


import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE      :						Union[str, Any]    =		logging.get_logger(__name__)






def   __lowerCamelCase	(       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	Optional[Any]=False     ,       lowerCamelCase__		:	str=False			):





			'''simple docstring'''




			lowerCamelCase								=     """backbone.""" if is_semantic else """"""

			lowerCamelCase								=     []
			for i in range(config.num_hidden_layers			):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')			)
						rename_keys.append(
						    (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')			)
						rename_keys.append(
						    (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')			)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        (f'{prefix}cls_token', """beit.embeddings.cls_token"""),
			        (f'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
			        (f'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
			    ]			)

			if has_lm_head:
						# mask token + shared relative position bias + layernorm
						rename_keys.extend(
						    [
						        ("""mask_token""", """beit.embeddings.mask_token"""),
						        (
						            """rel_pos_bias.relative_position_bias_table""",
						            """beit.encoder.relative_position_bias.relative_position_bias_table""",
						        ),
						        (
						            """rel_pos_bias.relative_position_index""",
						            """beit.encoder.relative_position_bias.relative_position_index""",
						        ),
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						    ]			)
			elif is_semantic:
						# semantic segmentation classification heads
						rename_keys.extend(
						    [
						        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
						        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
						        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
						        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
						    ]			)
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
						        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ]			)

			return rename_keys






def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	List[str]=False     ,       lowerCamelCase__		:	List[Any]=False			):





			'''simple docstring'''




			for i in range(config.num_hidden_layers			):
						lowerCamelCase								=     """backbone.""" if is_semantic else """"""
						# queries, keys and values
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'			)

						lowerCamelCase								=     in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase								=     q_bias
						lowerCamelCase								=     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase								=     in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase								=     v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.gamma_1'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.gamma_2'			)

						lowerCamelCase								=     gamma_a
						lowerCamelCase								=     gamma_a

						# relative_position bias table + index
						if not has_lm_head:
									# each layer has its own relative position bias
									lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
									lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'			)

									lowerCamelCase								=     table
									lowerCamelCase								=     index






def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]     ,       lowerCamelCase__		:	Union[str, Any]     ,       lowerCamelCase__		:	Union[str, Any]			):





			'''simple docstring'''




			lowerCamelCase								=     dct.pop(lowerCamelCase__			)
			lowerCamelCase								=     val






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase								=     Image.open(requests.get(lowerCamelCase__     ,       stream=lowerCamelCase__			).raw			)
			return im






@torch.no_grad()
def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	Tuple			):





			'''simple docstring'''




			lowerCamelCase								=     BeitConfig()
			lowerCamelCase								=     False
			lowerCamelCase								=     False
			lowerCamelCase								=     """huggingface/label-files"""
			# set config parameters based on URL
			if checkpoint_url[-9:-4] == "pt22k":
						# masked image modeling
						lowerCamelCase								=     True
						lowerCamelCase								=     True
						lowerCamelCase								=     True
			elif checkpoint_url[-9:-4] == "ft22k":
						# intermediate fine-tuning on ImageNet-22k
						lowerCamelCase								=     True
						lowerCamelCase								=     21841
						lowerCamelCase								=     """imagenet-22k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						# this dataset contains 21843 labels but the model only has 21841
						# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
						del idalabel[9205]
						del idalabel[15027]
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
			elif checkpoint_url[-8:-4] == "to1k":
						# fine-tuning on ImageNet-1k
						lowerCamelCase								=     True
						lowerCamelCase								=     1000
						lowerCamelCase								=     """imagenet-1k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
						if "384" in checkpoint_url:
									lowerCamelCase								=     384
						if "512" in checkpoint_url:
									lowerCamelCase								=     512
			elif "ade20k" in checkpoint_url:
						# fine-tuning
						lowerCamelCase								=     True
						lowerCamelCase								=     150
						lowerCamelCase								=     """ade20k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
						lowerCamelCase								=     640
						lowerCamelCase								=     True
			else:
						raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""			)

			# size of the architecture
			if "base" in checkpoint_url:
						pass
			elif "large" in checkpoint_url:
						lowerCamelCase								=     1024
						lowerCamelCase								=     4096
						lowerCamelCase								=     24
						lowerCamelCase								=     16
						if "ade20k" in checkpoint_url:
									lowerCamelCase								=     640
									lowerCamelCase								=     [7, 11, 15, 23]
			else:
						raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""			)

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase								=     torch.hub.load_state_dict_from_url(lowerCamelCase__     ,       map_location="""cpu"""     ,       check_hash=lowerCamelCase__			)
			lowerCamelCase								=     state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

			lowerCamelCase								=     create_rename_keys(lowerCamelCase__     ,       has_lm_head=lowerCamelCase__     ,       is_semantic=lowerCamelCase__			)
			for src, dest in rename_keys:
						rename_key(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
			read_in_q_k_v(lowerCamelCase__     ,       lowerCamelCase__     ,       has_lm_head=lowerCamelCase__     ,       is_semantic=lowerCamelCase__			)
			if is_semantic:
						# add prefix to decoder keys
						for key, val in state_dict.copy().items():
									lowerCamelCase								=     state_dict.pop(lowerCamelCase__			)
									if key.startswith("""backbone.fpn"""			):
												lowerCamelCase								=     key.replace("""backbone.fpn"""     ,       """fpn"""			)
									lowerCamelCase								=     val

    # load HuggingFace model
			if checkpoint_url[-9:-4] == "pt22k":
						lowerCamelCase								=     BeitForMaskedImageModeling(lowerCamelCase__			)
			elif "ade20k" in checkpoint_url:
						lowerCamelCase								=     BeitForSemanticSegmentation(lowerCamelCase__			)
			else:
						lowerCamelCase								=     BeitForImageClassification(lowerCamelCase__			)
			model.eval()
			model.load_state_dict(lowerCamelCase__			)

			# Check outputs on an image
			if is_semantic:
						lowerCamelCase								=     BeitImageProcessor(size=config.image_size     ,       do_center_crop=lowerCamelCase__			)
						lowerCamelCase								=     load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,       split="""test"""			)
						lowerCamelCase								=     Image.open(ds[0]["""file"""]			)
			else:
						lowerCamelCase								=     BeitImageProcessor(
						    size=config.image_size     ,       resample=PILImageResampling.BILINEAR     ,       do_center_crop=lowerCamelCase__			)
						lowerCamelCase								=     prepare_img()

			lowerCamelCase								=     image_processor(images=lowerCamelCase__     ,       return_tensors="""pt"""			)
			lowerCamelCase								=     encoding["""pixel_values"""]

			lowerCamelCase								=     model(lowerCamelCase__			)
			lowerCamelCase								=     outputs.logits

			# verify logits
			lowerCamelCase								=     torch.Size([1, 1000]			)
			if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
						lowerCamelCase								=     torch.Size([1, 196, 8192]			)
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
						lowerCamelCase								=     torch.Size([1, 196, 8192]			)
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
						lowerCamelCase								=     torch.Size([1, 21841]			)
						lowerCamelCase								=     torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]			)
						lowerCamelCase								=     2397
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
						lowerCamelCase								=     torch.Size([1, 21841]			)
						lowerCamelCase								=     torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)
						lowerCamelCase								=     2396
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
						lowerCamelCase								=     torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]			)
						lowerCamelCase								=     285
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)
						lowerCamelCase								=     281
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
						lowerCamelCase								=     torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
						lowerCamelCase								=     (1, 150, 160, 160)
						lowerCamelCase								=     torch.tensor(
						    [
						        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
						        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
						        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
						    ]			)
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
						lowerCamelCase								=     (1, 150, 160, 160)
						lowerCamelCase								=     torch.tensor(
						    [
						        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
						        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
						        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
						    ]			)
			else:
						raise ValueError("""Can't verify logits as model is not supported"""			)

			assert logits.shape == expected_shape, "Shape of logits not as expected"
			if not has_lm_head:
						if is_semantic:
									assert torch.allclose(
									    logits[0, :3, :3, :3]     ,       lowerCamelCase__     ,       atol=1E-3			), "First elements of logits not as expected"
						else:
									print("""Predicted class idx:"""     ,       logits.argmax(-1			).item()			)
									assert torch.allclose(
									    logits[0, :3]     ,       lowerCamelCase__     ,       atol=1E-3			), "First elements of logits not as expected"
									assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

			Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
			print(f'Saving model to {pytorch_dump_folder_path}'			)
			model.save_pretrained(lowerCamelCase__			)
			print(f'Saving image processor to {pytorch_dump_folder_path}'			)
			image_processor.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE      :						Any    =		argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_url",
		    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
		    type=str,
		    help="URL to the original PyTorch checkpoint (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		SCREAMING_SNAKE_CASE      :						Any    =		parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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      :						str    =		16
UpperCAmelCase      :						Union[str, Any]    =		32






def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):





			'''simple docstring'''




			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)

			def tokenize_function(lowerCamelCase__		:	List[str]			):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase								=     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								=     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								=     tokenized_datasets.rename_column("""label"""     ,       """labels"""			)

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

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

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

			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      :						Tuple    =		mocked_dataloaders  # noqa: F811






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Optional[int]			):





			'''simple docstring'''




			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "1":
						lowerCamelCase								=     2
			# Initialize accelerator
			lowerCamelCase								=     Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase								=     config["""lr"""]
			lowerCamelCase								=     int(config["""num_epochs"""]			)
			lowerCamelCase								=     int(config["""seed"""]			)
			lowerCamelCase								=     int(config["""batch_size"""]			)

			# New Code #
			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps     ,       """isdigit"""			):
						if args.checkpointing_steps == "epoch":
									lowerCamelCase								=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase								=     int(args.checkpointing_steps			)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
			else:
						lowerCamelCase								=     None

			set_seed(lowerCamelCase__			)

			lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
			lowerCamelCase								=     evaluate.load("""glue"""     ,       """mrpc"""			)

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

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

			# 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).
			lowerCamelCase								=     model.to(accelerator.device			)

			# Instantiate optimizer
			lowerCamelCase								=     AdamW(params=model.parameters()     ,       lr=lowerCamelCase__			)

			# Instantiate scheduler
			lowerCamelCase								=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * num_epochs) // gradient_accumulation_steps     ,       )

			# 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.
			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
			    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase								=     0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase								=     0

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			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								=     os.path.basename(args.resume_from_checkpoint			)
						else:
									# Get the most recent checkpoint
									lowerCamelCase								=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
									dirs.sort(key=os.path.getctime			)
									lowerCamelCase								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCamelCase								=     os.path.splitext(lowerCamelCase__			)[0]

						if "epoch" in training_difference:
									lowerCamelCase								=     int(training_difference.replace("""epoch_"""     ,       """"""			)			) + 1
									lowerCamelCase								=     None
						else:
									lowerCamelCase								=     int(training_difference.replace("""step_"""     ,       """"""			)			)
									lowerCamelCase								=     resume_step // len(lowerCamelCase__			)
									resume_step -= starting_epoch * len(lowerCamelCase__			)

    # Now we train the model
			for epoch in range(lowerCamelCase__     ,       lowerCamelCase__			):
						model.train()
						# New Code #
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									lowerCamelCase								=     accelerator.skip_first_batches(lowerCamelCase__     ,       lowerCamelCase__			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCamelCase								=     train_dataloader
						for step, batch in enumerate(lowerCamelCase__			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device			)
									lowerCamelCase								=     model(**lowerCamelCase__			)
									lowerCamelCase								=     outputs.loss
									lowerCamelCase								=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__			)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
												lowerCamelCase								=     f'step_{overall_step}'
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
															accelerator.save_state(lowerCamelCase__			)

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

						lowerCamelCase								=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:'     ,       lowerCamelCase__			)

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									lowerCamelCase								=     f'epoch_{epoch}'
									if args.output_dir is not None:
												lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
									accelerator.save_state(lowerCamelCase__			)






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     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(
			    """--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(
			    """--resume_from_checkpoint"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""If the training should continue from a checkpoint folder."""     ,       )
			lowerCamelCase								=     parser.parse_args()
			lowerCamelCase								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__     ,       lowerCamelCase__			)


if __name__ == "__main__":
		main()

