





"""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
#
########################################################################


lowercase__				:Tuple   =       1_6
lowercase__				:Tuple   =       3_2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = 16  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
  __UpperCAmelCase    :						List[Any]       =						DatasetDict(
      {
          '''train''': dataset['''train'''].select(UpperCAmelCase_  ),
          '''validation''': dataset['''train'''].select(UpperCAmelCase_  ),
          '''test''': dataset['''validation'''],
      }  )

  def tokenize_function(UpperCAmelCase_  ):
    # max_length=None => use the model max length (it's actually the default)
    __UpperCAmelCase    :						Optional[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():
    __UpperCAmelCase    :						List[Any]       =						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    :						int       =						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    :						List[Any]       =						1_28 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    :						List[str]       =						16
    elif accelerator.mixed_precision != "no":
      __UpperCAmelCase    :						List[str]       =						8
    else:
      __UpperCAmelCase    :						str       =						None

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

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

  __UpperCAmelCase    :						Dict       =						DataLoader(
      tokenized_datasets['''test''']  ,			shuffle=UpperCAmelCase_  ,			collate_fn=UpperCAmelCase_  ,			batch_size=UpperCAmelCase_  )

  return train_dataloader, eval_dataloader, test_dataloader


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


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

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

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

  set_seed(UpperCAmelCase_  )

  # New Code #
  # Create our folds:
  __UpperCAmelCase    :						int       =						kfold.split(np.zeros(datasets['''train'''].num_rows  )  ,			datasets['''train''']['''label''']  )
  __UpperCAmelCase    :						Union[str, Any]       =						[]
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase_  ):
    __UpperCAmelCase    :						List[Any]       =						get_fold_dataloaders(
        UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCAmelCase    :						Dict       =						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    :						int       =						model.to(accelerator.device  )

    # Instantiate optimizer
    __UpperCAmelCase    :						List[Any]       =						AdamW(params=model.parameters()  ,			lr=UpperCAmelCase_  )

    # Instantiate scheduler
    __UpperCAmelCase    :						Dict       =						get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase_  ,			num_warmup_steps=1_00  ,			num_training_steps=(len(UpperCAmelCase_  ) * 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    :						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  )
        __UpperCAmelCase    :						Any       =						model(**UpperCAmelCase_  )
        __UpperCAmelCase    :						Optional[int]       =						outputs.loss
        __UpperCAmelCase    :						Dict       =						loss / gradient_accumulation_steps
        accelerator.backward(UpperCAmelCase_  )
        if step % gradient_accumulation_steps == 0:
          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    :						List[Any]       =						model(**UpperCAmelCase_  )
        __UpperCAmelCase    :						Optional[Any]       =						outputs.logits.argmax(dim=-1  )
        __UpperCAmelCase    :						List[str]       =						accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
        metric.add_batch(
            predictions=UpperCAmelCase_  ,			references=UpperCAmelCase_  ,			)

      __UpperCAmelCase    :						Dict       =						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''  ,			UpperCAmelCase_  )

    # New Code #
    # We also run predictions on the test set at the very end
    __UpperCAmelCase    :						Any       =						[]
    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    :						Any       =						model(**UpperCAmelCase_  )
      __UpperCAmelCase    :						List[str]       =						outputs.logits
      __UpperCAmelCase    :						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(UpperCAmelCase_  ,			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:
  __UpperCAmelCase    :						Optional[Any]       =						torch.cat(UpperCAmelCase_  ,			dim=0  )
  __UpperCAmelCase    :						Dict       =						torch.stack(UpperCAmelCase_  ,			dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
  __UpperCAmelCase    :						Dict       =						metric.compute(predictions=UpperCAmelCase_  ,			references=UpperCAmelCase_  )
  accelerator.print('''Average test metrics from all folds:'''  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						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.'''  )
  # New Code #
  parser.add_argument('''--num_folds'''  ,			type=UpperCAmelCase_  ,			default=3  ,			help='''The number of splits to perform across the dataset'''  )
  __UpperCAmelCase    :						Any       =						parser.parse_args()
  __UpperCAmelCase    :						List[Any]       =						{'''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 timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						SwinvaConfig()
  __UpperCAmelCase    :						Any       =						swinva_name.split('''_'''  )

  __UpperCAmelCase    :						Optional[Any]       =						name_split[1]
  if "to" in name_split[3]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[3][-3:]  )
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						int(name_split[3]  )
  if "to" in name_split[2]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[2][-2:]  )
  else:
    __UpperCAmelCase    :						str       =						int(name_split[2][6:]  )

  if model_size == "tiny":
    __UpperCAmelCase    :						Union[str, Any]       =						96
    __UpperCAmelCase    :						str       =						(2, 2, 6, 2)
    __UpperCAmelCase    :						Union[str, Any]       =						(3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase    :						List[Any]       =						96
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase    :						Any       =						1_28
    __UpperCAmelCase    :						int       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						Dict       =						(4, 8, 16, 32)
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						1_92
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __UpperCAmelCase    :						Optional[int]       =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __UpperCAmelCase    :						int       =						2_18_41
    __UpperCAmelCase    :						Optional[Any]       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-22k-id2label.json'''
    __UpperCAmelCase    :						Any       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						List[Any]       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Optional[Any]       =						idalabel
    __UpperCAmelCase    :						Optional[int]       =						{v: k for k, v in idalabel.items()}

  else:
    __UpperCAmelCase    :						List[Any]       =						10_00
    __UpperCAmelCase    :						str       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Tuple       =						'''imagenet-1k-id2label.json'''
    __UpperCAmelCase    :						Optional[int]       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						str       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Any       =						idalabel
    __UpperCAmelCase    :						List[Any]       =						{v: k for k, v in idalabel.items()}

  __UpperCAmelCase    :						Optional[int]       =						img_size
  __UpperCAmelCase    :						int       =						num_classes
  __UpperCAmelCase    :						Tuple       =						embed_dim
  __UpperCAmelCase    :						str       =						depths
  __UpperCAmelCase    :						Optional[Any]       =						num_heads
  __UpperCAmelCase    :						Optional[int]       =						window_size

  return config


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  if "patch_embed.proj" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''patch_embed.proj'''  ,			'''embeddings.patch_embeddings.projection'''  )
  if "patch_embed.norm" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''patch_embed.norm'''  ,			'''embeddings.norm'''  )
  if "layers" in name:
    __UpperCAmelCase    :						Optional[int]       =						'''encoder.''' + name
  if "attn.proj" in name:
    __UpperCAmelCase    :						Optional[Any]       =						name.replace('''attn.proj'''  ,			'''attention.output.dense'''  )
  if "attn" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''attn'''  ,			'''attention.self'''  )
  if "norm1" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''norm1'''  ,			'''layernorm_before'''  )
  if "norm2" in name:
    __UpperCAmelCase    :						Dict       =						name.replace('''norm2'''  ,			'''layernorm_after'''  )
  if "mlp.fc1" in name:
    __UpperCAmelCase    :						str       =						name.replace('''mlp.fc1'''  ,			'''intermediate.dense'''  )
  if "mlp.fc2" in name:
    __UpperCAmelCase    :						int       =						name.replace('''mlp.fc2'''  ,			'''output.dense'''  )
  if "q_bias" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''q_bias'''  ,			'''query.bias'''  )
  if "k_bias" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''k_bias'''  ,			'''key.bias'''  )
  if "v_bias" in name:
    __UpperCAmelCase    :						str       =						name.replace('''v_bias'''  ,			'''value.bias'''  )
  if "cpb_mlp" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''cpb_mlp'''  ,			'''continuous_position_bias_mlp'''  )
  if name == "norm.weight":
    __UpperCAmelCase    :						int       =						'''layernorm.weight'''
  if name == "norm.bias":
    __UpperCAmelCase    :						Optional[Any]       =						'''layernorm.bias'''

  if "head" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''head'''  ,			'''classifier'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						'''swinv2.''' + name

  return name


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase    :						Any       =						orig_state_dict.pop(UpperCAmelCase_  )

    if "mask" in key:
      continue
    elif "qkv" in key:
      __UpperCAmelCase    :						int       =						key.split('''.'''  )
      __UpperCAmelCase    :						int       =						int(key_split[1]  )
      __UpperCAmelCase    :						str       =						int(key_split[3]  )
      __UpperCAmelCase    :						List[str]       =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase    :						Optional[Any]       =						val[:dim, :]
        __UpperCAmelCase    :						Union[str, Any]       =						val[dim : dim * 2, :]
        __UpperCAmelCase    :						List[Any]       =						val[-dim:, :]
      else:
        __UpperCAmelCase    :						Optional[int]       =						val[:dim]
        __UpperCAmelCase    :						Any       =						val[
            dim : dim * 2
        ]
        __UpperCAmelCase    :						List[str]       =						val[-dim:]
    else:
      __UpperCAmelCase    :						Tuple       =						val

  return orig_state_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

  __UpperCAmelCase    :						Any       =						get_swinva_config(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						SwinvaForImageClassification(UpperCAmelCase_  )
  model.eval()

  __UpperCAmelCase    :						int       =						convert_state_dict(timm_model.state_dict()  ,			UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  __UpperCAmelCase    :						Optional[Any]       =						AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''  ,			'''-'''  )  )  )
  __UpperCAmelCase    :						List[Any]       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  )
  __UpperCAmelCase    :						Union[str, Any]       =						image_processor(images=UpperCAmelCase_  ,			return_tensors='''pt'''  )

  __UpperCAmelCase    :						List[Any]       =						timm_model(inputs['''pixel_values''']  )
  __UpperCAmelCase    :						Tuple       =						model(**UpperCAmelCase_  ).logits

  assert torch.allclose(UpperCAmelCase_  ,			UpperCAmelCase_  ,			atol=1e-3  )

  print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(UpperCAmelCase_  )

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(UpperCAmelCase_  )

  model.push_to_hub(
      repo_path_or_name=Path(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			organization='''nandwalritik'''  ,			commit_message='''Add model'''  ,			)


if __name__ == "__main__":
  lowercase__				:List[Any]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swinv2_name',
      default='swinv2_tiny_patch4_window8_256',
      type=str,
      help='Name of the Swinv2 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.'
  )

  lowercase__				:List[Any]				   =       parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)