


import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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

def 			A_      (	__a     :				str		):

				"""simple docstring"""
				# initialize config
				if "resnet-50" in model_name:
								a__				      =		ResNetConfig.from_pretrained("""microsoft/resnet-50"""		)
				elif "resnet-101" in model_name:
								a__				      =		ResNetConfig.from_pretrained("""microsoft/resnet-101"""		)
				else:
								raise ValueError("""Model name should include either resnet50 or resnet101"""		)

				a__				      =		DetrConfig(use_timm_backbone=__A ,		backbone_config=__A		)

				# set label attributes
				a__				      =		"""panoptic""" in model_name
				if is_panoptic:
								a__				      =		250
				else:
								a__				      =		91
								a__				      =		"""huggingface/label-files"""
								a__				      =		"""coco-detection-id2label.json"""
								a__				      =		json.load(open(hf_hub_download(__A ,		__A ,		repo_type="""dataset"""		) ,		"""r"""		)		)
								a__				      =		{int(__A		): v for k, v in idalabel.items()}
								a__				      =		idalabel
								a__				      =		{v: k for k, v in idalabel.items()}

				return config, is_panoptic

def 			A_      (	__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		[]

				# stem
				# fmt: off
				rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")		)
				rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")		)
				rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")		)
				rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")		)
				rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")		)
				# stages
				for stage_idx in range(len(config.backbone_config.depths		)		):
								for layer_idx in range(config.backbone_config.depths[stage_idx]		):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    )		)
												# 3 convs
												for i in range(3		):
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )		)
    # fmt: on

				for i in range(config.encoder_layers		):
								# 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"""),
				    ]		)

				return rename_keys

def 			A_      (	__a     :				str ,		__a     :				Any ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		state_dict.pop(__A		)
				a__				      =		val

def 			A_      (	__a     :				List[str] ,		__a     :				Union[str, Any]=False		):

				"""simple docstring"""
				a__				      =		""""""
				if is_panoptic:
								a__				      =		"""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)
								a__				      =		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
								a__				      =		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
								a__				      =		in_proj_weight[:256, :]
								a__				      =		in_proj_bias[:256]
								a__				      =		in_proj_weight[256:512, :]
								a__				      =		in_proj_bias[256:512]
								a__				      =		in_proj_weight[-256:, :]
								a__				      =		in_proj_bias[-256:]
				# 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
								a__				      =		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
								a__				      =		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
								a__				      =		in_proj_weight[:256, :]
								a__				      =		in_proj_bias[:256]
								a__				      =		in_proj_weight[256:512, :]
								a__				      =		in_proj_bias[256:512]
								a__				      =		in_proj_weight[-256:, :]
								a__				      =		in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								a__				      =		state_dict.pop(
								    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''		)
								a__				      =		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
								a__				      =		in_proj_weight_cross_attn[:256, :]
								a__				      =		in_proj_bias_cross_attn[:256]
								a__				      =		in_proj_weight_cross_attn[256:512, :]
								a__				      =		in_proj_bias_cross_attn[256:512]
								a__				      =		in_proj_weight_cross_attn[-256:, :]
								a__				      =		in_proj_bias_cross_attn[-256:]

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				a__				      =		Image.open(requests.get(__A ,		stream=__A		).raw		)

				return im

@torch.no_grad()
def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Union[str, Any]=None ,		__a     :				Dict=False		):

				"""simple docstring"""
				a__				      =		get_detr_config(__A		)

				# load original model from torch hub
				a__				      =		{
				    """detr-resnet-50""": """detr_resnet50""",
				    """detr-resnet-101""": """detr_resnet101""",
				}
				logger.info(F'''Converting model {model_name}...'''		)
				a__				      =		torch.hub.load("""facebookresearch/detr""" ,		model_name_to_original_name[model_name] ,		pretrained=__A		).eval()
				a__				      =		detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(__A		):
								if is_panoptic:
												a__				      =		"""detr.""" + src
								rename_key(__A ,		__A ,		__A		)
				# query, key and value matrices need special treatment
				read_in_q_k_v(__A ,		is_panoptic=__A		)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				a__				      =		"""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"""		)
												):
																a__				      =		state_dict.pop(__A		)
																a__				      =		val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																a__				      =		state_dict.pop(__A		)
																a__				      =		val
												elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
																continue
												else:
																a__				      =		state_dict.pop(__A		)
																a__				      =		val
								else:
												if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
																a__				      =		state_dict.pop(__A		)
																a__				      =		val

    # finally, create HuggingFace model and load state dict
				a__				      =		DetrForSegmentation(__A		) if is_panoptic else DetrForObjectDetection(__A		)
				model.load_state_dict(__A		)
				model.eval()

				# verify our conversion on an image
				a__				      =		"""coco_panoptic""" if is_panoptic else """coco_detection"""
				a__				      =		DetrImageProcessor(format=__A		)

				a__				      =		processor(images=prepare_img() ,		return_tensors="""pt"""		)
				a__				      =		encoding["""pixel_values"""]

				a__				      =		detr(__A		)
				a__				      =		model(__A		)

				assert torch.allclose(outputs.logits ,		original_outputs["""pred_logits"""] ,		atol=1e-3		)
				assert torch.allclose(outputs.pred_boxes ,		original_outputs["""pred_boxes"""] ,		atol=1e-3		)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks ,		original_outputs["""pred_masks"""] ,		atol=1e-4		)
				print("""Looks ok!"""		)

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''		)
								Path(__A		).mkdir(exist_ok=__A		)
								model.save_pretrained(__A		)
								processor.save_pretrained(__A		)

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info("""Uploading PyTorch model and image processor to the hub..."""		)
								model.push_to_hub(F'''nielsr/{model_name}'''		)
								processor.push_to_hub(F'''nielsr/{model_name}'''		)


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

	parser.add_argument(
	    """--model_name""",
	    default="""detr-resnet-50""",
	    type=str,
	    choices=["""detr-resnet-50""", """detr-resnet-101"""],
	    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."""
	)
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
	UpperCAmelCase							 =					parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

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

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

								return tokenizer.pad(
								    __a ,		padding="""longest""" ,		max_length=__a ,		pad_to_multiple_of=__a ,		return_tensors="""pt""" ,		)

				# Instantiate dataloaders.
				a__				      =		DataLoader(
				    tokenized_datasets["""train"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)
				a__				      =		DataLoader(
				    tokenized_datasets["""validation"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)

				return train_dataloader, eval_dataloader


# 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_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "1":
								a__				      =		2
				# New Code #
				a__				      =		int(args.gradient_accumulation_steps		)
				a__				      =		int(args.local_sgd_steps		)
				# Initialize accelerator
				a__				      =		Accelerator(
				    cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		gradient_accumulation_steps=__a		)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				a__				      =		config["""lr"""]
				a__				      =		int(config["""num_epochs"""]		)
				a__				      =		int(config["""seed"""]		)
				a__				      =		int(config["""batch_size"""]		)

				a__				      =		evaluate.load("""glue""" ,		"""mrpc"""		)

				set_seed(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a__				      =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,		return_dict=__a		)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				a__				      =		model.to(accelerator.device		)

				# Instantiate optimizer
				a__				      =		AdamW(params=model.parameters() ,		lr=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * num_epochs) ,		)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# Now we train the model
				for epoch in range(__a		):
								model.train()
								with LocalSGD(
								    accelerator=__a ,		model=__a ,		local_sgd_steps=__a ,		enabled=local_sgd_steps is not None		) as local_sgd:
												for step, batch in enumerate(__a		):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device		)
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__a		):
																				a__				      =		model(**__a		)
																				a__				      =		output.loss
																				accelerator.backward(__a		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(__a		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																a__				      =		model(**__a		)
												a__				      =		outputs.logits.argmax(dim=-1		)
												a__      ,			a__				      =		accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
												metric.add_batch(
												    predictions=__a ,		references=__a ,		)

								a__				      =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:''' ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		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.""" ,		)
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps""" ,		type=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		default=8 ,		help="""Number of local SGD steps or None to disable local SGD"""		)
				parser.add_argument("""--cpu""" ,		action="""store_true""" ,		help="""If passed, will train on the CPU."""		)
				a__				      =		parser.parse_args()
				a__				      =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(__a ,		__a		)


if __name__ == "__main__":
	main()

