





"""simple docstring"""





import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =384
							__lowerCamelCase   :   Optional[Any]       =7
							if "tiny" in model_name:
														__lowerCamelCase   :   Tuple       =96
														__lowerCamelCase   :   Dict       =(2, 2, 6, 2)
														__lowerCamelCase   :   str       =(3, 6, 12, 24)
							elif "small" in model_name:
														__lowerCamelCase   :   Dict       =96
														__lowerCamelCase   :   Union[str, Any]       =(2, 2, 18, 2)
														__lowerCamelCase   :   Any       =(3, 6, 12, 24)
							elif "base" in model_name:
														__lowerCamelCase   :   Any       =128
														__lowerCamelCase   :   str       =(2, 2, 18, 2)
														__lowerCamelCase   :   List[Any]       =(4, 8, 16, 32)
														__lowerCamelCase   :   List[str]       =12
														__lowerCamelCase   :   List[Any]       =512
							elif "large" in model_name:
														__lowerCamelCase   :   Dict       =192
														__lowerCamelCase   :   Optional[int]       =(2, 2, 18, 2)
														__lowerCamelCase   :   Tuple       =(6, 12, 24, 48)
														__lowerCamelCase   :   str       =12
														__lowerCamelCase   :   Any       =768

							# set label information
							__lowerCamelCase   :   int       =150
							__lowerCamelCase   :   Union[str, Any]       ='''huggingface/label-files'''
							__lowerCamelCase   :   int       ='''ade20k-id2label.json'''
							__lowerCamelCase   :   Optional[Any]       =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    repo_type='''dataset''' )     ,    '''r''' ) )
							__lowerCamelCase   :   Dict       ={int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
							__lowerCamelCase   :   Union[str, Any]       ={v: k for k, v in idalabel.items()}

							__lowerCamelCase   :   Any       =SwinConfig(
							    embed_dim=SCREAMING_SNAKE_CASE     ,    depths=SCREAMING_SNAKE_CASE     ,    num_heads=SCREAMING_SNAKE_CASE     ,    window_size=SCREAMING_SNAKE_CASE     ,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     ,    )
							__lowerCamelCase   :   Tuple       =UperNetConfig(
							    backbone_config=SCREAMING_SNAKE_CASE     ,    auxiliary_in_channels=SCREAMING_SNAKE_CASE     ,    num_labels=SCREAMING_SNAKE_CASE     ,    idalabel=SCREAMING_SNAKE_CASE     ,    labelaid=SCREAMING_SNAKE_CASE     ,    )

							return config



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =[]

							# fmt: off
							# stem
							rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''') )
							rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''') )
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''') )
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''') )
							# stages
							for i in range(len(config.backbone_config.depths ) ):
														for j in range(config.backbone_config.depths[i] ):
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
																					rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias') )

														if i < 3:
																					rename_keys.append((F'backbone.stages.{i}.downsample.reduction.weight', F'backbone.encoder.layers.{i}.downsample.reduction.weight') )
																					rename_keys.append((F'backbone.stages.{i}.downsample.norm.weight', F'backbone.encoder.layers.{i}.downsample.norm.weight') )
																					rename_keys.append((F'backbone.stages.{i}.downsample.norm.bias', F'backbone.encoder.layers.{i}.downsample.norm.bias') )
														rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight') )
														rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias') )

							# decode head
							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'''),
							    ] )
							# fmt: on

							return rename_keys



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[str]     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Dict ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =dct.pop(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   str       =val



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Dict ):


							'''simple docstring'''







							__lowerCamelCase   :   Tuple       =[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
							for i in range(len(backbone_config.depths ) ):
														__lowerCamelCase   :   Union[str, Any]       =num_features[i]
														for j in range(backbone_config.depths[i] ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					__lowerCamelCase   :   Dict       =state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight' )
																					__lowerCamelCase   :   int       =state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias' )
																					# next, add query, keys and values (in that order) to the state dict
																					__lowerCamelCase   :   List[str]       =in_proj_weight[:dim, :]
																					__lowerCamelCase   :   Optional[int]       =in_proj_bias[: dim]
																					__lowerCamelCase   :   int       =in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__lowerCamelCase   :   Tuple       =in_proj_bias[
																					    dim : dim * 2
																					]
																					__lowerCamelCase   :   List[Any]       =in_proj_weight[
																					    -dim :, :
																					]
																					__lowerCamelCase   :   Dict       =in_proj_bias[-dim :]
																					# fmt: on



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[int] ):


							'''simple docstring'''







							__lowerCamelCase     ,    __lowerCamelCase   :   int       =x.shape
							__lowerCamelCase   :   List[str]       =x.reshape(SCREAMING_SNAKE_CASE     ,    4     ,    in_channel // 4 )
							__lowerCamelCase   :   Optional[Any]       =x[:, [0, 2, 1, 3], :].transpose(1     ,    2 ).reshape(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							return x



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[Any] ):


							'''simple docstring'''







							__lowerCamelCase     ,    __lowerCamelCase   :   int       =x.shape
							__lowerCamelCase   :   Optional[Any]       =x.reshape(SCREAMING_SNAKE_CASE     ,    in_channel // 4     ,    4 )
							__lowerCamelCase   :   Optional[int]       =x[:, :, [0, 2, 1, 3]].transpose(1     ,    2 ).reshape(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							return x



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =x.shape[0]
							__lowerCamelCase   :   int       =x.reshape(4     ,    in_channel // 4 )
							__lowerCamelCase   :   List[Any]       =x[[0, 2, 1, 3], :].transpose(0     ,    1 ).reshape(SCREAMING_SNAKE_CASE )
							return x



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   Any       =x.shape[0]
							__lowerCamelCase   :   Union[str, Any]       =x.reshape(in_channel // 4     ,    4 )
							__lowerCamelCase   :   Optional[int]       =x[:, [0, 2, 1, 3]].transpose(0     ,    1 ).reshape(SCREAMING_SNAKE_CASE )
							return x



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				Optional[int] ):


							'''simple docstring'''







							__lowerCamelCase   :   List[Any]       ={
							    '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
							    '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
							    '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
							    '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
							}
							__lowerCamelCase   :   Any       =model_name_to_url[model_name]
							__lowerCamelCase   :   Union[str, Any]       =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE     ,    map_location='''cpu'''     ,    file_name=SCREAMING_SNAKE_CASE )[
							    '''state_dict'''
							]

							for name, param in state_dict.items():
														print(SCREAMING_SNAKE_CASE     ,    param.shape )

							__lowerCamelCase   :   Union[str, Any]       =get_upernet_config(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   str       =UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE )
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														__lowerCamelCase   :   int       =state_dict.pop(SCREAMING_SNAKE_CASE )
														if "bn" in key:
																					__lowerCamelCase   :   Dict       =key.replace('''bn'''     ,    '''batch_norm''' )
														__lowerCamelCase   :   Optional[Any]       =val

							# rename keys
							__lowerCamelCase   :   Optional[Any]       =create_rename_keys(SCREAMING_SNAKE_CASE )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							read_in_q_k_v(SCREAMING_SNAKE_CASE     ,    config.backbone_config )

							# fix downsample parameters
							for key, value in state_dict.items():
														if "downsample" in key:
																					if "reduction" in key:
																												__lowerCamelCase   :   int       =reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE )
																					if "norm" in key:
																												__lowerCamelCase   :   List[Any]       =reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE )

							model.load_state_dict(SCREAMING_SNAKE_CASE )

							# verify on image
							__lowerCamelCase   :   Optional[int]       ='''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
							__lowerCamelCase   :   Optional[Any]       =Image.open(requests.get(SCREAMING_SNAKE_CASE     ,    stream=SCREAMING_SNAKE_CASE ).raw ).convert('''RGB''' )

							__lowerCamelCase   :   Any       =SegformerImageProcessor()
							__lowerCamelCase   :   int       =processor(SCREAMING_SNAKE_CASE     ,    return_tensors='''pt''' ).pixel_values

							with torch.no_grad():
														__lowerCamelCase   :   List[str]       =model(SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Any       =outputs.logits

							print(logits.shape )
							print('''First values of logits:'''     ,    logits[0, 0, :3, :3] )
							# assert values
							if model_name == "upernet-swin-tiny":
														__lowerCamelCase   :   str       =torch.tensor(
														    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] )
							elif model_name == "upernet-swin-small":
														__lowerCamelCase   :   Optional[int]       =torch.tensor(
														    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]] )
							elif model_name == "upernet-swin-base":
														__lowerCamelCase   :   Tuple       =torch.tensor(
														    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]] )
							elif model_name == "upernet-swin-large":
														__lowerCamelCase   :   List[Any]       =torch.tensor(
														    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]] )
							print('''Logits:'''     ,    outputs.logits[0, 0, :3, :3] )
							assert torch.allclose(outputs.logits[0, 0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=1E-4 )
							print('''Looks ok!''' )

							if pytorch_dump_folder_path is not None:
														print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
														model.save_pretrained(SCREAMING_SNAKE_CASE )
														print(F'Saving processor to {pytorch_dump_folder_path}' )
														processor.save_pretrained(SCREAMING_SNAKE_CASE )

							if push_to_hub:
														print(F'Pushing model and processor for {model_name} to hub' )
														model.push_to_hub(F'openmmlab/{model_name}' )
														processor.push_to_hub(F'openmmlab/{model_name}' )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='upernet-swin-tiny',
							    type=str,
							    choices=[f'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
							    help='Name of the Swin + UperNet model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							_UpperCamelCase					      =   parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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					      =   16
_UpperCamelCase					      =   32



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Accelerator     ,    SCREAMING_SNAKE_CASE							:				DatasetDict     ,    SCREAMING_SNAKE_CASE							:				List[int]     ,    SCREAMING_SNAKE_CASE							:				List[int]     ,    SCREAMING_SNAKE_CASE							:				int = 16 ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =AutoTokenizer.from_pretrained('''bert-base-cased''' )
							__lowerCamelCase   :   List[str]       =DatasetDict(
							    {
							        '''train''': dataset['''train'''].select(SCREAMING_SNAKE_CASE ),
							        '''validation''': dataset['''train'''].select(SCREAMING_SNAKE_CASE ),
							        '''test''': dataset['''validation'''],
							    } )

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

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														__lowerCamelCase   :   Optional[Any]       =datasets.map(
														    SCREAMING_SNAKE_CASE     ,    batched=SCREAMING_SNAKE_CASE     ,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,    )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							__lowerCamelCase   :   Dict       =tokenized_datasets.rename_column('''label'''     ,    '''labels''' )

							def collate_fn(SCREAMING_SNAKE_CASE							:				List[str] ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__lowerCamelCase   :   Tuple       =128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					__lowerCamelCase   :   List[str]       =16
														elif accelerator.mixed_precision != "no":
																					__lowerCamelCase   :   Tuple       =8
														else:
																					__lowerCamelCase   :   Optional[int]       =None

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

							# Instantiate dataloaders.
							__lowerCamelCase   :   int       =DataLoader(
							    tokenized_datasets['''train''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Optional[Any]       =DataLoader(
							    tokenized_datasets['''validation''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Tuple       =DataLoader(
							    tokenized_datasets['''test''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE )

							return train_dataloader, eval_dataloader, test_dataloader



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =[]
							# Download the dataset
							__lowerCamelCase   :   Any       =load_dataset('''glue'''     ,    '''mrpc''' )
							# Create our splits
							__lowerCamelCase   :   Optional[Any]       =StratifiedKFold(n_splits=int(args.num_folds ) )
							# Initialize accelerator
							__lowerCamelCase   :   str       =Accelerator(cpu=args.cpu     ,    mixed_precision=args.mixed_precision )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowerCamelCase   :   Any       =config['''lr''']
							__lowerCamelCase   :   int       =int(config['''num_epochs'''] )
							__lowerCamelCase   :   Optional[int]       =int(config['''seed'''] )
							__lowerCamelCase   :   Any       =int(config['''batch_size'''] )

							__lowerCamelCase   :   List[str]       =evaluate.load('''glue'''     ,    '''mrpc''' )

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

							set_seed(SCREAMING_SNAKE_CASE )

							# New Code #
							# Create our folds:
							__lowerCamelCase   :   str       =kfold.split(np.zeros(datasets['''train'''].num_rows )     ,    datasets['''train''']['''label'''] )
							__lowerCamelCase   :   Tuple       =[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(SCREAMING_SNAKE_CASE ):
														__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Any       =get_fold_dataloaders(
														    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    )
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														__lowerCamelCase   :   Optional[int]       =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,    return_dict=SCREAMING_SNAKE_CASE )

														# 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   :   Optional[Any]       =model.to(accelerator.device )

														# Instantiate optimizer
														__lowerCamelCase   :   Any       =AdamW(params=model.parameters()     ,    lr=SCREAMING_SNAKE_CASE )

														# Instantiate scheduler
														__lowerCamelCase   :   Dict       =get_linear_schedule_with_warmup(
														    optimizer=SCREAMING_SNAKE_CASE     ,    num_warmup_steps=100     ,    num_training_steps=(len(SCREAMING_SNAKE_CASE ) * 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   :   Any       =accelerator.prepare(
														    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														# Now we train the model
														for epoch in range(SCREAMING_SNAKE_CASE ):
																					model.train()
																					for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device )
																												__lowerCamelCase   :   Dict       =model(**SCREAMING_SNAKE_CASE )
																												__lowerCamelCase   :   Optional[Any]       =outputs.loss
																												__lowerCamelCase   :   int       =loss / gradient_accumulation_steps
																												accelerator.backward(SCREAMING_SNAKE_CASE )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

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

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

														# New Code #
														# We also run predictions on the test set at the very end
														__lowerCamelCase   :   Tuple       =[]
														for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device )
																					with torch.no_grad():
																												__lowerCamelCase   :   List[Any]       =model(**SCREAMING_SNAKE_CASE )
																					__lowerCamelCase   :   int       =outputs.logits
																					__lowerCamelCase     ,    __lowerCamelCase   :   Union[str, Any]       =accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																					fold_predictions.append(predictions.cpu() )
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(SCREAMING_SNAKE_CASE     ,    dim=0 ) )
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							__lowerCamelCase   :   Optional[Any]       =torch.cat(SCREAMING_SNAKE_CASE     ,    dim=0 )
							__lowerCamelCase   :   int       =torch.stack(SCREAMING_SNAKE_CASE     ,    dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
							__lowerCamelCase   :   Tuple       =metric.compute(predictions=SCREAMING_SNAKE_CASE     ,    references=SCREAMING_SNAKE_CASE )
							accelerator.print('''Average test metrics from all folds:'''     ,    SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =argparse.ArgumentParser(description='''Simple example of training script.''' )
							parser.add_argument(
							    '''--mixed_precision'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=SCREAMING_SNAKE_CASE     ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''     ,    )
							parser.add_argument('''--cpu'''     ,    action='''store_true'''     ,    help='''If passed, will train on the CPU.''' )
							# New Code #
							parser.add_argument('''--num_folds'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=3     ,    help='''The number of splits to perform across the dataset''' )
							__lowerCamelCase   :   Any       =parser.parse_args()
							__lowerCamelCase   :   Optional[int]       ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							main()




