=lowerCamelCase__		,     default=160		,     help='''Number of validation examples to use.'''		,     )
 parser.add_argument(
     '''--num_epochs'''		,     type=lowerCamelCase__		,     default=1		,     help='''Number of train epochs.'''		,     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[Any]  =					{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
 training_function(lowerCamelCase__		,     lowerCamelCase__     )


if __name__ == "__main__":
  main()






'''simple docstring'''




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     VideoMAEConfig()

			set_architecture_configs(lowerCamelCase__						,      lowerCamelCase__    )

			if "finetuned" not in model_name:
						A_	:						Dict	=     False

			if "finetuned" in model_name:
						A_	:						List[Any]	=     """huggingface/label-files"""
						if "kinetics" in model_name:
									A_	:						Dict	=     4_00
									A_	:						List[str]	=     """kinetics400-id2label.json"""
						elif "ssv2" in model_name:
									A_	:						Tuple	=     1_74
									A_	:						str	=     """something-something-v2-id2label.json"""
						else:
									raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""    )
						A_	:						Dict	=     json.load(open(hf_hub_download(lowerCamelCase__						,      lowerCamelCase__						,      repo_type="""dataset"""    )						,      """r"""    )    )
						A_	:						List[str]	=     {int(lowerCamelCase__    ): v for k, v in idalabel.items()}
						A_	:						Optional[Any]	=     idalabel
						A_	:						Union[str, Any]	=     {v: k for k, v in idalabel.items()}

			return config



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if "small" in model_name:
						A_	:						int	=     3_84
						A_	:						Union[str, Any]	=     15_36
						A_	:						List[str]	=     12
						A_	:						Optional[int]	=     16
						A_	:						Any	=     12
						A_	:						int	=     3
						A_	:						Optional[Any]	=     1_92
						A_	:						Union[str, Any]	=     7_68
			elif "large" in model_name:
						A_	:						List[Any]	=     10_24
						A_	:						Optional[Any]	=     40_96
						A_	:						Optional[Any]	=     24
						A_	:						List[str]	=     16
						A_	:						Any	=     12
						A_	:						str	=     8
						A_	:						str	=     5_12
						A_	:						int	=     20_48
			elif "huge" in model_name:
						A_	:						Optional[Any]	=     12_80
						A_	:						str	=     51_20
						A_	:						str	=     32
						A_	:						int	=     16
						A_	:						Any	=     12
						A_	:						Union[str, Any]	=     8
						A_	:						Dict	=     6_40
						A_	:						Optional[Any]	=     25_60
			elif "base" not in model_name:
						raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if "encoder." in name:
						A_	:						List[Any]	=     name.replace("""encoder."""						,      """"""    )
			if "cls_token" in name:
						A_	:						List[str]	=     name.replace("""cls_token"""						,      """videomae.embeddings.cls_token"""    )
			if "decoder_pos_embed" in name:
						A_	:						Tuple	=     name.replace("""decoder_pos_embed"""						,      """decoder.decoder_pos_embed"""    )
			if "pos_embed" in name and "decoder" not in name:
						A_	:						int	=     name.replace("""pos_embed"""						,      """videomae.embeddings.position_embeddings"""    )
			if "patch_embed.proj" in name:
						A_	:						Optional[Any]	=     name.replace("""patch_embed.proj"""						,      """videomae.embeddings.patch_embeddings.projection"""    )
			if "patch_embed.norm" in name:
						A_	:						Dict	=     name.replace("""patch_embed.norm"""						,      """videomae.embeddings.norm"""    )
			if "decoder.blocks" in name:
						A_	:						List[str]	=     name.replace("""decoder.blocks"""						,      """decoder.decoder_layers"""    )
			if "blocks" in name:
						A_	:						List[str]	=     name.replace("""blocks"""						,      """videomae.encoder.layer"""    )
			if "attn.proj" in name:
						A_	:						str	=     name.replace("""attn.proj"""						,      """attention.output.dense"""    )
			if "attn" in name and "bias" not in name:
						A_	:						str	=     name.replace("""attn"""						,      """attention.self"""    )
			if "attn" in name:
						A_	:						Union[str, Any]	=     name.replace("""attn"""						,      """attention.attention"""    )
			if "norm1" in name:
						A_	:						Any	=     name.replace("""norm1"""						,      """layernorm_before"""    )
			if "norm2" in name:
						A_	:						List[str]	=     name.replace("""norm2"""						,      """layernorm_after"""    )
			if "mlp.fc1" in name:
						A_	:						Dict	=     name.replace("""mlp.fc1"""						,      """intermediate.dense"""    )
			if "mlp.fc2" in name:
						A_	:						List[str]	=     name.replace("""mlp.fc2"""						,      """output.dense"""    )
			if "decoder_embed" in name:
						A_	:						Optional[Any]	=     name.replace("""decoder_embed"""						,      """decoder.decoder_embed"""    )
			if "decoder_norm" in name:
						A_	:						Tuple	=     name.replace("""decoder_norm"""						,      """decoder.decoder_norm"""    )
			if "decoder_pred" in name:
						A_	:						Tuple	=     name.replace("""decoder_pred"""						,      """decoder.decoder_pred"""    )
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						A_	:						Dict	=     name.replace("""norm.weight"""						,      """videomae.layernorm.weight"""    )
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						A_	:						List[str]	=     name.replace("""norm.bias"""						,      """videomae.layernorm.bias"""    )
			if "head" in name and "decoder" not in name:
						A_	:						Optional[Any]	=     name.replace("""head"""						,      """classifier"""    )

			return name



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						A_	:						str	=     orig_state_dict.pop(lowerCamelCase__    )

						if key.startswith("""encoder."""    ):
									A_	:						Tuple	=     key.replace("""encoder."""						,      """"""    )

						if "qkv" in key:
									A_	:						Optional[int]	=     key.split("""."""    )
									if key.startswith("""decoder.blocks"""    ):
												A_	:						Union[str, Any]	=     config.decoder_hidden_size
												A_	:						Any	=     int(key_split[2]    )
												A_	:						int	=     """decoder.decoder_layers."""
												if "weight" in key:
															A_	:						Optional[Any]	=     val[:dim, :]
															A_	:						Any	=     val[dim : dim * 2, :]
															A_	:						Dict	=     val[-dim:, :]
									else:
												A_	:						List[Any]	=     config.hidden_size
												A_	:						List[Any]	=     int(key_split[1]    )
												A_	:						int	=     """videomae.encoder.layer."""
												if "weight" in key:
															A_	:						Any	=     val[:dim, :]
															A_	:						Union[str, Any]	=     val[dim : dim * 2, :]
															A_	:						List[str]	=     val[-dim:, :]
						else:
									A_	:						Union[str, Any]	=     val

			return orig_state_dict



def 	a			( ):
			'''simple docstring'''





			A_	:						List[Any]	=     hf_hub_download(
			    repo_id="""hf-internal-testing/spaghetti-video"""						,      filename="""eating_spaghetti.npy"""						,      repo_type="""dataset"""    )
			A_	:						Optional[Any]	=     np.load(lowerCamelCase__    )
			return list(lowerCamelCase__    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     get_videomae_config(lowerCamelCase__    )

			if "finetuned" in model_name:
						A_	:						List[str]	=     VideoMAEForVideoClassification(lowerCamelCase__    )
			else:
						A_	:						Optional[Any]	=     VideoMAEForPreTraining(lowerCamelCase__    )

			# download original checkpoint, hosted on Google Drive
			A_	:						Optional[Any]	=     """pytorch_model.bin"""
			gdown.cached_download(lowerCamelCase__						,      lowerCamelCase__						,      quiet=lowerCamelCase__    )
			A_	:						Any	=     torch.load(lowerCamelCase__						,      map_location="""cpu"""    )
			if "model" in files:
						A_	:						Any	=     files["""model"""]
			else:
						A_	:						Dict	=     files["""module"""]
			A_	:						Any	=     convert_state_dict(lowerCamelCase__						,      lowerCamelCase__    )

			model.load_state_dict(lowerCamelCase__    )
			model.eval()

			# verify model on basic input
			A_	:						int	=     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,      image_std=[0.5, 0.5, 0.5]    )
			A_	:						Union[str, Any]	=     prepare_video()
			A_	:						str	=     image_processor(lowerCamelCase__						,      return_tensors="""pt"""    )

			if "finetuned" not in model_name:
						A_	:						List[str]	=     hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""						,      filename="""bool_masked_pos.pt"""    )
						A_	:						Optional[Any]	=     torch.load(lowerCamelCase__    )

			A_	:						Dict	=     model(**lowerCamelCase__    )
			A_	:						List[Any]	=     outputs.logits

			A_	:						Any	=     [
			    """videomae-small-finetuned-kinetics""",
			    """videomae-small-finetuned-ssv2""",
			    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
			    """videomae-base-short""",
			    """videomae-base-short-finetuned-kinetics""",
			    """videomae-base""",
			    """videomae-base-finetuned-kinetics""",
			    """videomae-large""",
			    """videomae-large-finetuned-kinetics""",
			    """videomae-huge-finetuned-kinetics""",
			    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
			    """videomae-base-short-ssv2""",
			    """videomae-base-short-finetuned-ssv2""",
			    """videomae-base-ssv2""",
			    """videomae-base-finetuned-ssv2""",
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "videomae-small-finetuned-kinetics":
						A_	:						str	=     torch.Size([1, 4_00]    )
						A_	:						Optional[Any]	=     torch.tensor([-0.9_291, -0.4_061, -0.9_307]    )
			elif model_name == "videomae-small-finetuned-ssv2":
						A_	:						str	=     torch.Size([1, 1_74]    )
						A_	:						Union[str, Any]	=     torch.tensor([0.2_671, -0.4_689, -0.8_235]    )
			elif model_name == "videomae-base":
						A_	:						Tuple	=     torch.Size([1, 14_08, 15_36]    )
						A_	:						List[str]	=     torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]    )
			elif model_name == "videomae-base-short":
						A_	:						Dict	=     torch.Size([1, 14_08, 15_36]    )
						A_	:						List[str]	=     torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]    )
						# we verified the loss both for normalized and unnormalized targets for this one
						A_	:						List[Any]	=     torch.tensor([0.5_142]    ) if config.norm_pix_loss else torch.tensor([0.6_469]    )
			elif model_name == "videomae-large":
						A_	:						str	=     torch.Size([1, 14_08, 15_36]    )
						A_	:						Dict	=     torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]    )
			elif model_name == "videomae-large-finetuned-kinetics":
						A_	:						int	=     torch.Size([1, 4_00]    )
						A_	:						Optional[Any]	=     torch.tensor([0.0_771, 0.0_011, -0.3_625]    )
			elif model_name == "videomae-huge-finetuned-kinetics":
						A_	:						Union[str, Any]	=     torch.Size([1, 4_00]    )
						A_	:						Optional[int]	=     torch.tensor([0.2_433, 0.1_632, -0.4_894]    )
			elif model_name == "videomae-base-short-finetuned-kinetics":
						A_	:						List[Any]	=     torch.Size([1, 4_00]    )
						A_	:						Optional[Any]	=     torch.tensor([0.6_588, 0.0_990, -0.2_493]    )
			elif model_name == "videomae-base-finetuned-kinetics":
						A_	:						Union[str, Any]	=     torch.Size([1, 4_00]    )
						A_	:						Tuple	=     torch.tensor([0.3_669, -0.0_688, -0.2_421]    )
			elif model_name == "videomae-base-short-ssv2":
						A_	:						Optional[Any]	=     torch.Size([1, 14_08, 15_36]    )
						A_	:						List[Any]	=     torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]    )
			elif model_name == "videomae-base-short-finetuned-ssv2":
						A_	:						Any	=     torch.Size([1, 1_74]    )
						A_	:						Any	=     torch.tensor([-0.0_537, -0.1_539, -0.3_266]    )
			elif model_name == "videomae-base-ssv2":
						A_	:						Dict	=     torch.Size([1, 14_08, 15_36]    )
						A_	:						Dict	=     torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]    )
			elif model_name == "videomae-base-finetuned-ssv2":
						A_	:						Any	=     torch.Size([1, 1_74]    )
						A_	:						str	=     torch.tensor([0.1_961, -0.8_337, -0.6_389]    )
			else:
						raise ValueError(f'Model name not supported. Should be one of {model_names}'    )

			# verify logits
			assert logits.shape == expected_shape
			if "finetuned" in model_name:
						assert torch.allclose(logits[0, :3]						,      lowerCamelCase__						,      atol=1E-4    )
			else:
						print("""Logits:"""						,      logits[0, :3, :3]    )
						assert torch.allclose(logits[0, :3, :3]						,      lowerCamelCase__						,      atol=1E-4    )
			print("""Logits ok!"""    )

			# verify loss, if applicable
			if model_name == "videomae-base-short":
						A_	:						Optional[int]	=     outputs.loss
						assert torch.allclose(lowerCamelCase__						,      lowerCamelCase__						,      atol=1E-4    )
						print("""Loss ok!"""    )

			if pytorch_dump_folder_path is not None:
						print(f'Saving model and image processor to {pytorch_dump_folder_path}'    )
						image_processor.save_pretrained(lowerCamelCase__    )
						model.save_pretrained(lowerCamelCase__    )

			if push_to_hub:
						print("""Pushing to the hub..."""    )
						model.push_to_hub(lowerCamelCase__						,      organization="""nielsr"""    )


if __name__ == "__main__":
						lowerCamelCase							:Tuple				    =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
						    type=str,
						    help=(
						        '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
						        ''' download link.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						lowerCamelCase							:Union[str, Any]				    =					parser.parse_args()
						convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)