


import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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    :Union[str, Any]				       =     1_6
__UpperCAmelCase    :List[Any]				       =     3_2




def 			_a			(		_lowercase      :			Accelerator    ,      _lowercase      :			int = 16						):





     '''simple docstring'''






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

     def tokenize_function(_lowercase      :			Any						):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCAmelCase			:				List[Any]													=    tokenizer(examples['''sentence1''']    ,      examples['''sentence2''']    ,      truncation=A__    ,      max_length=A__						)
          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			:				str													=    datasets.map(
              A__    ,      batched=A__    ,      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			:				Union[str, Any]													=    tokenized_datasets.rename_column('''label'''    ,      '''labels'''						)

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

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

     # Instantiate dataloaders.
     __UpperCAmelCase			:				int													=    DataLoader(
         tokenized_datasets['''train''']    ,      shuffle=A__    ,      collate_fn=A__    ,      batch_size=A__						)
     __UpperCAmelCase			:				List[str]													=    DataLoader(
         tokenized_datasets['''validation''']    ,      shuffle=A__    ,      collate_fn=A__    ,      batch_size=A__						)

     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    :int				       =     mocked_dataloaders  # noqa: F811




def 			_a			(		_lowercase      :			Union[str, Any]    ,      _lowercase      :			Any						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				Tuple													=    evaluate.load('''glue'''    ,      '''mrpc'''						)

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

     set_seed(A__						)
     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[int]													=    get_dataloaders(A__    ,      A__						)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCAmelCase			:				List[Any]													=    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,      return_dict=A__						)

     # 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			:				str													=    model.to(accelerator.device						)

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

     # Instantiate scheduler
     __UpperCAmelCase			:				Dict													=    get_linear_schedule_with_warmup(
         optimizer=A__    ,      num_warmup_steps=100    ,      num_training_steps=(len(A__						) * 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.
     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[int]													=    accelerator.prepare(
         A__    ,      A__    ,      A__    ,      A__    ,      A__						)

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

          model.eval()
          __UpperCAmelCase			:				Union[str, Any]													=    0
          for step, batch in enumerate(A__						):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device						)
               with torch.no_grad():
                    __UpperCAmelCase			:				str													=    model(**A__						)
               __UpperCAmelCase			:				Tuple													=    outputs.logits.argmax(dim=-1						)
               __UpperCAmelCase							,     __UpperCAmelCase			:				List[Any]													=    accelerator.gather((predictions, batch['''labels'''])						)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(A__						) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         __UpperCAmelCase			:				Optional[Any]													=    predictions[: len(eval_dataloader.dataset						) - samples_seen]
                         __UpperCAmelCase			:				Tuple													=    references[: len(eval_dataloader.dataset						) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=A__    ,      references=A__    ,      )

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




def 			_a			(		):





     '''simple docstring'''






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


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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    :List[Any]				       =     logging.get_logger(__name__)




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    '''huggingface/label-files'''
     __UpperCAmelCase			:				str													=    '''imagenet-1k-id2label.json'''
     __UpperCAmelCase			:				Optional[Any]													=    json.load(open(hf_hub_download(_lowercase    ,      _lowercase    ,      repo_type='''dataset'''						)    ,      '''r'''						)						)
     __UpperCAmelCase			:				int													=    {int(_lowercase						): v for k, v in idalabel.items()}
     __UpperCAmelCase			:				int													=    {v: k for k, v in idalabel.items()}

     __UpperCAmelCase			:				Optional[Any]													=    '''std_conv''' if '''bit''' in model_name else False

     # note that when using BiT as backbone for ViT-hybrid checkpoints,
     # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
     # config.conv_layer = "std_conv_same"
     __UpperCAmelCase			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     if "stem.conv" in name:
          __UpperCAmelCase			:				Any													=    name.replace('''stem.conv'''    ,      '''bit.embedder.convolution'''						)
     if "blocks" in name:
          __UpperCAmelCase			:				Optional[int]													=    name.replace('''blocks'''    ,      '''layers'''						)
     if "head.fc" in name:
          __UpperCAmelCase			:				Dict													=    name.replace('''head.fc'''    ,      '''classifier.1'''						)
     if name.startswith('''norm'''						):
          __UpperCAmelCase			:				List[Any]													=    '''bit.''' + name
     if "bit" not in name and "classifier" not in name:
          __UpperCAmelCase			:				List[str]													=    '''bit.encoder.''' + name

     return name




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase			:				Optional[Any]													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     return im




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

     __UpperCAmelCase			:				str													=    {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     __UpperCAmelCase			:				List[Any]													=    BitImageProcessor(
         do_resize=_lowercase    ,      size={'''shortest_edge''': timm_transforms[0].size}    ,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,      do_center_crop=_lowercase    ,      crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,      do_normalize=_lowercase    ,      image_mean=timm_transforms[-1].mean.tolist()    ,      image_std=timm_transforms[-1].std.tolist()    ,      )

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

     # verify pixel values
     assert torch.allclose(_lowercase    ,      _lowercase						)

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

     print('''Logits:'''    ,      logits[0, :3]						)
     print('''Predicted class:'''    ,      model.config.idalabel[logits.argmax(-1						).item()]						)
     __UpperCAmelCase			:				Optional[int]													=    timm_model(_lowercase						)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(_lowercase    ,      outputs.logits    ,      atol=1E-3						)
     print('''Looks ok!'''						)

     if pytorch_dump_folder_path is not None:
          Path(_lowercase						).mkdir(exist_ok=_lowercase						)
          print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'						)
          model.save_pretrained(_lowercase						)
          processor.save_pretrained(_lowercase						)

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


if __name__ == "__main__":
      __UpperCAmelCase    :Optional[int]				       =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="resnetv2_50x1_bitm",
          type=str,
          help="Name of the BiT timm 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 to push the model to the hub.",
      )

      __UpperCAmelCase    :str				       =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)