



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A       =[]
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
				)
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
				)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
				)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
				)
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
				)
				rename_keys.append(
				    (
				        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
				        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
				    )
				)
				rename_keys.append(
				    (
				        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
				        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
				    )
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
				)
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      state_dict.pop(__UpperCAmelCase		)
		__UpperCAmelCase       :	Tuple						=      val

def 			a		( _UpperCAmelCase				:   Optional[Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__UpperCAmelCase       :	Any						=      key.replace('''backbone.0.body'''					,  '''backbone.conv_encoder.model'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      value
				else:
						__UpperCAmelCase       :	Dict						=      value

		return new_state_dict

def 			a		( _UpperCAmelCase				:   Any					,  _UpperCAmelCase				:   Tuple=False		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      ''''''
		if is_panoptic:
				__UpperCAmelCase       :	List[Any]						=      '''detr.'''

		# first: transformer encoder
		for i in range(6		):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				__UpperCAmelCase       :	int						=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
				__UpperCAmelCase       :	List[str]						=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'		)
				# next, add query, keys and values (in that order) to the state dict
				__UpperCAmelCase       :	List[Any]						=      in_proj_weight[:2_56, :]
				__UpperCAmelCase       :	str						=      in_proj_bias[:2_56]
				__UpperCAmelCase       :	Dict						=      in_proj_weight[2_56:5_12, :]
				__UpperCAmelCase       :	Union[str, Any]						=      in_proj_bias[2_56:5_12]
				__UpperCAmelCase       :	Any						=      in_proj_weight[-2_56:, :]
				__UpperCAmelCase       :	str						=      in_proj_bias[-2_56:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6		):
				# read in weights + bias of input projection layer of self-attention
				__UpperCAmelCase       :	Optional[int]						=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
				__UpperCAmelCase       :	Any						=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'		)
				# next, add query, keys and values (in that order) to the state dict
				__UpperCAmelCase       :	Optional[int]						=      in_proj_weight[:2_56, :]
				__UpperCAmelCase       :	str						=      in_proj_bias[:2_56]
				__UpperCAmelCase       :	Dict						=      in_proj_weight[2_56:5_12, :]
				__UpperCAmelCase       :	List[str]						=      in_proj_bias[2_56:5_12]
				__UpperCAmelCase       :	Optional[Any]						=      in_proj_weight[-2_56:, :]
				__UpperCAmelCase       :	List[Any]						=      in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				__UpperCAmelCase       :	str						=      state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
				__UpperCAmelCase       :	Tuple						=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'		)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				__UpperCAmelCase       :	List[Any]						=      in_proj_weight_cross_attn[:2_56, :]
				__UpperCAmelCase       :	str						=      in_proj_bias_cross_attn[:2_56]
				__UpperCAmelCase       :	str						=      in_proj_weight_cross_attn[2_56:5_12, :]
				__UpperCAmelCase       :	Tuple						=      in_proj_bias_cross_attn[2_56:5_12]
				__UpperCAmelCase       :	Dict						=      in_proj_weight_cross_attn[-2_56:, :]
				__UpperCAmelCase       :	str						=      in_proj_bias_cross_attn[-2_56:]

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase       :	int						=      Image.open(requests.get(__UpperCAmelCase					,  stream=__UpperCAmelCase		).raw		)

		return im

@torch.no_grad()
def 			a		( _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   Tuple		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				__UpperCAmelCase       :	Any						=      '''resnet101'''
		if "dc5" in model_name:
				__UpperCAmelCase       :	List[Any]						=      True
		__UpperCAmelCase       :	Union[str, Any]						=      '''panoptic''' in model_name
		if is_panoptic:
				__UpperCAmelCase       :	Optional[int]						=      2_50
		else:
				__UpperCAmelCase       :	List[Any]						=      91
				__UpperCAmelCase       :	Optional[int]						=      '''huggingface/label-files'''
				__UpperCAmelCase       :	int						=      '''coco-detection-id2label.json'''
				__UpperCAmelCase       :	int						=      json.load(open(hf_hub_download(__UpperCAmelCase					,  __UpperCAmelCase					,  repo_type='''dataset'''		)					,  '''r'''		)		)
				__UpperCAmelCase       :	Optional[int]						=      {int(__UpperCAmelCase		): v for k, v in idalabel.items()}
				__UpperCAmelCase       :	Optional[int]						=      idalabel
				__UpperCAmelCase       :	Tuple						=      {v: k for k, v in idalabel.items()}

		# load image processor
		__UpperCAmelCase       :	Tuple						=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
		__UpperCAmelCase       :	Dict						=      DetrImageProcessor(format=__UpperCAmelCase		)

		# prepare image
		__UpperCAmelCase       :	Dict						=      prepare_img()
		__UpperCAmelCase       :	int						=      image_processor(images=__UpperCAmelCase					,  return_tensors='''pt'''		)
		__UpperCAmelCase       :	Tuple						=      encoding['''pixel_values''']

		logger.info(f'Converting model {model_name}...'		)

		# load original model from torch hub
		__UpperCAmelCase       :	Dict						=      torch.hub.load('''facebookresearch/detr'''					,  __UpperCAmelCase					,  pretrained=__UpperCAmelCase		).eval()
		__UpperCAmelCase       :	Tuple						=      detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						__UpperCAmelCase       :	Dict						=      '''detr.''' + src
				rename_key(__UpperCAmelCase					,  __UpperCAmelCase					,  __UpperCAmelCase		)
		__UpperCAmelCase       :	str						=      rename_backbone_keys(__UpperCAmelCase		)
		# query, key and value matrices need special treatment
		read_in_q_k_v(__UpperCAmelCase					,  is_panoptic=__UpperCAmelCase		)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		__UpperCAmelCase       :	Dict						=      '''detr.model.''' if is_panoptic else '''model.'''
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('''detr'''		)
						    and not key.startswith('''class_labels_classifier'''		)
						    and not key.startswith('''bbox_predictor'''		)
						):
								__UpperCAmelCase       :	Union[str, Any]						=      state_dict.pop(__UpperCAmelCase		)
								__UpperCAmelCase       :	List[str]						=      val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								__UpperCAmelCase       :	List[Any]						=      state_dict.pop(__UpperCAmelCase		)
								__UpperCAmelCase       :	int						=      val
						elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
								continue
						else:
								__UpperCAmelCase       :	Union[str, Any]						=      state_dict.pop(__UpperCAmelCase		)
								__UpperCAmelCase       :	Optional[int]						=      val
				else:
						if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
								__UpperCAmelCase       :	Dict						=      state_dict.pop(__UpperCAmelCase		)
								__UpperCAmelCase       :	Tuple						=      val
    # finally, create HuggingFace model and load state dict
		__UpperCAmelCase       :	Tuple						=      DetrForSegmentation(__UpperCAmelCase		) if is_panoptic else DetrForObjectDetection(__UpperCAmelCase		)
		model.load_state_dict(__UpperCAmelCase		)
		model.eval()
		# verify our conversion
		__UpperCAmelCase       :	Optional[int]						=      detr(__UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      model(__UpperCAmelCase		)
		assert torch.allclose(outputs.logits					,  original_outputs['''pred_logits''']					,  atol=1e-4		)
		assert torch.allclose(outputs.pred_boxes					,  original_outputs['''pred_boxes''']					,  atol=1e-4		)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks					,  original_outputs['''pred_masks''']					,  atol=1e-4		)

		# Save model and image processor
		logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
		Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
		model.save_pretrained(__UpperCAmelCase		)
		image_processor.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
				__A       =argparse.ArgumentParser()

				parser.add_argument(
				    "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
				)
				__A       =parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










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
#
########################################################################


UpperCamelCase_      			=  16
UpperCamelCase_      			=  32







def 							A     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						return int(x / 2**20      )







class 				a_    :

						def __enter__(       self      :Any)						->   Optional[Any]:
												gc.collect()
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	psutil.Process()

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	threading.Thread(target=self.peak_monitor_func)
												UpperCAmelCase_					=	True
												peak_monitor_thread.start()
												return self

						def       __a			(       self      :Tuple)						->   int:
												return self.process.memory_info().rss

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_					=	-1

												while True:
																		UpperCAmelCase_					=	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      :Optional[Any]  ,		*_lowercase      :Optional[Any])						->   List[str]:
												UpperCAmelCase_					=	False

												gc.collect()
												torch.cuda.empty_cache()
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	torch.cuda.max_memory_allocated()
												UpperCAmelCase_					=	bamb(self.end - self.begin)
												UpperCAmelCase_					=	bamb(self.peak - self.begin)

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	bamb(self.cpu_end - self.cpu_begin)
												UpperCAmelCase_					=	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

							UpperCamelCase_      			=  mocked_dataloaders  # noqa: F811







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,   __UpperCAmelCase      ) == "1":
												UpperCAmelCase_					=	2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						UpperCAmelCase_					=	FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   )

						# Initialize accelerator
						if args.with_tracking:
												UpperCAmelCase_					=	Accelerator(
												    cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with='''wandb'''				,   project_dir=args.logging_dir				,   fsdp_plugin=__UpperCAmelCase				,   )
						else:
												UpperCAmelCase_					=	Accelerator(fsdp_plugin=__UpperCAmelCase      )
						accelerator.print(accelerator.distributed_type      )

						if hasattr(args.checkpointing_steps				,   '''isdigit'''      ):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase_					=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase_					=	int(args.checkpointing_steps      )
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
						else:
												UpperCAmelCase_					=	None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_					=	config['''lr''']
						UpperCAmelCase_					=	int(config['''num_epochs''']      )
						UpperCAmelCase_					=	int(config['''seed''']      )
						UpperCAmelCase_					=	int(config['''batch_size''']      )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase_					=	vars(__UpperCAmelCase      )
												accelerator.init_trackers('''fsdp_glue_no_trainer'''				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	load_dataset('''glue'''				,   '''mrpc'''      )
						UpperCAmelCase_					=	evaluate.load('''glue'''				,   '''mrpc'''      )

						def tokenize_function(__UpperCAmelCase      ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_					=	tokenizer(examples['''sentence1''']				,   examples['''sentence2''']				,   truncation=__UpperCAmelCase				,   max_length=__UpperCAmelCase      )
												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():
												UpperCAmelCase_					=	datasets.map(
												    __UpperCAmelCase				,   batched=__UpperCAmelCase				,   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
						UpperCAmelCase_					=	tokenized_datasets.rename_column('''label'''				,   '''labels'''      )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase_					=	1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase_					=	batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase_					=	MAX_GPU_BATCH_SIZE

						def collate_fn(__UpperCAmelCase      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase_					=	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":
																		UpperCAmelCase_					=	16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase_					=	8
												else:
																		UpperCAmelCase_					=	None

												return tokenizer.pad(
												    __UpperCAmelCase				,   padding='''longest'''				,   max_length=__UpperCAmelCase				,   pad_to_multiple_of=__UpperCAmelCase				,   return_tensors='''pt'''				,   )

						# Instantiate dataloaders.
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''train''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''validation''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )

						set_seed(__UpperCAmelCase      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_					=	AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path				,   return_dict=__UpperCAmelCase				,   low_cpu_mem_usage=__UpperCAmelCase      )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						UpperCAmelCase_					=	accelerator.prepare(__UpperCAmelCase      )
						accelerator.print(__UpperCAmelCase      )

						# 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
						UpperCAmelCase_					=	torch.optim.AdamW(params=model.parameters()				,   lr=__UpperCAmelCase				,   weight_decay=2e-4      )

						# Instantiate scheduler
						UpperCAmelCase_					=	get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,   num_warmup_steps=10				,   num_training_steps=(len(__UpperCAmelCase      ) * 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.
						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.prepare(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	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      )
																		UpperCAmelCase_					=	os.path.basename(args.resume_from_checkpoint      )
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase_					=	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime      )
																		UpperCAmelCase_					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase_					=	os.path.splitext(__UpperCAmelCase      )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''				,   ''''''      )      )
																		UpperCAmelCase_					=	None
												else:
																		UpperCAmelCase_					=	int(training_difference.replace('''step_'''				,   ''''''      )      )
																		num_epochs -= resume_step // len(__UpperCAmelCase      )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		UpperCAmelCase_					=	(num_epochs * len(__UpperCAmelCase      )) - resume_step

    # Now we train the model
						for epoch in range(__UpperCAmelCase      ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								UpperCAmelCase_					=	0
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# 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      )
																								UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.loss
																								UpperCAmelCase_					=	loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(__UpperCAmelCase      )
																								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(__UpperCAmelCase				,   __UpperCAmelCase      ):
																														UpperCAmelCase_					=	f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																																				accelerator.save_state(__UpperCAmelCase      )
        # 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=__UpperCAmelCase				,   )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								with torch.no_grad():
																														UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.logits.argmax(dim=-1      )
																								UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
																								metric.add_batch(
																								    predictions=__UpperCAmelCase				,   references=__UpperCAmelCase				,   )

																		UpperCAmelCase_					=	metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"epoch {epoch}:"				,   __UpperCAmelCase      )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(__UpperCAmelCase      ),
																								    }				,   step=__UpperCAmelCase				,   )

																		if checkpointing_steps == "epoch":
																								UpperCAmelCase_					=	f"epoch_{epoch}"
																								if args.output_dir is not None:
																														UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																								accelerator.save_state(__UpperCAmelCase      )
        # 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=__UpperCAmelCase				,   )

						if args.with_tracking:
												accelerator.end_training()







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	argparse.ArgumentParser(description='''Simple example of training script.'''      )
						parser.add_argument(
						    '''--mixed_precision'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   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=__UpperCAmelCase				,   default=__UpperCAmelCase				,   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=__UpperCAmelCase				,   default=__UpperCAmelCase				,   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=__UpperCAmelCase				,   default='''.'''				,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,   )
						parser.add_argument(
						    '''--logging_dir'''				,   type=__UpperCAmelCase				,   default='''logs'''				,   help='''Location on where to store experiment tracking logs`'''				,   )
						parser.add_argument(
						    '''--model_name_or_path'''				,   type=__UpperCAmelCase				,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,   required=__UpperCAmelCase				,   )
						UpperCAmelCase_					=	parser.parse_args()
						UpperCAmelCase_					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							main()





