


'''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()
__snake_case				 =	logging.get_logger(__name__)

__snake_case				 =	torch.device('''cpu''')


def      a (     )    ->      str:



					'''simple docstring'''






					UpperCamelCase__  :Tuple				   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCamelCase__  :str				   =					Image.open(requests.get(_UpperCamelCase					,   stream=_UpperCamelCase  ).raw  )
					return im


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					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      a (     __a					,   __a					,   __a  )    ->      Optional[int]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					dct.pop(_UpperCamelCase  )
					UpperCamelCase__  :Dict				   =					val


def      a (     __a  )    ->      Optional[int]:



					'''simple docstring'''






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


@torch.no_grad()
def      a (     __a					,   __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					SwiftFormerConfig()

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

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

					elif swiftformer_name == "swiftformer_s":
										UpperCamelCase__  :List[str]				   =					[3, 3, 9, 6]
										UpperCamelCase__  :List[str]				   =					[48, 64, 168, 224]

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

					elif swiftformer_name == "swiftformer_l3":
										UpperCamelCase__  :List[Any]				   =					[4, 4, 12, 6]
										UpperCamelCase__  :Union[str, Any]				   =					[64, 128, 320, 512]

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

					UpperCamelCase__  :Optional[int]				   =					create_rename_keys(_UpperCamelCase  )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_UpperCamelCase					,   _UpperCamelCase					,   _UpperCamelCase  )

					# load HuggingFace model
					UpperCamelCase__  :int				   =					SwiftFormerForImageClassification(_UpperCamelCase  ).eval()
					hf_model.load_state_dict(_UpperCamelCase  )

					# prepare test inputs
					UpperCamelCase__  :List[str]				   =					prepare_img()
					UpperCamelCase__  :Union[str, Any]				   =					ViTImageProcessor.from_pretrained('''preprocessor_config'''  )
					UpperCamelCase__  :List[str]				   =					processor(images=_UpperCamelCase					,   return_tensors='''pt'''  )

					# compare outputs from both models
					UpperCamelCase__  :Any				   =					get_expected_output(_UpperCamelCase  )
					UpperCamelCase__  :List[Any]				   =					hf_model(inputs['''pixel_values''']  ).logits

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

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


if __name__ == "__main__":
		__snake_case				 =	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.''')

		__snake_case				 =	parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







'''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
#
########################################################################


UpperCamelCase__			=      16
UpperCamelCase__			=      32




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16				):


   """simple docstring"""




   lowercase_						:     int							     =  AutoTokenizer.from_pretrained("bert-base-cased"				)
   lowercase_						:     Optional[Any]							     =  load_dataset("glue"    ,		"mrpc"				)

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

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

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

   # Instantiate dataloaders.
   lowercase_						:     Optional[int]							     =  DataLoader(
       tokenized_datasets["train"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)
   lowercase_						:     str							     =  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

 UpperCamelCase__			=      mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




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

   lowercase_						:     List[Any]							     =  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(_UpperCamelCase				):
      # 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)
      lowercase_						:     List[str]							     =  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).
      lowercase_						:     List[Any]							     =  model.to(accelerator.device				)

      # Instantiate optimizer
      lowercase_						:     Tuple							     =  AdamW(params=model.parameters()    ,		lr=_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     Any							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase				)

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

         lowercase_						:     int							     =  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 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  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."				)
   lowercase_						:     int							     =  parser.parse_args()
   lowercase_						:     Optional[Any]							     =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()




