code: 
  import types
  from typing import List, Optional, Tuple, Union
  
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from transformers.models.clip.modeling_clip import CLIPTextModelOutput
  
  from diffusers.models import PriorTransformer
  from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
  from diffusers.schedulers import UnCLIPScheduler
  from diffusers.utils import logging, randn_tensor
  
  
  snake_case				:	Tuple							   =						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      self   :						Any   ,     _snake_case   :						List[str]   ,     _snake_case   :						Dict   ,     _snake_case   :						Union[str, Any]   ,     _snake_case   :						List[str]     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  image.to(device=_snake_case     )
  	__magic_name__	:	Optional[int]			     =  image  # take image as image_embeddings
  	__magic_name__	:	str			     =  image_embeddings.unsqueeze(1     )
  
  	# duplicate image embeddings for each generation per prompt, using mps friendly method
  	__magic_name__	:	Any			     =  image_embeddings.shape
  	__magic_name__	:	Optional[int]			     =  image_embeddings.repeat(1   ,     _snake_case   ,     1     )
  	__magic_name__	:	int			     =  image_embeddings.view(bs_embed * num_images_per_prompt   ,     _snake_case   ,     -1     )
  
  	if do_classifier_free_guidance:
  		__magic_name__	:	List[str]			     =  torch.zeros_like(_snake_case     )
  
  		# For classifier free guidance, we need to do two forward passes.
  		# Here we concatenate the unconditional and text embeddings into a single batch
  		# to avoid doing two forward passes
  		__magic_name__	:	Optional[Any]			     =  torch.cat([uncond_embeddings, image_embeddings]     )
  
  	return image_embeddings
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  	def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     ):
  		super().__init__()
  
  		__magic_name__	:	List[str]			     =  {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  		__magic_name__	:	int			     =  decoder_pipe_kwargs.get("torch_dtype"		,     _a	) or prior.dtype
  
  		__magic_name__	:	List[str]			     =  StableDiffusionImageVariationPipeline.from_pretrained(
  		    "lambdalabs/sd-image-variations-diffusers"		,     **_a	)
  
  		# replace `_encode_image` method
  		__magic_name__	:	List[Any]			     =  types.MethodType(_encode_image		,     self.decoder_pipe	)
  
  		self.register_modules(
  		    prior=_a		,     tokenizer=_a		,     text_encoder=_a		,     prior_scheduler=_a		,     )
  
  
  
  
  
  
  
  	def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     _a = None		,     ):
  		if text_model_output is None:
  			__magic_name__	:	Tuple			     =  len(_a	) if isinstance(_a		,     _a	) else 1
  			# get prompt text embeddings
  			__magic_name__	:	Tuple			     =  self.tokenizer(
  			    _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     return_tensors="pt"		,     )
  			__magic_name__	:	List[Any]			     =  text_inputs.input_ids
  			__magic_name__	:	List[str]			     =  text_inputs.attention_mask.bool().to(_a	)
  
  			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  				__magic_name__	:	Dict			     =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
  				logger.warning(
  				    "The following part of your input was truncated because CLIP can only handle sequences up to"
  				    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''	)
  				__magic_name__	:	str			     =  text_input_ids[:, : self.tokenizer.model_max_length]
  
  			__magic_name__	:	int			     =  self.text_encoder(text_input_ids.to(_a	)	)
  
  			__magic_name__	:	Dict			     =  text_encoder_output.text_embeds
  			__magic_name__	:	Tuple			     =  text_encoder_output.last_hidden_state
  
  		else:
  			__magic_name__	:	Tuple			     =  text_model_output[0].shape[0]
  			__magic_name__	:	int			     =  text_model_output[0], text_model_output[1]
  			__magic_name__	:	str			     =  text_attention_mask
  
  		__magic_name__	:	List[Any]			     =  text_embeddings.repeat_interleave(_a		,     dim=0	)
  		__magic_name__	:	str			     =  text_encoder_hidden_states.repeat_interleave(_a		,     dim=0	)
  		__magic_name__	:	str			     =  text_mask.repeat_interleave(_a		,     dim=0	)
  
  		if do_classifier_free_guidance:
  			__magic_name__	:	Union[str, Any]			     =  [""] * batch_size
  
  			__magic_name__	:	Tuple			     =  self.tokenizer(
  			    _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     truncation=_a		,     return_tensors="pt"		,     )
  			__magic_name__	:	Dict			     =  uncond_input.attention_mask.bool().to(_a	)
  			__magic_name__	:	Dict			     =  self.text_encoder(uncond_input.input_ids.to(_a	)	)
  
  			__magic_name__	:	Union[str, Any]			     =  uncond_embeddings_text_encoder_output.text_embeds
  			__magic_name__	:	List[str]			     =  uncond_embeddings_text_encoder_output.last_hidden_state
  
  			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  			__magic_name__	:	str			     =  uncond_embeddings.shape[1]
  			__magic_name__	:	Dict			     =  uncond_embeddings.repeat(1		,     _a	)
  			__magic_name__	:	Any			     =  uncond_embeddings.view(batch_size * num_images_per_prompt		,     _a	)
  
  			__magic_name__	:	List[Any]			     =  uncond_text_encoder_hidden_states.shape[1]
  			__magic_name__	:	Optional[Any]			     =  uncond_text_encoder_hidden_states.repeat(1		,     _a		,     1	)
  			__magic_name__	:	int			     =  uncond_text_encoder_hidden_states.view(
  			    batch_size * num_images_per_prompt		,     _a		,     -1	)
  			__magic_name__	:	Union[str, Any]			     =  uncond_text_mask.repeat_interleave(_a		,     dim=0	)
  
  			# done duplicates
  
  			# For classifier free guidance, we need to do two forward passes.
  			# Here we concatenate the unconditional and text embeddings into a single batch
  			# to avoid doing two forward passes
  			__magic_name__	:	List[str]			     =  torch.cat([uncond_embeddings, text_embeddings]	)
  			__magic_name__	:	Union[str, Any]			     =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)
  
  			__magic_name__	:	List[Any]			     =  torch.cat([uncond_text_mask, text_mask]	)
  
  		return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE					(       self	):
  		if self.device != torch.device("meta"	) or not hasattr(self.prior		,     "_hf_hook"	):
  			return self.device
  		for module in self.prior.modules():
  			if (
  			    hasattr(_a		,     "_hf_hook"	)
  			    and hasattr(module._hf_hook		,     "execution_device"	)
  			    and module._hf_hook.execution_device is not None
  			):
  				return torch.device(module._hf_hook.execution_device	)
  		return self.device
  
  
  
  
  
  
  
  	def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a	):
  		if latents is None:
  			__magic_name__	:	Tuple			     =  randn_tensor(_a		,     generator=_a		,     device=_a		,     dtype=_a	)
  		else:
  			if latents.shape != shape:
  				raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
  			__magic_name__	:	Optional[int]			     =  latents.to(_a	)
  
  		__magic_name__	:	Dict			     =  latents * scheduler.init_noise_sigma
  		return latents
  
  
  
  
  
  
  
  	def     SCREAMING_SNAKE_CASE					(       self		,     _a = None	):
  		self.decoder_pipe.to(_a	)
  		super().to(_a	)
  
  
  
  
  
  
  
  	@torch.no_grad()
  	def __call__(       self		,     _a = None		,     _a = None		,     _a = None		,     _a = 1		,     _a = 25		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 8.0		,     _a = 50		,     _a = 1		,     _a = 0.0		,     _a = "pil"		,     _a = True		,     ):
  		if prompt is not None:
  			if isinstance(_a		,     _a	):
  				__magic_name__	:	int			     =  1
  			elif isinstance(_a		,     _a	):
  				__magic_name__	:	List[str]			     =  len(_a	)
  			else:
  				raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_a	)}'''	)
  		else:
  			__magic_name__	:	int			     =  text_model_output[0].shape[0]
  
  		__magic_name__	:	int			     =  self._execution_device
  
  		__magic_name__	:	List[str]			     =  batch_size * num_images_per_prompt
  
  		__magic_name__	:	Dict			     =  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  		__magic_name__	:	Tuple			     =  self._encode_prompt(
  		    _a		,     _a		,     _a		,     _a		,     _a		,     _a	)
  
  		# prior
  
  		self.prior_scheduler.set_timesteps(_a		,     device=_a	)
  		__magic_name__	:	Tuple			     =  self.prior_scheduler.timesteps
  
  		__magic_name__	:	int			     =  self.prior.config.embedding_dim
  
  		__magic_name__	:	List[Any]			     =  self.prepare_latents(
  		    (batch_size, embedding_dim)		,     text_embeddings.dtype		,     _a		,     _a		,     _a		,     self.prior_scheduler		,     )
  
  		for i, t in enumerate(self.progress_bar(_a	)	):
  			# expand the latents if we are doing classifier free guidance
  			__magic_name__	:	List[str]			     =  torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents
  
  			__magic_name__	:	List[Any]			     =  self.prior(
  			    _a		,     timestep=_a		,     proj_embedding=_a		,     encoder_hidden_states=_a		,     attention_mask=_a		,     ).predicted_image_embedding
  
  			if do_classifier_free_guidance:
  				__magic_name__	:	List[Any]			     =  predicted_image_embedding.chunk(2	)
  				__magic_name__	:	str			     =  predicted_image_embedding_uncond + prior_guidance_scale * (
  				    predicted_image_embedding_text - predicted_image_embedding_uncond
  				)
  
  			if i + 1 == prior_timesteps_tensor.shape[0]:
  				__magic_name__	:	Union[str, Any]			     =  None
  			else:
  				__magic_name__	:	Optional[Any]			     =  prior_timesteps_tensor[i + 1]
  
  			__magic_name__	:	List[Any]			     =  self.prior_scheduler.step(
  			    _a		,     timestep=_a		,     sample=_a		,     generator=_a		,     prev_timestep=_a		,     ).prev_sample
  
  		__magic_name__	:	List[Any]			     =  self.prior.post_process_latents(_a	)
  
  		__magic_name__	:	Optional[int]			     =  prior_latents
  
  		__magic_name__	:	Optional[int]			     =  self.decoder_pipe(
  		    image=_a		,     height=_a		,     width=_a		,     num_inference_steps=_a		,     guidance_scale=_a		,     generator=_a		,     output_type=_a		,     return_dict=_a		,     num_images_per_prompt=_a		,     eta=_a		,     )
  		return output
  
style_context: 
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #   - FSDP
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  
  snake_case				:	Any							   =						16
  snake_case				:	Union[str, Any]							   =						32
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return int(x / 2**20     )
  
  
  
  
  
  class    _snake_case      :
  
  
  
  
  
  
  
  			def __enter__(       self	):
  				gc.collect()
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  				__magic_name__	:	List[Any]			     =  torch.cuda.memory_allocated()
  				__magic_name__	:	Dict			     =  psutil.Process()
  
  				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
  				__magic_name__	:	Optional[int]			     =  True
  				__magic_name__	:	int			     =  threading.Thread(target=self.peak_monitor_func	)
  				__magic_name__	:	str			     =  True
  				peak_monitor_thread.start()
  				return self
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.process.memory_info().rss
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Dict			     =  -1
  
  				while True:
  					__magic_name__	:	Optional[Any]			     =  max(self.cpu_mem_used()		,     self.cpu_peak	)
  
  					# can't sleep or will not catch the peak right (this comment is here on purpose)
  					# time.sleep(0.001) # 1msec
  
  					if not self.peak_monitoring:
  						break
  
  
  
  
  
  
  
  			def __exit__(       self		,     *_a	):
  				__magic_name__	:	List[Any]			     =  False
  
  				gc.collect()
  				torch.cuda.empty_cache()
  				__magic_name__	:	Union[str, Any]			     =  torch.cuda.memory_allocated()
  				__magic_name__	:	Any			     =  torch.cuda.max_memory_allocated()
  				__magic_name__	:	List[Any]			     =  bamb(self.end - self.begin	)
  				__magic_name__	:	List[str]			     =  bamb(self.peak - self.begin	)
  
  				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
  				__magic_name__	:	int			     =  bamb(self.cpu_end - self.cpu_begin	)
  				__magic_name__	:	List[Any]			     =  bamb(self.cpu_peak - self.cpu_begin	)
  				# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
  				from accelerate.test_utils.training import mocked_dataloaders
  
  				snake_case				:	Tuple							   =						mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						str   ,     _snake_case   :						Dict     )       ->	Dict:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,     _snake_case     ) == "1":
  		__magic_name__	:	Optional[Any]			     =  2
  
  	# New Code #
  	# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  	__magic_name__	:	Dict			     =  FullyShardedDataParallelPlugin(
  	    state_dict_config=FullStateDictConfig(offload_to_cpu=_snake_case   ,     ranka_only=_snake_case     )   ,     optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_snake_case   ,     ranka_only=_snake_case     )   ,     )
  
  	# Initialize accelerator
  	if args.with_tracking:
  		__magic_name__	:	Union[str, Any]			     =  Accelerator(
  		    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with="wandb"   ,     project_dir=args.logging_dir   ,     fsdp_plugin=_snake_case   ,     )
  	else:
  		__magic_name__	:	List[str]			     =  Accelerator(fsdp_plugin=_snake_case     )
  	accelerator.print(accelerator.distributed_type     )
  
  	if hasattr(args.checkpointing_steps   ,     "isdigit"     ):
  		if args.checkpointing_steps == "epoch":
  			__magic_name__	:	List[Any]			     =  args.checkpointing_steps
  		elif args.checkpointing_steps.isdigit():
  			__magic_name__	:	List[Any]			     =  int(args.checkpointing_steps     )
  		else:
  			raise ValueError(
  			    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
  	else:
  		__magic_name__	:	List[str]			     =  None
  	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  	__magic_name__	:	List[Any]			     =  config["lr"]
  	__magic_name__	:	Optional[Any]			     =  int(config["num_epochs"]     )
  	__magic_name__	:	Optional[int]			     =  int(config["seed"]     )
  	__magic_name__	:	int			     =  int(config["batch_size"]     )
  
  	# We need to initialize the trackers we use, and also store our configuration
  	if args.with_tracking:
  		__magic_name__	:	List[Any]			     =  vars(_snake_case     )
  		accelerator.init_trackers("fsdp_glue_no_trainer"   ,     _snake_case     )
  
  	__magic_name__	:	Dict			     =  AutoTokenizer.from_pretrained(args.model_name_or_path     )
  	__magic_name__	:	List[Any]			     =  load_dataset("glue"   ,     "mrpc"     )
  	__magic_name__	:	Union[str, Any]			     =  evaluate.load("glue"   ,     "mrpc"     )
  
  	def tokenize_function(_snake_case   :						List[Any]     ):
  		# max_length=None => use the model max length (it's actually the default)
  		__magic_name__	:	Union[str, Any]			     =  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
  	# starting with the main process first:
  	with accelerator.main_process_first():
  		__magic_name__	:	int			     =  datasets.map(
  		    _snake_case   ,     batched=_snake_case   ,     remove_columns=["idx", "sentence1", "sentence2"]   ,     )
  
  	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  	# transformers library
  	__magic_name__	:	Optional[Any]			     =  tokenized_datasets.rename_column("label"   ,     "labels"     )
  
  	# If the batch size is too big we use gradient accumulation
  	__magic_name__	:	Dict			     =  1
  	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  		__magic_name__	:	List[Any]			     =  batch_size // MAX_GPU_BATCH_SIZE
  		__magic_name__	:	Dict			     =  MAX_GPU_BATCH_SIZE
  
  	def collate_fn(_snake_case   :						Union[str, Any]     ):
  		# On TPU it's best to pad everything to the same length or training will be very slow.
  		__magic_name__	:	Tuple			     =  128 if accelerator.distributed_type == DistributedType.TPU else None
  		# When using mixed precision we want round multiples of 8/16
  		if accelerator.mixed_precision == "fp8":
  			__magic_name__	:	List[str]			     =  16
  		elif accelerator.mixed_precision != "no":
  			__magic_name__	:	Any			     =  8
  		else:
  			__magic_name__	:	Tuple			     =  None
  
  		return tokenizer.pad(
  		    _snake_case   ,     padding="longest"   ,     max_length=_snake_case   ,     pad_to_multiple_of=_snake_case   ,     return_tensors="pt"   ,     )
  
  	# Instantiate dataloaders.
  	__magic_name__	:	Optional[int]			     =  DataLoader(
  	    tokenized_datasets["train"]   ,     shuffle=_snake_case   ,     collate_fn=_snake_case   ,     batch_size=_snake_case     )
  	__magic_name__	:	Dict			     =  DataLoader(
  	    tokenized_datasets["validation"]   ,     shuffle=_snake_case   ,     collate_fn=_snake_case   ,     batch_size=_snake_case     )
  
  	set_seed(_snake_case     )
  
  	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  	__magic_name__	:	Optional[Any]			     =  AutoModelForSequenceClassification.from_pretrained(
  	    args.model_name_or_path   ,     return_dict=_snake_case   ,     low_cpu_mem_usage=_snake_case     )
  
  	# New Code #
  	# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  	__magic_name__	:	List[str]			     =  accelerator.prepare(_snake_case     )
  	accelerator.print(_snake_case     )
  
  	# Instantiate optimizer
  	# New Code #
  	# For FSDP feature, at present it doesn't support multiple parameter groups,
  	# so we need to create a single parameter group for the whole model
  	__magic_name__	:	Optional[Any]			     =  torch.optim.AdamW(params=model.parameters()   ,     lr=_snake_case   ,     weight_decay=2E-4     )
  
  	# Instantiate scheduler
  	__magic_name__	:	Any			     =  get_linear_schedule_with_warmup(
  	    optimizer=_snake_case   ,     num_warmup_steps=10   ,     num_training_steps=(len(_snake_case     ) * num_epochs) // gradient_accumulation_steps   ,     )
  
  	# New Code #
  	# For FSDP feature, prepare everything except the model as we have already prepared the model
  	# before creating the optimizer
  	# 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.
  	__magic_name__     ,					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Dict			     =  accelerator.prepare(
  	    _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
  
  	__magic_name__	:	Union[str, Any]			     =  0
  
  	# Potentially load in the weights and states from a previous save
  	if args.resume_from_checkpoint:
  		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  			accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''     )
  			accelerator.load_state(args.resume_from_checkpoint     )
  			__magic_name__	:	Optional[Any]			     =  os.path.basename(args.resume_from_checkpoint     )
  		else:
  			# Get the most recent checkpoint
  			__magic_name__	:	Tuple			     =  [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
  			dirs.sort(key=os.path.getctime     )
  			__magic_name__	:	Tuple			     =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  		# Extract `epoch_{i}` or `step_{i}`
  		__magic_name__	:	Dict			     =  os.path.splitext(_snake_case     )[0]
  
  		if "epoch" in training_difference:
  			num_epochs -= int(training_difference.replace("epoch_"   ,     ""     )     )
  			__magic_name__	:	List[Any]			     =  None
  		else:
  			__magic_name__	:	List[str]			     =  int(training_difference.replace("step_"   ,     ""     )     )
  			num_epochs -= resume_step // len(_snake_case     )
  			# If resuming by step, we also need to know exactly how far into the DataLoader we went
  			__magic_name__	:	Tuple			     =  (num_epochs * len(_snake_case     )) - resume_step
  
      # Now we train the model
  	for epoch in range(_snake_case     ):
  		# New Code #
  		# context manager to track the peak memory usage during the training epoch
  		with TorchTracemalloc() as tracemalloc:
  			model.train()
  			if args.with_tracking:
  				__magic_name__	:	List[Any]			     =  0
  			for step, batch in enumerate(_snake_case     ):
  				# We need to skip steps until we reach the resumed step
  				if args.resume_from_checkpoint and epoch == 0:
  					if resume_step is not None and step < resume_step:
  						pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
  				batch.to(accelerator.device     )
  				__magic_name__	:	List[Any]			     =  model(**_snake_case     )
  				__magic_name__	:	Optional[int]			     =  outputs.loss
  				__magic_name__	:	Tuple			     =  loss / gradient_accumulation_steps
  				# We keep track of the loss at each epoch
  				if args.with_tracking:
  					total_loss += loss.detach().float()
  				accelerator.backward(_snake_case     )
  				if step % gradient_accumulation_steps == 0:
  					optimizer.step()
  					lr_scheduler.step()
  					optimizer.zero_grad()
  					# accelerator.print(lr_scheduler.get_lr())
  
  				overall_step += 1
  
  				if isinstance(_snake_case   ,     _snake_case     ):
  					__magic_name__	:	List[Any]			     =  F'''step_{overall_step}'''
  					if overall_step % checkpointing_steps == 0:
  						if args.output_dir is not None:
  							__magic_name__	:	List[str]			     =  os.path.join(args.output_dir   ,     _snake_case     )
  						accelerator.save_state(_snake_case     )
          # New Code #
          # 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     )     )     )
  		# Logging the peak memory usage of the GPU to the tracker
  		if args.with_tracking:
  			accelerator.log(
  			    {
  			        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin     ),
  			    }   ,     step=_snake_case   ,     )
  
  		# New Code #
  		# context manager to track the peak memory usage during the evaluation
  		with TorchTracemalloc() as tracemalloc:
  			model.eval()
  			for step, batch in enumerate(_snake_case     ):
  				# We could avoid this line since we set the accelerator with `device_placement=True`.
  				batch.to(accelerator.device     )
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_snake_case     )
  				__magic_name__	:	Any			     =  outputs.logits.argmax(dim=-1     )
  				__magic_name__     ,					__magic_name__	:	Any			     =  accelerator.gather_for_metrics((predictions, batch["labels"])     )
  				metric.add_batch(
  				    predictions=_snake_case   ,     references=_snake_case   ,     )
  
  			__magic_name__	:	Tuple			     =  metric.compute()
  			# Use accelerator.print to print only on the main process.
  			accelerator.print(F'''epoch {epoch}:'''   ,     _snake_case     )
  			if args.with_tracking:
  				accelerator.log(
  				    {
  				        "accuracy": eval_metric["accuracy"],
  				        "f1": eval_metric["f1"],
  				        "train_loss": total_loss.item() / len(_snake_case     ),
  				    }   ,     step=_snake_case   ,     )
  
  			if checkpointing_steps == "epoch":
  				__magic_name__	:	Dict			     =  F'''epoch_{epoch}'''
  				if args.output_dir is not None:
  					__magic_name__	:	Dict			     =  os.path.join(args.output_dir   ,     _snake_case     )
  				accelerator.save_state(_snake_case     )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  		accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin     )     )     )
  		accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used     )     )
  		accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked     )     )
  		accelerator.print(
  		    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
  		# Logging the peak memory usage of the GPU to the tracker
  		if args.with_tracking:
  			accelerator.log(
  			    {
  			        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin     ),
  			    }   ,     step=_snake_case   ,     )
  
  	if args.with_tracking:
  		accelerator.end_training()
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[str]			     =  argparse.ArgumentParser(description="Simple example of training script."     )
  	parser.add_argument(
  	    "--mixed_precision"   ,     type=_snake_case   ,     default=_snake_case   ,     choices=["no", "fp16", "bf16", "fp8"]   ,     help="Whether to use mixed precision. Choose"
  	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
  	    "and an Nvidia Ampere GPU."   ,     )
  	parser.add_argument("--cpu"   ,     action="store_true"   ,     help="If passed, will train on the CPU."     )
  	parser.add_argument(
  	    "--checkpointing_steps"   ,     type=_snake_case   ,     default=_snake_case   ,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."   ,     )
  	parser.add_argument(
  	    "--resume_from_checkpoint"   ,     type=_snake_case   ,     default=_snake_case   ,     help="If the training should continue from a checkpoint folder."   ,     )
  	parser.add_argument(
  	    "--with_tracking"   ,     action="store_true"   ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."   ,     )
  	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(
  	    "--logging_dir"   ,     type=_snake_case   ,     default="logs"   ,     help="Location on where to store experiment tracking logs`"   ,     )
  	parser.add_argument(
  	    "--model_name_or_path"   ,     type=_snake_case   ,     help="Path to pretrained model or model identifier from huggingface.co/models."   ,     required=_snake_case   ,     )
  	__magic_name__	:	str			     =  parser.parse_args()
  	__magic_name__	:	Dict			     =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  	training_function(_snake_case   ,     _snake_case     )
  
  
  if __name__ == "__main__":
  				main()
  