


'''simple docstring'''







import argparse
import os

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

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


A				:					str    						=			16
A				:					List[Any]    						=			32






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str] = 16       ):
       _A					:     Union[str, Any]     = AutoTokenizer.from_pretrained('bert-base-cased'       )
       _A					:     int     = load_dataset('glue'  ,'mrpc'       )

       def tokenize_function(lowerCamelCase					:      List[str]       ):
              # max_length=None => use the model max length (it's actually the default)
              _A					:     List[str]     = tokenizer(examples['sentence1']  ,examples['sentence2']  ,truncation=__UpperCAmelCase  ,max_length=__UpperCAmelCase       )
              return outputs

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

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

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

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

       # Instantiate dataloaders.
       _A					:     Tuple     = DataLoader(
           tokenized_datasets['train']  ,shuffle=__UpperCAmelCase  ,collate_fn=__UpperCAmelCase  ,batch_size=__UpperCAmelCase       )
       _A					:     Tuple     = DataLoader(
           tokenized_datasets['validation']  ,shuffle=__UpperCAmelCase  ,collate_fn=__UpperCAmelCase  ,batch_size=__UpperCAmelCase       )

       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

   A				:					Tuple    						=			mocked_dataloaders  # noqa: F811






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      List[str]       ):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,__UpperCAmelCase       ) == "1":
              _A					:     int     = 2
       # Initialize accelerator
       _A					:     str     = Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _A					:     List[str]     = config['''lr''']
       _A					:     Optional[Any]     = int(config['num_epochs']       )
       _A					:     Optional[Any]     = int(config['seed']       )
       _A					:     Optional[int]     = int(config['batch_size']       )

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

       # New Code #
       # We now can define an inner training loop function. It should take a batch size as the only parameter,
       # and build the dataloaders in there.
       # It also gets our decorator
       @find_executable_batch_size(starting_batch_size=__UpperCAmelCase       )
       def inner_training_loop(lowerCamelCase					:      int       ):
              # And now just move everything below under this function
              # We need to bring in the Accelerator object from earlier
              nonlocal accelerator
              # And reset all of its attributes that could hold onto any memory:
              accelerator.free_memory()

              # Then we can declare the model, optimizer, and everything else:
              set_seed(__UpperCAmelCase       )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              _A					:     Union[str, Any]     = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,return_dict=__UpperCAmelCase       )

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

              # Instantiate optimizer
              _A					:     str     = AdamW(params=model.parameters()  ,lr=__UpperCAmelCase       )
              _A					:     Tuple     = get_dataloaders(__UpperCAmelCase  ,__UpperCAmelCase       )

              # Instantiate scheduler
              _A					:     int     = get_linear_schedule_with_warmup(
                  optimizer=__UpperCAmelCase  ,num_warmup_steps=100  ,num_training_steps=(len(__UpperCAmelCase       ) * num_epochs)  ,)

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

              # Now we train the model
              for epoch in range(__UpperCAmelCase       ):
                     model.train()
                     for step, batch in enumerate(__UpperCAmelCase       ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device       )
                            _A					:     List[Any]     = model(**__UpperCAmelCase       )
                            _A					:     Union[str, Any]     = outputs.loss
                            accelerator.backward(__UpperCAmelCase       )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

                     _A					:     Union[str, Any]     = metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'epoch {epoch}:'  ,__UpperCAmelCase       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()






def 				lowerCAmelCase__	(    ):
       _A					:     List[str]     = argparse.ArgumentParser(description='Simple example of training script.'       )
       parser.add_argument(
           '--mixed_precision'  ,type=__UpperCAmelCase  ,default=__UpperCAmelCase  ,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.'       )
       _A					:     int     = parser.parse_args()
       _A					:     str     = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(__UpperCAmelCase  ,__UpperCAmelCase       )


if __name__ == "__main__":
   main()




'''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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Dict     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]       )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]       )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]       )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Optional[Any]     = []
       for k in state_dict.keys():
              _A					:     Optional[int]     = k
              if ".pwconv" in k:
                     _A					:     str     = k_new.replace('.pwconv'  ,'.point_wise_conv'       )
              if ".dwconv" in k:
                     _A					:     Any     = k_new.replace('.dwconv'  ,'.depth_wise_conv'       )
              if ".Proj." in k:
                     _A					:     Optional[Any]     = k_new.replace('.Proj.'  ,'.proj.'       )
              if "patch_embed" in k_new:
                     _A					:     Optional[int]     = k_new.replace('patch_embed'  ,'swiftformer.patch_embed.patch_embedding'       )
              if "network" in k_new:
                     _A					:     Tuple     = k_new.split('.'       )
                     if ls[2].isdigit():
                            _A					:     List[Any]     = 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
                     else:
                            _A					:     List[str]     = k_new.replace('network'  ,'swiftformer.encoder.network'       )
              rename_keys.append((k, k_new)       )
       return rename_keys






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _A					:     Any     = 1000
       _A					:     int     = 'huggingface/label-files'
       _A					:     List[Any]     = 'imagenet-1k-id2label.json'
       _A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
       _A					:     Optional[int]     = idalabel
       _A					:     Any     = {v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _A					:     Optional[Any]     = [3, 3, 6, 4]
              _A					:     Optional[int]     = [48, 56, 112, 220]

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

       elif swiftformer_name == "swiftformer_l1":
              _A					:     int     = [4, 3, 10, 5]
              _A					:     int     = [48, 96, 192, 384]

       elif swiftformer_name == "swiftformer_l3":
              _A					:     Optional[Any]     = [4, 4, 12, 6]
              _A					:     Any     = [64, 128, 320, 512]

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith('https'       ):
                     _A					:     Optional[Any]     = torch.hub.load_state_dict_from_url(lowerCamelCase  ,map_location='cpu'  ,check_hash=lowerCamelCase       )
              else:
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase  ,map_location='cpu'       )
       _A					:     Union[str, Any]     = checkpoint

       _A					:     List[str]     = create_rename_keys(lowerCamelCase       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # load HuggingFace model
       _A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

       assert hf_logits.shape == torch.Size([1, 1000]       )
       assert torch.allclose(hf_logits[0, 0:5]  ,lowerCamelCase  ,atol=1E-3       )

       Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
       print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
       hf_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
   A				:					Dict    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swiftformer_name''',
       default='''swiftformer_xs''',
       choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
       type=str,
       help='''Name of the SwiftFormer model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default='''./converted_outputs/''',
       type=str,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

   A				:					List[str]    						=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






