


'''simple docstring'''


import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


lowerCamelCase__    	=     "https://openaipublic.azureedge.net/jukebox/models/"
lowerCamelCase__    	=     {
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}






def  __lowerCAmelCase (__lowerCAmelCase   )     ->   Union[str, Any]:
		if key.endswith(".model.1.bias"   ) and len(key.split("."   )   ) > 10:
				_UpperCAmelCase		:       List[Any]								=      key.replace(".model.1.bias"      , ".conv1d_1.bias"   )
		elif key.endswith(".model.1.weight"   ) and len(key.split("."   )   ) > 10:
				_UpperCAmelCase		:       List[str]								=      key.replace(".model.1.weight"      , ".conv1d_1.weight"   )
		elif key.endswith(".model.3.bias"   ) and len(key.split("."   )   ) > 10:
				_UpperCAmelCase		:       List[Any]								=      key.replace(".model.3.bias"      , ".conv1d_2.bias"   )
		elif key.endswith(".model.3.weight"   ) and len(key.split("."   )   ) > 10:
				_UpperCAmelCase		:       Dict								=      key.replace(".model.3.weight"      , ".conv1d_2.weight"   )

		if "conditioner_blocks.0." in key:
				_UpperCAmelCase		:       Any								=      key.replace("conditioner_blocks.0"      , "conditioner_blocks"   )

		if "prime_prior" in key:
				_UpperCAmelCase		:       str								=      key.replace("prime_prior"      , "encoder"   )

		if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
				_UpperCAmelCase		:       List[str]								=      key.replace(".emb."      , "."   )

		if key.endswith("k"   ):  # replace vqvae.X.k with vqvae.X.codebook
				return key.replace(".k"      , ".codebook"   )
		if "y_emb." in key:
				return key.replace("y_emb."      , "metadata_embedding."   )

		if "x_emb.emb." in key:
				_UpperCAmelCase		:       List[Any]								=      key.replace("0.x_emb.emb"      , "embed_tokens"   )

		if "prime_state_ln" in key:
				return key.replace("prime_state_ln"      , "encoder.final_layer_norm"   )
		if ".ln" in key:
				return key.replace(".ln"      , ".layer_norm"   )
		if "_ln" in key:
				return key.replace("_ln"      , "_layer_norm"   )

		if "prime_state_proj" in key:
				return key.replace("prime_state_proj"      , "encoder.proj_in"   )
		if "prime_x_out" in key:
				return key.replace("prime_x_out"      , "encoder.lm_head"   )
		if "prior.x_out" in key:
				return key.replace("x_out"      , "fc_proj_out"   )
		if "x_emb" in key:
				return key.replace("x_emb"      , "embed_tokens"   )

		return key






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )     ->   Optional[Any]:
		_UpperCAmelCase		:       Optional[Any]								=      {}
		import re

		_UpperCAmelCase		:       Union[str, Any]								=      re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"   )
		_UpperCAmelCase		:       Tuple								=      re.compile(
		    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"   )
		_UpperCAmelCase		:       Any								=      re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"   )

		_UpperCAmelCase		:       Dict								=      re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"   )
		_UpperCAmelCase		:       str								=      re.compile(
		    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"   )
		_UpperCAmelCase		:       Tuple								=      re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"   )

		_UpperCAmelCase		:       List[Any]								=      re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"   )
		_UpperCAmelCase		:       str								=      re.compile(
		    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"   )
		_UpperCAmelCase		:       List[str]								=      re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"   )

		for original_key, value in state_dict.items():
				# rename vqvae.encoder keys
				if re_encoder_block_conv_in.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Tuple								=      re_encoder_block_conv_in.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Union[str, Any]								=      regex_match.groups()
						_UpperCAmelCase		:       str								=      int(groups[2]   ) * 2 + int(groups[3]   )
						_UpperCAmelCase		:       Optional[Any]								=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
						_UpperCAmelCase		:       List[Any]								=      re_encoder_block_conv_in.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_encoder_block_resnet.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Dict								=      re_encoder_block_resnet.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      regex_match.groups()
						_UpperCAmelCase		:       str								=      int(groups[2]   ) * 2 + int(groups[3]   )
						_UpperCAmelCase		:       List[Any]								=      {"1": 1, "3": 2}[groups[-2]]
						_UpperCAmelCase		:       List[str]								=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
						_UpperCAmelCase		:       Any								=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
						_UpperCAmelCase		:       List[Any]								=      prefix + resnet_block
						_UpperCAmelCase		:       Any								=      re_encoder_block_resnet.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_encoder_block_proj_out.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Any								=      re_encoder_block_proj_out.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       List[Any]								=      regex_match.groups()
						_UpperCAmelCase		:       Tuple								=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
						_UpperCAmelCase		:       int								=      re_encoder_block_proj_out.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				# rename vqvae.decoder keys
				elif re_decoder_block_conv_out.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Optional[Any]								=      re_decoder_block_conv_out.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Union[str, Any]								=      regex_match.groups()
						_UpperCAmelCase		:       Optional[int]								=      int(groups[2]   ) * 2 + int(groups[3]   ) - 2
						_UpperCAmelCase		:       Optional[Any]								=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
						_UpperCAmelCase		:       List[Any]								=      re_decoder_block_conv_out.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_decoder_block_resnet.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Optional[Any]								=      re_decoder_block_resnet.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       List[Any]								=      regex_match.groups()
						_UpperCAmelCase		:       int								=      int(groups[2]   ) * 2 + int(groups[3]   ) - 2
						_UpperCAmelCase		:       List[Any]								=      {"1": 1, "3": 2}[groups[-2]]
						_UpperCAmelCase		:       Optional[Any]								=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
						_UpperCAmelCase		:       Optional[int]								=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
						_UpperCAmelCase		:       List[str]								=      prefix + resnet_block
						_UpperCAmelCase		:       Dict								=      re_decoder_block_resnet.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_decoder_block_proj_in.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Union[str, Any]								=      re_decoder_block_proj_in.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Tuple								=      regex_match.groups()
						_UpperCAmelCase		:       List[Any]								=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
						_UpperCAmelCase		:       Any								=      re_decoder_block_proj_in.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				# rename prior cond.model to upsampler.upsample_block and resnet
				elif re_prior_cond_conv_out.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       List[str]								=      re_prior_cond_conv_out.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Tuple								=      regex_match.groups()
						_UpperCAmelCase		:       Any								=      int(groups[1]   ) * 2 + int(groups[2]   ) - 2
						_UpperCAmelCase		:       int								=      F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
						_UpperCAmelCase		:       Optional[Any]								=      re_prior_cond_conv_out.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_prior_cond_resnet.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Dict								=      re_prior_cond_resnet.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      regex_match.groups()
						_UpperCAmelCase		:       int								=      int(groups[1]   ) * 2 + int(groups[2]   ) - 2
						_UpperCAmelCase		:       Tuple								=      {"1": 1, "3": 2}[groups[-2]]
						_UpperCAmelCase		:       Optional[Any]								=      F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
						_UpperCAmelCase		:       Tuple								=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
						_UpperCAmelCase		:       Any								=      prefix + resnet_block
						_UpperCAmelCase		:       int								=      re_prior_cond_resnet.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				elif re_prior_cond_proj_in.fullmatch(__lowerCAmelCase   ):
						_UpperCAmelCase		:       str								=      re_prior_cond_proj_in.match(__lowerCAmelCase   )
						_UpperCAmelCase		:       List[Any]								=      regex_match.groups()
						_UpperCAmelCase		:       Optional[Any]								=      F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
						_UpperCAmelCase		:       int								=      re_prior_cond_proj_in.sub(__lowerCAmelCase      , __lowerCAmelCase   )

				# keep original key
				else:
						_UpperCAmelCase		:       int								=      original_key

				_UpperCAmelCase		:       List[str]								=      replace_key(__lowerCAmelCase   )

				if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
						print(F"""failed converting {original_key} to {key}, does not match"""   )

				# handle missmatched shape
				elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
						_UpperCAmelCase		:       Optional[int]								=      model_state_dict[F"""{key_prefix}.{key}"""]
						print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
						_UpperCAmelCase		:       Tuple								=      original_key

				_UpperCAmelCase		:       List[Any]								=      original_key
				_UpperCAmelCase		:       Dict								=      value

		return new_dict






@torch.no_grad()
def  __lowerCAmelCase (__lowerCAmelCase=None      , __lowerCAmelCase=None   )     ->   Optional[int]:
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"""   ):
						_UpperCAmelCase		:       Union[str, Any]								=      requests.get(F"""{PREFIX}{file}"""      , allow_redirects=__lowerCAmelCase   )
						os.makedirs(F"""{pytorch_dump_folder_path}/"""      , exist_ok=__lowerCAmelCase   )
						open(F"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"""      , "wb"   ).write(r.content   )

		_UpperCAmelCase		:       Union[str, Any]								=      MODEL_MAPPING[model_name.split("/"   )[-1]]

		_UpperCAmelCase		:       Dict								=      JukeboxConfig.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       List[Any]								=      JukeboxModel(__lowerCAmelCase   )

		_UpperCAmelCase		:       List[Any]								=      []
		_UpperCAmelCase		:       Union[str, Any]								=      {}
		for i, dict_name in enumerate(__lowerCAmelCase   ):
				_UpperCAmelCase		:       Optional[int]								=      torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"""   )["model"]

				_UpperCAmelCase		:       List[Any]								=      {}
				for k in old_dic.keys():
						if k.endswith(".b"   ):
								_UpperCAmelCase		:       Optional[Any]								=      old_dic[k]
						elif k.endswith(".w"   ):
								_UpperCAmelCase		:       Any								=      old_dic[k]
						elif "level_2" not in dict_name and "cond.model." in k:
								_UpperCAmelCase		:       Tuple								=      old_dic[k]
						else:
								_UpperCAmelCase		:       List[Any]								=      old_dic[k]

				_UpperCAmelCase		:       Dict								=      "vqvae" if i == 0 else F"""priors.{3 - i}"""
				_UpperCAmelCase		:       List[str]								=      fix_jukebox_keys(__lowerCAmelCase      , model.state_dict()      , __lowerCAmelCase      , __lowerCAmelCase   )
				weight_dict.append(__lowerCAmelCase   )

		_UpperCAmelCase		:       List[str]								=      weight_dict.pop(0   )
		model.vqvae.load_state_dict(__lowerCAmelCase   )
		for i in range(len(__lowerCAmelCase   )   ):
				model.priors[i].load_state_dict(weight_dict[2 - i]   )

		Path(__lowerCAmelCase   ).mkdir(exist_ok=__lowerCAmelCase   )
		with open(F"""{pytorch_dump_folder_path}/mapping.json"""      , "w"   ) as txtfile:
				json.dump(__lowerCAmelCase      , __lowerCAmelCase   )

		print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
		model.save_pretrained(__lowerCAmelCase   )

		return weight_dict


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='jukebox-5b-lyrics',
						    type=str,
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='jukebox-5b-lyrics-converted',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						lowerCamelCase__    	=     parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''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


lowerCamelCase__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return int(x / 2**20   )






class 	lowerCAmelCase__			:




			def __enter__(		self  :     int )   ->Optional[Any]:



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_UpperCAmelCase		:       Tuple								=      torch.cuda.memory_allocated()
					return self






			def __exit__(		self  :     Tuple	,  *lowerCamelCase__  :     str )   ->int:



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					_UpperCAmelCase		:       List[str]								=      torch.cuda.memory_allocated()
					_UpperCAmelCase		:       Tuple								=      torch.cuda.max_memory_allocated()
					_UpperCAmelCase		:       List[Any]								=      bamb(self.end - self.begin )
					_UpperCAmelCase		:       int								=      bamb(self.peak - self.begin )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"      , __lowerCAmelCase = 320      , __lowerCAmelCase = 160      , ):
		_UpperCAmelCase		:       int								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       Any								=      load_dataset(
		    "glue"      , "mrpc"      , split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

		def tokenize_function(__lowerCAmelCase   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCAmelCase		:       List[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
		_UpperCAmelCase		:       int								=      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
		_UpperCAmelCase		:       List[Any]								=      tokenized_datasets.rename_column("label"      , "labels"   )

		def collate_fn(__lowerCAmelCase   ):
				# 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.
		_UpperCAmelCase		:       Any								=      DataLoader(
		    tokenized_datasets["train"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )
		_UpperCAmelCase		:       List[str]								=      DataLoader(
		    tokenized_datasets["validation"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )

		return train_dataloader, eval_dataloader






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		# Initialize accelerator
		_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

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

		set_seed(__lowerCAmelCase   )
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[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)
		_UpperCAmelCase		:       Optional[int]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

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

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCAmelCase		:       Any								=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				_UpperCAmelCase		:       Any								=      1
		_UpperCAmelCase		:       Optional[int]								=      (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
		):
				_UpperCAmelCase		:       Tuple								=      get_linear_schedule_with_warmup(
				    optimizer=__lowerCAmelCase      , num_warmup_steps=0      , num_training_steps=__lowerCAmelCase      , )
		else:
				_UpperCAmelCase		:       Optional[Any]								=      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.
		_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      accelerator.prepare(
		    __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

		# We need to keep track of how many total steps we have iterated over
		_UpperCAmelCase		:       Union[str, Any]								=      0
		# We also need to keep track of the stating epoch so files are named properly
		_UpperCAmelCase		:       str								=      0

		# Now we train the model
		_UpperCAmelCase		:       Optional[Any]								=      {}
		for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(__lowerCAmelCase   ):
								_UpperCAmelCase		:       Union[str, Any]								=      model(**__lowerCAmelCase   )
								_UpperCAmelCase		:       Union[str, Any]								=      outputs.loss
								_UpperCAmelCase		:       List[Any]								=      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   )   )   )
				_UpperCAmelCase		:       Optional[int]								=      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  __lowerCAmelCase ():
		_UpperCAmelCase		:       Any								=      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."      , )
		_UpperCAmelCase		:       Tuple								=      parser.parse_args()
		_UpperCAmelCase		:       Optional[Any]								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
		training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						main()



