"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					torch.FloatTensor





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a=3	,    a=3	,    a=("DownEncoderBlock2D",)	,    a=(6_4,)	,    a=2	,    a=3_2	,    a="silu"	,    a=True	,    ) ->      str:
             super().__init__()
             lowercase__    :	Tuple     					=			layers_per_block

             lowercase__    :	Union[str, Any]     					=			torch.nn.Convad(
                 a	,    block_out_channels[0]	,    kernel_size=3	,    stride=1	,    padding=1	,    )

             lowercase__    :	int     					=			None
             lowercase__    :	str     					=			nn.ModuleList([]							)

             # down
             lowercase__    :	int     					=			block_out_channels[0]
             for i, down_block_type in enumerate(a							):
                    lowercase__    :	List[Any]     					=			output_channel
                    lowercase__    :	Optional[int]     					=			block_out_channels[i]
                    lowercase__    :	Union[str, Any]     					=			i == len(a							) - 1

                    lowercase__    :	Tuple     					=			get_down_block(
                        a	,    num_layers=self.layers_per_block	,    in_channels=a	,    out_channels=a	,    add_downsample=not is_final_block	,    resnet_eps=1e-6	,    downsample_padding=0	,    resnet_act_fn=a	,    resnet_groups=a	,    attention_head_dim=a	,    temb_channels=a	,    )
                    self.down_blocks.append(a							)

             # mid
             lowercase__    :	int     					=			UNetMidBlockaD(
                 in_channels=block_out_channels[-1]	,    resnet_eps=1e-6	,    resnet_act_fn=a	,    output_scale_factor=1	,    resnet_time_scale_shift='default'	,    attention_head_dim=block_out_channels[-1]	,    resnet_groups=a	,    temb_channels=a	,    )

             # out
             lowercase__    :	Dict     					=			nn.GroupNorm(num_channels=block_out_channels[-1]	,    num_groups=a	,    eps=1e-6							)
             lowercase__    :	Union[str, Any]     					=			nn.SiLU()

             lowercase__    :	Dict     					=			2 * out_channels if double_z else out_channels
             lowercase__    :	List[Any]     					=			nn.Convad(block_out_channels[-1]	,    a	,    3	,    padding=1							)

             lowercase__    :	Dict     					=			False


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             lowercase__    :	Dict     					=			x
             lowercase__    :	Optional[Any]     					=			self.conv_in(a							)

             if self.training and self.gradient_checkpointing:

                    def create_custom_forward(a							):
                           def custom_forward(*a							):
                                  return module(*a							)

                           return custom_forward

                    # down
                    if is_torch_version('>='	,    '1.11.0'							):
                           for down_block in self.down_blocks:
                                  lowercase__    :	int     					=			torch.utils.checkpoint.checkpoint(
                                      create_custom_forward(a							)	,    a	,    use_reentrant=a							)
                           # middle
                           lowercase__    :	int     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    use_reentrant=a							)
                    else:
                           for down_block in self.down_blocks:
                                  lowercase__    :	Optional[int]     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(a							)	,    a							)
                           # middle
                           lowercase__    :	Union[str, Any]     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)	,    a							)

             else:
                    # down
                    for down_block in self.down_blocks:
                           lowercase__    :	int     					=			down_block(a							)

                    # middle
                    lowercase__    :	Optional[int]     					=			self.mid_block(a							)

             # post-process
             lowercase__    :	List[Any]     					=			self.conv_norm_out(a							)
             lowercase__    :	Dict     					=			self.conv_act(a							)
             lowercase__    :	Optional[Any]     					=			self.conv_out(a							)

             return sample





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a=3	,    a=3	,    a=("UpDecoderBlock2D",)	,    a=(6_4,)	,    a=2	,    a=3_2	,    a="silu"	,    a="group"	,    ) ->      Dict:
             super().__init__()
             lowercase__    :	List[Any]     					=			layers_per_block

             lowercase__    :	Optional[int]     					=			nn.Convad(
                 a	,    block_out_channels[-1]	,    kernel_size=3	,    stride=1	,    padding=1	,    )

             lowercase__    :	Tuple     					=			None
             lowercase__    :	Dict     					=			nn.ModuleList([]							)

             lowercase__    :	int     					=			in_channels if norm_type == 'spatial' else None

             # mid
             lowercase__    :	List[Any]     					=			UNetMidBlockaD(
                 in_channels=block_out_channels[-1]	,    resnet_eps=1e-6	,    resnet_act_fn=a	,    output_scale_factor=1	,    resnet_time_scale_shift='default' if norm_type == 'group' else norm_type	,    attention_head_dim=block_out_channels[-1]	,    resnet_groups=a	,    temb_channels=a	,    )

             # up
             lowercase__    :	Optional[Any]     					=			list(reversed(a							)							)
             lowercase__    :	Dict     					=			reversed_block_out_channels[0]
             for i, up_block_type in enumerate(a							):
                    lowercase__    :	str     					=			output_channel
                    lowercase__    :	str     					=			reversed_block_out_channels[i]

                    lowercase__    :	Tuple     					=			i == len(a							) - 1

                    lowercase__    :	List[Any]     					=			get_up_block(
                        a	,    num_layers=self.layers_per_block + 1	,    in_channels=a	,    out_channels=a	,    prev_output_channel=a	,    add_upsample=not is_final_block	,    resnet_eps=1e-6	,    resnet_act_fn=a	,    resnet_groups=a	,    attention_head_dim=a	,    temb_channels=a	,    resnet_time_scale_shift=a	,    )
                    self.up_blocks.append(a							)
                    lowercase__    :	Optional[Any]     					=			output_channel

             # out
             if norm_type == "spatial":
                    lowercase__    :	Optional[Any]     					=			SpatialNorm(block_out_channels[0]	,    a							)
             else:
                    lowercase__    :	Union[str, Any]     					=			nn.GroupNorm(num_channels=block_out_channels[0]	,    num_groups=a	,    eps=1e-6							)
             lowercase__    :	Union[str, Any]     					=			nn.SiLU()
             lowercase__    :	Any     					=			nn.Convad(block_out_channels[0]	,    a	,    3	,    padding=1							)

             lowercase__    :	str     					=			False


      def 	_UpperCAmelCase (     self	,    a	,    a=None							) ->      Dict:
             lowercase__    :	List[str]     					=			z
             lowercase__    :	List[Any]     					=			self.conv_in(a							)

             lowercase__    :	Dict     					=			next(iter(self.up_blocks.parameters()							)							).dtype
             if self.training and self.gradient_checkpointing:

                    def create_custom_forward(a							):
                           def custom_forward(*a							):
                                  return module(*a							)

                           return custom_forward

                    if is_torch_version('>='	,    '1.11.0'							):
                           # middle
                           lowercase__    :	List[Any]     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    a	,    use_reentrant=a							)
                           lowercase__    :	List[Any]     					=			sample.to(a							)

                           # up
                           for up_block in self.up_blocks:
                                  lowercase__    :	Dict     					=			torch.utils.checkpoint.checkpoint(
                                      create_custom_forward(a							)	,    a	,    a	,    use_reentrant=a							)
                    else:
                           # middle
                           lowercase__    :	str     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    a							)
                           lowercase__    :	Optional[Any]     					=			sample.to(a							)

                           # up
                           for up_block in self.up_blocks:
                                  lowercase__    :	Any     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(a							)	,    a	,    a							)
             else:
                    # middle
                    lowercase__    :	Any     					=			self.mid_block(a	,    a							)
                    lowercase__    :	List[Any]     					=			sample.to(a							)

                    # up
                    for up_block in self.up_blocks:
                           lowercase__    :	Any     					=			up_block(a	,    a							)

        # post-process
             if latent_embeds is None:
                    lowercase__    :	int     					=			self.conv_norm_out(a							)
             else:
                    lowercase__    :	Union[str, Any]     					=			self.conv_norm_out(a	,    a							)
             lowercase__    :	Union[str, Any]     					=			self.conv_act(a							)
             lowercase__    :	Optional[int]     					=			self.conv_out(a							)

             return sample





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a	,    a	,    a=None	,    a="random"	,    a=False	,    a=True							) ->      Optional[Any]:
             super().__init__()
             lowercase__    :	int     					=			n_e
             lowercase__    :	str     					=			vq_embed_dim
             lowercase__    :	Tuple     					=			beta
             lowercase__    :	List[Any]     					=			legacy

             lowercase__    :	Tuple     					=			nn.Embedding(self.n_e	,    self.vq_embed_dim							)
             self.embedding.weight.data.uniform_(-1.0 / self.n_e	,    1.0 / self.n_e							)

             lowercase__    :	Optional[int]     					=			remap
             if self.remap is not None:
                    self.register_buffer('used'	,    torch.tensor(np.load(self.remap							)							)							)
                    lowercase__    :	Optional[int]     					=			self.used.shape[0]
                    lowercase__    :	List[Any]     					=			unknown_index  # "random" or "extra" or integer
                    if self.unknown_index == "extra":
                           lowercase__    :	Any     					=			self.re_embed
                           lowercase__    :	List[Any]     					=			self.re_embed + 1
                    print(
                        f"""Remapping {self.n_e} indices to {self.re_embed} indices. """
                        f"""Using {self.unknown_index} for unknown indices."""							)
             else:
                    lowercase__    :	str     					=			n_e

             lowercase__    :	str     					=			sane_index_shape


      def 	_UpperCAmelCase (     self	,    a							) ->      List[Any]:
             lowercase__    :	Dict     					=			inds.shape
             assert len(a							) > 1
             lowercase__    :	List[Any]     					=			inds.reshape(ishape[0]	,    -1							)
             lowercase__    :	Tuple     					=			self.used.to(a							)
             lowercase__    :	str     					=			(inds[:, :, None] == used[None, None, ...]).long()
             lowercase__    :	Dict     					=			match.argmax(-1							)
             lowercase__    :	int     					=			match.sum(2							) < 1
             if self.unknown_index == "random":
                    lowercase__    :	List[Any]     					=			torch.randint(0	,    self.re_embed	,    size=new[unknown].shape							).to(device=new.device							)
             else:
                    lowercase__    :	Optional[Any]     					=			self.unknown_index
             return new.reshape(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Tuple:
             lowercase__    :	List[str]     					=			inds.shape
             assert len(a							) > 1
             lowercase__    :	int     					=			inds.reshape(ishape[0]	,    -1							)
             lowercase__    :	str     					=			self.used.to(a							)
             if self.re_embed > self.used.shape[0]:  # extra token
                    lowercase__    :	Dict     					=			0  # simply set to zero
             lowercase__    :	Any     					=			torch.gather(used[None, :][inds.shape[0] * [0], :]	,    1	,    a							)
             return back.reshape(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      List[str]:
             # reshape z -> (batch, height, width, channel) and flatten
             lowercase__    :	str     					=			z.permute(0	,    2	,    3	,    1							).contiguous()
             lowercase__    :	List[str]     					=			z.view(-1	,    self.vq_embed_dim							)

             # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
             lowercase__    :	str     					=			torch.argmin(torch.cdist(a	,    self.embedding.weight							)	,    dim=1							)

             lowercase__    :	str     					=			self.embedding(a							).view(z.shape							)
             lowercase__    :	Union[str, Any]     					=			None
             lowercase__    :	Tuple     					=			None

             # compute loss for embedding
             if not self.legacy:
                    lowercase__    :	int     					=			self.beta * torch.mean((z_q.detach() - z) ** 2							) + torch.mean((z_q - z.detach()) ** 2							)
             else:
                    lowercase__    :	str     					=			torch.mean((z_q.detach() - z) ** 2							) + self.beta * torch.mean((z_q - z.detach()) ** 2							)

             # preserve gradients
             lowercase__    :	List[str]     					=			z + (z_q - z).detach()

             # reshape back to match original input shape
             lowercase__    :	List[str]     					=			z_q.permute(0	,    3	,    1	,    2							).contiguous()

             if self.remap is not None:
                    lowercase__    :	Optional[int]     					=			min_encoding_indices.reshape(z.shape[0]	,    -1							)  # add batch axis
                    lowercase__    :	Optional[int]     					=			self.remap_to_used(a							)
                    lowercase__    :	Union[str, Any]     					=			min_encoding_indices.reshape(-1	,    1							)  # flatten

             if self.sane_index_shape:
                    lowercase__    :	Any     					=			min_encoding_indices.reshape(z_q.shape[0]	,    z_q.shape[2]	,    z_q.shape[3]							)

             return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Any:
             # shape specifying (batch, height, width, channel)
             if self.remap is not None:
                    lowercase__    :	Optional[int]     					=			indices.reshape(shape[0]	,    -1							)  # add batch axis
                    lowercase__    :	Any     					=			self.unmap_to_all(a							)
                    lowercase__    :	Dict     					=			indices.reshape(-1							)  # flatten again

             # get quantized latent vectors
             lowercase__    :	Optional[Any]     					=			self.embedding(a							)

             if shape is not None:
                    lowercase__    :	Dict     					=			z_q.view(a							)
                    # reshape back to match original input shape
                    lowercase__    :	Union[str, Any]     					=			z_q.permute(0	,    3	,    1	,    2							).contiguous()

             return z_q





class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a=False							) ->      Any:
             lowercase__    :	Optional[Any]     					=			parameters
             lowercase__    :	List[str]     					=			torch.chunk(a	,    2	,    dim=1							)
             lowercase__    :	Any     					=			torch.clamp(self.logvar	,    -30.0	,    20.0							)
             lowercase__    :	Dict     					=			deterministic
             lowercase__    :	List[str]     					=			torch.exp(0.5 * self.logvar							)
             lowercase__    :	Optional[int]     					=			torch.exp(self.logvar							)
             if self.deterministic:
                    lowercase__    :	int     					=			torch.zeros_like(
                        self.mean	,    device=self.parameters.device	,    dtype=self.parameters.dtype							)


      def 	_UpperCAmelCase (     self	,    a = None							) ->      torch.FloatTensor:
             # make sure sample is on the same device as the parameters and has same dtype
             lowercase__    :	List[str]     					=			randn_tensor(
                 self.mean.shape	,    generator=a	,    device=self.parameters.device	,    dtype=self.parameters.dtype							)
             lowercase__    :	Dict     					=			self.mean + self.std * sample
             return x


      def 	_UpperCAmelCase (     self	,    a=None							) ->      Union[str, Any]:
             if self.deterministic:
                    return torch.Tensor([0.0]							)
             else:
                    if other is None:
                           return 0.5 * torch.sum(torch.pow(self.mean	,    2							) + self.var - 1.0 - self.logvar	,    dim=[1, 2, 3]							)
                    else:
                           return 0.5 * torch.sum(
                               torch.pow(self.mean - other.mean	,    2							) / other.var
                               + self.var / other.var
                               - 1.0
                               - self.logvar
                               + other.logvar	,    dim=[1, 2, 3]	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a=[1, 2, 3]							) ->      str:
             if self.deterministic:
                    return torch.Tensor([0.0]							)
             lowercase__    :	Optional[Any]     					=			np.log(2.0 * np.pi							)
             return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean	,    2							) / self.var	,    dim=a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             return self.mean


"""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


_UpperCamelCase     :		int											=					16
_UpperCamelCase     :		Union[str, Any]											=					32





def        a_  (   _lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       return int(x / 2**20  )


class   UpperCAmelCase_			:


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


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





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				int = 16     ,				_lowerCAmelCase						:				str = "bert-base-cased"     ,				_lowerCAmelCase						:				int = 320     ,				_lowerCAmelCase						:				int = 160     ,				):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			load_dataset(
           'glue'     ,				'mrpc'     ,				split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""}  )

       def tokenize_function(_lowerCAmelCase						:				int  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[str]     					=			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__    :	Union[str, Any]     					=			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__    :	Union[str, Any]     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

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

       # Instantiate dataloaders.
       lowercase__    :	Dict     					=			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        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			Accelerator()

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

       set_seed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	Tuple     					=			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__    :	List[Any]     					=			AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase     ,				return_dict=_lowerCAmelCase  )

       # Instantiate optimizer
       lowercase__    :	List[Any]     					=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__    :	Optional[Any]     					=			optimizer_cls(params=model.parameters()     ,				lr=_lowerCAmelCase  )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__    :	Optional[Any]     					=			accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              lowercase__    :	List[Any]     					=			1
       lowercase__    :	List[Any]     					=			(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__    :	Optional[int]     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=0     ,				num_training_steps=_lowerCAmelCase     ,				)
       else:
              lowercase__    :	Tuple     					=			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__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			accelerator.prepare(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

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

       # Now we train the model
       lowercase__    :	Optional[Any]     					=			{}
       for epoch in range(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            lowercase__    :	List[Any]     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	Dict     					=			outputs.loss
                            lowercase__    :	int     					=			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__    :	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        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			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__    :	Any     					=			parser.parse_args()
       lowercase__    :	Optional[Any]     					=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()

