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


lowerCamelCase__        =						16
lowerCamelCase__        =						32
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			return int(x / 2**20	)







class 	A__				:






				def __enter__(		self   : List[Any]  ):






							'''simple docstring'''







							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							lowerCAmelCase__ : Optional[int]   =						torch.cuda.memory_allocated()
							lowerCAmelCase__ : List[Any]   =						psutil.Process()

							lowerCAmelCase__ : Union[str, Any]   =						self.cpu_mem_used()
							lowerCAmelCase__ : List[str]   =						True
							lowerCAmelCase__ : int   =						threading.Thread(target=self.peak_monitor_func  )
							lowerCAmelCase__ : List[Any]   =						True
							peak_monitor_thread.start()
							return self






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							return self.process.memory_info().rss






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						-1

							while True:
										lowerCAmelCase__ : List[str]   =						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[int]       ,	*a   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						False

							gc.collect()
							torch.cuda.empty_cache()
							lowerCAmelCase__ : Optional[int]   =						torch.cuda.memory_allocated()
							lowerCAmelCase__ : Tuple   =						torch.cuda.max_memory_allocated()
							lowerCAmelCase__ : Optional[Any]   =						bamb(self.end - self.begin  )
							lowerCAmelCase__ : Optional[int]   =						bamb(self.peak - self.begin  )

							lowerCAmelCase__ : Dict   =						self.cpu_mem_used()
							lowerCAmelCase__ : Optional[Any]   =						bamb(self.cpu_end - self.cpu_begin  )
							lowerCAmelCase__ : 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

					lowerCamelCase__        =						mocked_dataloaders  # noqa: F811
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'			,				lowerCamelCase_	) == "1":
						lowerCAmelCase__ : Any   =						2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			lowerCAmelCase__ : Optional[Any]   =						FullyShardedDataParallelPlugin(
			    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase_			,				ranka_only=lowerCamelCase_	)			,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase_			,				ranka_only=lowerCamelCase_	)			,				)

			# Initialize accelerator
			if args.with_tracking:
						lowerCAmelCase__ : str   =						Accelerator(
						    cpu=args.cpu			,				mixed_precision=args.mixed_precision			,				log_with='wandb'			,				project_dir=args.logging_dir			,				fsdp_plugin=lowerCamelCase_			,				)
			else:
						lowerCAmelCase__ : List[Any]   =						Accelerator(fsdp_plugin=lowerCamelCase_	)
			accelerator.print(accelerator.distributed_type	)

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

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

			lowerCAmelCase__ : Optional[int]   =						AutoTokenizer.from_pretrained(args.model_name_or_path	)
			lowerCAmelCase__ : str   =						load_dataset('glue'			,				'mrpc'	)
			lowerCAmelCase__ : Any   =						evaluate.load('glue'			,				'mrpc'	)

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

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						lowerCAmelCase__ : List[Any]   =						datasets.map(
						    lowerCamelCase_			,				batched=lowerCamelCase_			,				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
			lowerCAmelCase__ : List[str]   =						tokenized_datasets.rename_column('label'			,				'labels'	)

			# If the batch size is too big we use gradient accumulation
			lowerCAmelCase__ : Union[str, Any]   =						1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						lowerCAmelCase__ : str   =						batch_size // MAX_GPU_BATCH_SIZE
						lowerCAmelCase__ : List[str]   =						MAX_GPU_BATCH_SIZE

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

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

			# Instantiate dataloaders.
			lowerCAmelCase__ : Optional[Any]   =						DataLoader(
			    tokenized_datasets['train']			,				shuffle=lowerCamelCase_			,				collate_fn=lowerCamelCase_			,				batch_size=lowerCamelCase_	)
			lowerCAmelCase__ : str   =						DataLoader(
			    tokenized_datasets['validation']			,				shuffle=lowerCamelCase_			,				collate_fn=lowerCamelCase_			,				batch_size=lowerCamelCase_	)

			set_seed(lowerCamelCase_	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCAmelCase__ : List[str]   =						AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path			,				return_dict=lowerCamelCase_			,				low_cpu_mem_usage=lowerCamelCase_	)

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			lowerCAmelCase__ : List[str]   =						accelerator.prepare(lowerCamelCase_	)
			accelerator.print(lowerCamelCase_	)

			# 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
			lowerCAmelCase__ : Optional[Any]   =						torch.optim.AdamW(params=model.parameters()			,				lr=lowerCamelCase_			,				weight_decay=2e-4	)

			# Instantiate scheduler
			lowerCAmelCase__ : List[Any]   =						get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_			,				num_warmup_steps=10			,				num_training_steps=(len(lowerCamelCase_	) * 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.
			lowerCAmelCase__ : Tuple   =						accelerator.prepare(
			    lowerCamelCase_			,				lowerCamelCase_			,				lowerCamelCase_			,				lowerCamelCase_	)

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

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

    # Now we train the model
			for epoch in range(lowerCamelCase_	):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												lowerCAmelCase__ : str   =						0
									for step, batch in enumerate(lowerCamelCase_	):
												# 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	)
												lowerCAmelCase__ : List[Any]   =						model(**lowerCamelCase_	)
												lowerCAmelCase__ : Any   =						outputs.loss
												lowerCAmelCase__ : 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(lowerCamelCase_	)
												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(lowerCamelCase_			,				lowerCamelCase_	):
															lowerCAmelCase__ : Optional[Any]   =						F'''step_{overall_step}'''
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					lowerCAmelCase__ : int   =						os.path.join(args.output_dir			,				lowerCamelCase_	)
																		accelerator.save_state(lowerCamelCase_	)
        # 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=lowerCamelCase_			,				)

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

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

									if checkpointing_steps == "epoch":
												lowerCAmelCase__ : Optional[int]   =						F'''epoch_{epoch}'''
												if args.output_dir is not None:
															lowerCAmelCase__ : List[str]   =						os.path.join(args.output_dir			,				lowerCamelCase_	)
												accelerator.save_state(lowerCamelCase_	)
        # 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=lowerCamelCase_			,				)

			if args.with_tracking:
						accelerator.end_training()
def 					lowerCAmelCase__     (    )							->						Optional[int]:
			lowerCAmelCase__ : str   =						argparse.ArgumentParser(description='Simple example of training script.'	)
			parser.add_argument(
			    '--mixed_precision'			,				type=lowerCamelCase_			,				default=lowerCamelCase_			,				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=lowerCamelCase_			,				default=lowerCamelCase_			,				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=lowerCamelCase_			,				default=lowerCamelCase_			,				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=lowerCamelCase_			,				default='.'			,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,				)
			parser.add_argument(
			    '--logging_dir'			,				type=lowerCamelCase_			,				default='logs'			,				help='Location on where to store experiment tracking logs`'			,				)
			parser.add_argument(
			    '--model_name_or_path'			,				type=lowerCamelCase_			,				help='Path to pretrained model or model identifier from huggingface.co/models.'			,				required=lowerCamelCase_			,				)
			lowerCAmelCase__ : List[str]   =						parser.parse_args()
			lowerCAmelCase__ : Union[str, Any]   =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase_			,				lowerCamelCase_	)


if __name__ == "__main__":
					main()

"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase		:    List[str]			=       logging.get_logger(__name__)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


					'''simple docstring'''
					if "backbone.0.body" in orig_key:
										__a:    Any		     =       orig_key.replace("""backbone.0.body"""						,					"""backbone.conv_encoder.model"""			)
					if "transformer" in orig_key:
										__a:    str		     =       orig_key.replace("""transformer."""						,					""""""			)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__a:    List[str]		     =       orig_key.replace("""norm1"""						,					"""self_attn_layer_norm"""			)
										else:
															__a:    Optional[int]		     =       orig_key.replace("""norm1"""						,					"""encoder_attn_layer_norm"""			)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__a:    Union[str, Any]		     =       orig_key.replace("""norm2"""						,					"""final_layer_norm"""			)
										else:
															__a:    str		     =       orig_key.replace("""norm2"""						,					"""self_attn_layer_norm"""			)
					if "norm3" in orig_key:
										__a:    int		     =       orig_key.replace("""norm3"""						,					"""final_layer_norm"""			)
					if "linear1" in orig_key:
										__a:    Dict		     =       orig_key.replace("""linear1"""						,					"""fc1"""			)
					if "linear2" in orig_key:
										__a:    Any		     =       orig_key.replace("""linear2"""						,					"""fc2"""			)
					if "query_embed" in orig_key:
										__a:    Dict		     =       orig_key.replace("""query_embed"""						,					"""query_position_embeddings"""			)
					if "cross_attn" in orig_key:
										__a:    Dict		     =       orig_key.replace("""cross_attn"""						,					"""encoder_attn"""			)

					return orig_key


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# transformer decoder self-attention layers
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a:    Optional[Any]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
										__a:    List[str]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""			)
										# next, add query, keys and values (in that order) to the state dict
										__a:    Optional[int]		     =       in_proj_weight[:2_56, :]
										__a:    int		     =       in_proj_bias[:2_56]
										__a:    Tuple		     =       in_proj_weight[2_56:5_12, :]
										__a:    Union[str, Any]		     =       in_proj_bias[2_56:5_12]
										__a:    Optional[int]		     =       in_proj_weight[-2_56:, :]
										__a:    Any		     =       in_proj_bias[-2_56:]


def      lowerCamelCase_     (						)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    List[Any]		     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__a:    Tuple		     =       Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_			).raw			)

					return im


@torch.no_grad()
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    Optional[Any]		     =       DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__a:    Optional[int]		     =       1
					__a:    Optional[Any]		     =       dilation
					__a:    Dict		     =       with_box_refine
					__a:    Any		     =       two_stage
					# set labels
					__a:    str		     =       91
					__a:    Dict		     =       """huggingface/label-files"""
					__a:    Tuple		     =       """coco-detection-id2label.json"""
					__a:    Optional[int]		     =       json.load(open(cached_download(hf_hub_url(lowerCamelCase_						,					lowerCamelCase_						,					repo_type="""dataset"""			)			)						,					"""r"""			)			)
					__a:    Union[str, Any]		     =       {int(lowerCamelCase_			): v for k, v in idalabel.items()}
					__a:    Dict		     =       idalabel
					__a:    List[Any]		     =       {v: k for k, v in idalabel.items()}

					# load image processor
					__a:    Tuple		     =       DeformableDetrImageProcessor(format="""coco_detection"""			)

					# prepare image
					__a:    Tuple		     =       prepare_img()
					__a:    Optional[int]		     =       image_processor(images=lowerCamelCase_						,					return_tensors="""pt"""			)
					__a:    Any		     =       encoding["""pixel_values"""]

					logger.info("""Converting model..."""			)

					# load original state dict
					__a:    List[Any]		     =       torch.load(lowerCamelCase_						,					map_location="""cpu"""			)["""model"""]
					# rename keys
					for key in state_dict.copy().keys():
										__a:    Dict		     =       state_dict.pop(lowerCamelCase_			)
										__a:    List[str]		     =       val
					# query, key and value matrices need special treatment
					read_in_q_k_v(lowerCamelCase_			)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__a:    Optional[int]		     =       """model."""
					for key in state_dict.copy().keys():
										if not key.startswith("""class_embed"""			) and not key.startswith("""bbox_embed"""			):
															__a:    List[Any]		     =       state_dict.pop(lowerCamelCase_			)
															__a:    Optional[int]		     =       val
    # finally, create HuggingFace model and load state dict
					__a:    List[str]		     =       DeformableDetrForObjectDetection(lowerCamelCase_			)
					model.load_state_dict(lowerCamelCase_			)
					model.eval()

					__a:    List[str]		     =       """cuda""" if torch.cuda.is_available() else """cpu"""
					model.to(lowerCamelCase_			)
					# verify our conversion
					__a:    str		     =       model(pixel_values.to(lowerCamelCase_			)			)

					__a:    Union[str, Any]		     =       torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]			)
					__a:    Union[str, Any]		     =       torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]			)

					if single_scale:
										__a:    Optional[Any]		     =       torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]			)
										__a:    Any		     =       torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]			)

					if single_scale and dilation:
										__a:    Dict		     =       torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]			)
										__a:    int		     =       torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]			)

					if with_box_refine:
										__a:    List[str]		     =       torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]			)
										__a:    Optional[int]		     =       torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]			)

					if with_box_refine and two_stage:
										__a:    List[Any]		     =       torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]			)
										__a:    Union[str, Any]		     =       torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]			)

					print("""Logits:"""						,					outputs.logits[0, :3, :3]			)

					assert torch.allclose(outputs.logits[0, :3, :3]						,					expected_logits.to(lowerCamelCase_			)						,					atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,					expected_boxes.to(lowerCamelCase_			)						,					atol=1e-4			)

					print("""Everything ok!"""			)

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

					# Push to hub
					if push_to_hub:
										__a:    Dict		     =       """deformable-detr"""
										model_name += "-single-scale" if single_scale else ""
										model_name += "-dc5" if dilation else ""
										model_name += "-with-box-refine" if with_box_refine else ""
										model_name += "-two-stage" if two_stage else ""
										print("""Pushing model to hub..."""			)
										model.push_to_hub(repo_path_or_name=lowerCamelCase_						,					organization="""nielsr"""						,					commit_message="""Add model"""			)


if __name__ == "__main__":
			lowercase		:    int			=       argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    type=str,
			    default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
			    help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
			)
			parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
			parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
			parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
			parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the folder to output PyTorch model.',
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)
			lowercase		:    Union[str, Any]			=       parser.parse_args()
			convert_deformable_detr_checkpoint(
			    args.checkpoint_path,
			    args.single_scale,
			    args.dilation,
			    args.with_box_refine,
			    args.two_stage,
			    args.pytorch_dump_folder_path,
			    args.push_to_hub,
			)
