






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


SCREAMING_SNAKE_CASE__     =       1_6
SCREAMING_SNAKE_CASE__     =       3_2







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = 16   )->			Union[str, Any]:
      UpperCamelCase      = AutoTokenizer.from_pretrained("""bert-base-cased"""   )
      UpperCamelCase      = load_dataset("""glue"""		,						"""mrpc"""   )

      def tokenize_function(__UpperCamelCase   ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase      = 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():
            UpperCamelCase      = 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
      UpperCamelCase      = 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.
            UpperCamelCase      = 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":
                  UpperCamelCase      = 16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase      = 8
            else:
                  UpperCamelCase      = None

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

      # Instantiate dataloaders.
      UpperCamelCase      = DataLoader(
          tokenized_datasets["""train"""]		,						shuffle=__UpperCAmelCase		,						collate_fn=__UpperCAmelCase		,						batch_size=__UpperCAmelCase   )
      UpperCamelCase      = 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

     SCREAMING_SNAKE_CASE__     =       mocked_dataloaders  # noqa: F811







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

      UpperCamelCase      = 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)
            UpperCamelCase      = 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).
            UpperCamelCase      = model.to(accelerator.device   )

            # Instantiate optimizer
            UpperCamelCase      = AdamW(params=model.parameters()		,						lr=__UpperCAmelCase   )
            UpperCamelCase      = get_dataloaders(__UpperCAmelCase		,						__UpperCAmelCase   )

            # Instantiate scheduler
            UpperCamelCase      = 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.
            UpperCamelCase      = 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   )
                        UpperCamelCase      = model(**__UpperCAmelCase   )
                        UpperCamelCase      = 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():
                              UpperCamelCase      = model(**__UpperCAmelCase   )
                        UpperCamelCase      = outputs.logits.argmax(dim=-1   )
                        UpperCamelCase      = accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
                        metric.add_batch(
                            predictions=__UpperCAmelCase		,						references=__UpperCAmelCase		,						)

                  UpperCamelCase      = 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    lowercase__			(				)->			int:
      UpperCamelCase      = 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."""   )
      UpperCamelCase      = parser.parse_args()
      UpperCamelCase      = {'''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 dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.BatchNormad				)
        if has_not_submodules:
              self.traced.append(_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_SCREAMING_SNAKE_CASE				)
        [x.remove() for x in self.handles]
        return self


  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return list(filter(lambda _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0      ,		self.traced				)				)






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				0
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip      ,		_SCREAMING_SNAKE_CASE				)				)

        if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
              raise Exception(
                  F"Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE				)} operations while"
                  F" destination module has {len(_SCREAMING_SNAKE_CASE				)}."				)

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              dest_m.load_state_dict(src_m.state_dict()				)
              if self.verbose == 1:
                    print(F"Transfered from={src_m} to={dest_m}"				)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True   )->			Optional[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase      = timm.create_model(__UpperCamelCase		,						pretrained=__UpperCamelCase   ).eval()
            UpperCamelCase      = ResNetForImageClassification(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

      assert torch.allclose(from_model(__UpperCamelCase   )		,						our_model(__UpperCamelCase   ).logits   ), "The model logits don't match the original one."

      UpperCamelCase      = F"resnet{'-'.join(name.split('resnet'   )   )}"
      print(__UpperCamelCase   )

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add model"""		,						use_temp_dir=__UpperCamelCase		,						)

            # we can use the convnext one
            UpperCamelCase      = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add image processor"""		,						use_temp_dir=__UpperCamelCase		,						)

            print(F"Pushed {checkpoint_name}"   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			str:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

      UpperCamelCase      = """huggingface/label-files"""
      UpperCamelCase      = num_labels
      UpperCamelCase      = json.load(open(hf_hub_download(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )		,						"""r"""   )   )
      UpperCamelCase      = {int(__UpperCamelCase   ): v for k, v in idalabel.items()}

      UpperCamelCase      = idalabel
      UpperCamelCase      = {v: k for k, v in idalabel.items()}

      UpperCamelCase      = partial(__UpperCamelCase		,						num_labels=__UpperCamelCase		,						idalabel=__UpperCamelCase		,						labelaid=__UpperCamelCase   )

      UpperCamelCase      = {
          """resnet18""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet26""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet34""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet50""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet101""": ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet152""": ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
      }

      if model_name:
            convert_weight_and_push(__UpperCamelCase		,						names_to_config[model_name]		,						__UpperCamelCase		,						__UpperCamelCase   )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
      return config, expected_shape


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help=(
             'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         type=Path,
         required=True,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     SCREAMING_SNAKE_CASE__     =       args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
