"""simple docstring"""





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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
       _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    [144, 192, 240]
              _SCREAMING_SNAKE_CASE		:						str			  =    [16, 32, 64, 96, 128, 160, 640]
       elif "mobilevit_xs" in mobilevit_name:
              _SCREAMING_SNAKE_CASE		:						List[Any]			  =    [96, 120, 144]
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [16, 32, 48, 64, 80, 96, 384]
       elif "mobilevit_xxs" in mobilevit_name:
              _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [64, 80, 96]
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    [16, 16, 24, 48, 64, 80, 320]
              _SCREAMING_SNAKE_CASE		:						Any			  =    0.05
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    2.0

       if mobilevit_name.startswith("""deeplabv3_"""  ):
              _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    512
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    16
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    21
              _SCREAMING_SNAKE_CASE		:						Dict			  =    """pascal-voc-id2label.json"""
       else:
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_000
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    """imagenet-1k-id2label.json"""

       _SCREAMING_SNAKE_CASE		:						Tuple			  =    """huggingface/label-files"""
       _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    json.load(open(hf_hub_download(__A     ,	__A     ,	repo_type="""dataset"""  )     ,	"""r"""  )  )
       _SCREAMING_SNAKE_CASE		:						Dict			  =    {int(__A  ): v for k, v in idalabel.items()}
       _SCREAMING_SNAKE_CASE		:						Tuple			  =    idalabel
       _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {v: k for k, v in idalabel.items()}

       return config



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
       for i in range(1     ,	6  ):
              if F"""layer_{i}.""" in name:
                     _SCREAMING_SNAKE_CASE		:						Tuple			  =    name.replace(F"""layer_{i}."""     ,	F"""encoder.layer.{i - 1}."""  )

       if "conv_1." in name:
              _SCREAMING_SNAKE_CASE		:						Any			  =    name.replace("""conv_1."""     ,	"""conv_stem."""  )
       if ".block." in name:
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    name.replace(""".block."""     ,	"""."""  )

       if "exp_1x1" in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace("""exp_1x1"""     ,	"""expand_1x1"""  )
       if "red_1x1" in name:
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    name.replace("""red_1x1"""     ,	"""reduce_1x1"""  )
       if ".local_rep.conv_3x3." in name:
              _SCREAMING_SNAKE_CASE		:						List[Any]			  =    name.replace(""".local_rep.conv_3x3."""     ,	""".conv_kxk."""  )
       if ".local_rep.conv_1x1." in name:
              _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    name.replace(""".local_rep.conv_1x1."""     ,	""".conv_1x1."""  )
       if ".norm." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(""".norm."""     ,	""".normalization."""  )
       if ".conv." in name:
              _SCREAMING_SNAKE_CASE		:						List[Any]			  =    name.replace(""".conv."""     ,	""".convolution."""  )
       if ".conv_proj." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(""".conv_proj."""     ,	""".conv_projection."""  )

       for i in range(0     ,	2  ):
              for j in range(0     ,	4  ):
                     if F""".{i}.{j}.""" in name:
                            _SCREAMING_SNAKE_CASE		:						str			  =    name.replace(F""".{i}.{j}."""     ,	F""".{i}.layer.{j}."""  )

       for i in range(2     ,	6  ):
              for j in range(0     ,	4  ):
                     if F""".{i}.{j}.""" in name:
                            _SCREAMING_SNAKE_CASE		:						Dict			  =    name.replace(F""".{i}.{j}."""     ,	F""".{i}."""  )
                            if "expand_1x1" in name:
                                   _SCREAMING_SNAKE_CASE		:						List[Any]			  =    name.replace("""expand_1x1"""     ,	"""downsampling_layer.expand_1x1"""  )
                            if "conv_3x3" in name:
                                   _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    name.replace("""conv_3x3"""     ,	"""downsampling_layer.conv_3x3"""  )
                            if "reduce_1x1" in name:
                                   _SCREAMING_SNAKE_CASE		:						int			  =    name.replace("""reduce_1x1"""     ,	"""downsampling_layer.reduce_1x1"""  )

       for i in range(2     ,	5  ):
              if F""".global_rep.{i}.weight""" in name:
                     _SCREAMING_SNAKE_CASE		:						int			  =    name.replace(F""".global_rep.{i}.weight"""     ,	""".layernorm.weight"""  )
              if F""".global_rep.{i}.bias""" in name:
                     _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(F""".global_rep.{i}.bias"""     ,	""".layernorm.bias"""  )

       if ".global_rep." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(""".global_rep."""     ,	""".transformer."""  )
       if ".pre_norm_mha.0." in name:
              _SCREAMING_SNAKE_CASE		:						Tuple			  =    name.replace(""".pre_norm_mha.0."""     ,	""".layernorm_before."""  )
       if ".pre_norm_mha.1.out_proj." in name:
              _SCREAMING_SNAKE_CASE		:						Dict			  =    name.replace(""".pre_norm_mha.1.out_proj."""     ,	""".attention.output.dense."""  )
       if ".pre_norm_ffn.0." in name:
              _SCREAMING_SNAKE_CASE		:						int			  =    name.replace(""".pre_norm_ffn.0."""     ,	""".layernorm_after."""  )
       if ".pre_norm_ffn.1." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    name.replace(""".pre_norm_ffn.1."""     ,	""".intermediate.dense."""  )
       if ".pre_norm_ffn.4." in name:
              _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    name.replace(""".pre_norm_ffn.4."""     ,	""".output.dense."""  )
       if ".transformer." in name:
              _SCREAMING_SNAKE_CASE		:						str			  =    name.replace(""".transformer."""     ,	""".transformer.layer."""  )

       if ".aspp_layer." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(""".aspp_layer."""     ,	"""."""  )
       if ".aspp_pool." in name:
              _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    name.replace(""".aspp_pool."""     ,	"""."""  )
       if "seg_head." in name:
              _SCREAMING_SNAKE_CASE		:						int			  =    name.replace("""seg_head."""     ,	"""segmentation_head."""  )
       if "segmentation_head.classifier.classifier." in name:
              _SCREAMING_SNAKE_CASE		:						List[str]			  =    name.replace("""segmentation_head.classifier.classifier."""     ,	"""segmentation_head.classifier."""  )

       if "classifier.fc." in name:
              _SCREAMING_SNAKE_CASE		:						Any			  =    name.replace("""classifier.fc."""     ,	"""classifier."""  )
       elif (not base_model) and ("segmentation_head." not in name):
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    """mobilevit.""" + name

       return name



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    int:
       if base_model:
              _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """"""
       else:
              _SCREAMING_SNAKE_CASE		:						Dict			  =    """mobilevit."""

       for key in orig_state_dict.copy().keys():
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    orig_state_dict.pop(__A  )

              if key[:8] == "encoder.":
                     _SCREAMING_SNAKE_CASE		:						str			  =    key[8:]

              if "qkv" in key:
                     _SCREAMING_SNAKE_CASE		:						List[str]			  =    key.split("""."""  )
                     _SCREAMING_SNAKE_CASE		:						List[Any]			  =    int(key_split[0][6:]  ) - 1
                     _SCREAMING_SNAKE_CASE		:						str			  =    int(key_split[3]  )
                     _SCREAMING_SNAKE_CASE		:						Any			  =    model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""  )
                     _SCREAMING_SNAKE_CASE		:						Tuple			  =    layer.transformer.layer[transformer_num].attention.attention.all_head_size
                     _SCREAMING_SNAKE_CASE		:						int			  =    (
                         F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                     )
                     if "weight" in key:
                            _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    val[:dim, :]
                            _SCREAMING_SNAKE_CASE		:						Tuple			  =    val[dim : dim * 2, :]
                            _SCREAMING_SNAKE_CASE		:						Any			  =    val[-dim:, :]
                     else:
                            _SCREAMING_SNAKE_CASE		:						Any			  =    val[:dim]
                            _SCREAMING_SNAKE_CASE		:						str			  =    val[dim : dim * 2]
                            _SCREAMING_SNAKE_CASE		:						str			  =    val[-dim:]
              else:
                     _SCREAMING_SNAKE_CASE		:						List[str]			  =    val

       return orig_state_dict



def      lowerCamelCase_()->    Any:
       _SCREAMING_SNAKE_CASE		:						Dict			  =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _SCREAMING_SNAKE_CASE		:						Any			  =    Image.open(requests.get(__A     ,	stream=__A  ).raw  )
       return im



@torch.no_grad()
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    Dict:
       _SCREAMING_SNAKE_CASE		:						List[str]			  =    get_mobilevit_config(__A  )

       # load original state_dict
       _SCREAMING_SNAKE_CASE		:						int			  =    torch.load(__A     ,	map_location="""cpu"""  )

       # load 🤗 model
       if mobilevit_name.startswith("""deeplabv3_"""  ):
              _SCREAMING_SNAKE_CASE		:						List[str]			  =    MobileViTForSemanticSegmentation(__A  ).eval()
       else:
              _SCREAMING_SNAKE_CASE		:						List[str]			  =    MobileViTForImageClassification(__A  ).eval()

       _SCREAMING_SNAKE_CASE		:						List[str]			  =    convert_state_dict(__A     ,	__A  )
       model.load_state_dict(__A  )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       _SCREAMING_SNAKE_CASE		:						int			  =    MobileViTImageProcessor(crop_size=config.image_size     ,	size=config.image_size + 32  )
       _SCREAMING_SNAKE_CASE		:						Any			  =    image_processor(images=prepare_img()     ,	return_tensors="""pt"""  )
       _SCREAMING_SNAKE_CASE		:						Any			  =    model(**__A  )
       _SCREAMING_SNAKE_CASE		:						Any			  =    outputs.logits

       if mobilevit_name.startswith("""deeplabv3_"""  ):
              assert logits.shape == (1, 21, 32, 32)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     _SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.tensor(
                         [
                             [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                             [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                             [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                         ]  )
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.tensor(
                         [
                             [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                             [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                             [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                         ]  )
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
                         [
                             [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                             [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                             [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                         ]  )
              else:
                     raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

              assert torch.allclose(logits[0, :3, :3, :3]     ,	__A     ,	atol=1e-4  )
       else:
              assert logits.shape == (1, 1_000)

              if mobilevit_name == "mobilevit_s":
                     _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
              elif mobilevit_name == "mobilevit_xs":
                     _SCREAMING_SNAKE_CASE		:						Dict			  =    torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
              elif mobilevit_name == "mobilevit_xxs":
                     _SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
              else:
                     raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

              assert torch.allclose(logits[0, :3]     ,	__A     ,	atol=1e-4  )

       Path(__A  ).mkdir(exist_ok=__A  )
       print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""  )
       model.save_pretrained(__A  )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
       image_processor.save_pretrained(__A  )

       if push_to_hub:
              _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {
                  """mobilevit_s""": """mobilevit-small""",
                  """mobilevit_xs""": """mobilevit-x-small""",
                  """mobilevit_xxs""": """mobilevit-xx-small""",
                  """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
                  """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
                  """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
              }

              print("""Pushing to the hub..."""  )
              _SCREAMING_SNAKE_CASE		:						Dict			  =    model_mapping[mobilevit_name]
              image_processor.push_to_hub(__A     ,	organization="""apple"""  )
              model.push_to_hub(__A     ,	organization="""apple"""  )


if __name__ == "__main__":
   lowerCAmelCase_     =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--mobilevit_name''',
       default='''mobilevit_s''',
       type=str,
       help=(
           '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
           ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', required=True, 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.'''
   )

   lowerCAmelCase_     =	parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, 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
#
########################################################################


lowerCAmelCase_     =	16
lowerCAmelCase_     =	32



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    DatasetDict(
							    {
							        """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """test""": dataset["""validation"""],
							    }  )

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

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_SNAKE_CASE		:						Any			  =    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":
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE		:						Any			  =    8
														else:
																					_SCREAMING_SNAKE_CASE		:						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.
							_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(
							    tokenized_datasets["""train"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

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

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(__SCREAMING_SNAKE_CASE  )

							# New Code #
							# Create our folds:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    kfold.split(np.zeros(datasets["""train"""].num_rows  )     ,	datasets["""train"""]["""label"""]  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, 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)
														_SCREAMING_SNAKE_CASE		:						Any			  =    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).
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.to(accelerator.device  )

														# Instantiate optimizer
														_SCREAMING_SNAKE_CASE		:						int			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														_SCREAMING_SNAKE_CASE		:						int			  =    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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, 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  )
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						Dict			  =    outputs.loss
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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():
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits.argmax(dim=-1  )
																												_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																												metric.add_batch(
																												    predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE     ,	)

																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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
														_SCREAMING_SNAKE_CASE		:						str			  =    []
														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():
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[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:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.stack(__SCREAMING_SNAKE_CASE     ,	dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
							_SCREAMING_SNAKE_CASE		:						int			  =    metric.compute(predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE  )
							accelerator.print("""Average test metrics from all folds:"""     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    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"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						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()


