import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)







def  __UpperCamelCase    (						A ,							A=False							):
     UpperCamelCase__										=      []
     for i in range(config.num_hidden_layers							):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")							)
          rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")							)
          rename_keys.append(
              (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")							)
          rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")							)
          rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")							)
          rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")							)
          rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")							)
          rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")							)
          rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")							)
          rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")							)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''module.cls_token''', '''vit.embeddings.cls_token'''),
             ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
         ]							)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''module.norm.weight''', '''layernorm.weight'''),
                  ('''module.norm.bias''', '''layernorm.bias'''),
              ]							)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          UpperCamelCase__										=      [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''							) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''norm.weight''', '''vit.layernorm.weight'''),
                  ('''norm.bias''', '''vit.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]							)

     return rename_keys







def  __UpperCamelCase    (						A ,							A ,							A=False							):
     for i in range(config.num_hidden_layers							):
          if base_model:
               UpperCamelCase__										=      ''''''
          else:
               UpperCamelCase__										=      '''vit.'''
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          UpperCamelCase__										=      state_dict.pop(f"module.blocks.{i}.attn.qkv.weight"							)
          UpperCamelCase__										=      state_dict.pop(f"module.blocks.{i}.attn.qkv.bias"							)
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase__										=      in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCamelCase__										=      in_proj_bias[: config.hidden_size]
          UpperCamelCase__										=      in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCamelCase__										=      in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          UpperCamelCase__										=      in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCamelCase__										=      in_proj_bias[-config.hidden_size :]







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(A ,							A							)







def  __UpperCamelCase    (						A							):
     # projection head is used in the self-supervised pre-training in MSN,
     # for downstream task it's not needed.
     UpperCamelCase__										=      [
         '''module.fc.fc1.weight''',
         '''module.fc.fc1.bias''',
         '''module.fc.bn1.weight''',
         '''module.fc.bn1.bias''',
         '''module.fc.bn1.running_mean''',
         '''module.fc.bn1.running_var''',
         '''module.fc.bn1.num_batches_tracked''',
         '''module.fc.fc2.weight''',
         '''module.fc.fc2.bias''',
         '''module.fc.bn2.weight''',
         '''module.fc.bn2.bias''',
         '''module.fc.bn2.running_mean''',
         '''module.fc.bn2.running_var''',
         '''module.fc.bn2.num_batches_tracked''',
         '''module.fc.fc3.weight''',
         '''module.fc.fc3.bias''',
     ]
     for k in ignore_keys:
          state_dict.pop(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      dct.pop(A							)
     UpperCamelCase__										=      val







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      ViTMSNConfig()
     UpperCamelCase__										=      1000

     UpperCamelCase__										=      '''datasets/huggingface/label-files'''
     UpperCamelCase__										=      '''imagenet-1k-id2label.json'''
     UpperCamelCase__										=      json.load(open(hf_hub_download(A ,							A							) ,							'''r'''							)							)
     UpperCamelCase__										=      {int(A							): v for k, v in idalabel.items()}
     UpperCamelCase__										=      idalabel
     UpperCamelCase__										=      {v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          UpperCamelCase__										=      384
          UpperCamelCase__										=      1536
          UpperCamelCase__										=      6
     elif "l16" in checkpoint_url:
          UpperCamelCase__										=      1024
          UpperCamelCase__										=      4096
          UpperCamelCase__										=      24
          UpperCamelCase__										=      16
          UpperCamelCase__										=      0.1
     elif "b4" in checkpoint_url:
          UpperCamelCase__										=      4
     elif "l7" in checkpoint_url:
          UpperCamelCase__										=      7
          UpperCamelCase__										=      1024
          UpperCamelCase__										=      4096
          UpperCamelCase__										=      24
          UpperCamelCase__										=      16
          UpperCamelCase__										=      0.1

     UpperCamelCase__										=      ViTMSNModel(A							)

     UpperCamelCase__										=      torch.hub.load_state_dict_from_url(A ,							map_location='''cpu'''							)['''target_encoder''']

     UpperCamelCase__										=      ViTImageProcessor(size=config.image_size							)

     remove_projection_head(A							)
     UpperCamelCase__										=      create_rename_keys(A ,							base_model=A							)

     for src, dest in rename_keys:
          rename_key(A ,							A ,							A							)
     read_in_q_k_v(A ,							A ,							base_model=A							)

     model.load_state_dict(A							)
     model.eval()

     UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A							).raw							)
     UpperCamelCase__										=      ViTImageProcessor(
         size=config.image_size ,							image_mean=A ,							image_std=A							)
     UpperCamelCase__										=      image_processor(images=A ,							return_tensors='''pt'''							)

     # forward pass
     torch.manual_seed(2							)
     UpperCamelCase__										=      model(**A							)
     UpperCamelCase__										=      outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]							)
     elif "b16" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[14.28_89, -18.90_45, 11.72_81]]							)
     elif "l16" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[41.50_28, -22.86_81, 45.64_75]]							)
     elif "b4" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]							)
     else:
          UpperCamelCase__										=      torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]							)

     # verify logits
     assert torch.allclose(last_hidden_state[:, 0, :3] ,							A ,							atol=1e-4							)

     print(f"Saving model to {pytorch_dump_folder_path}"							)
     model.save_pretrained(A							)

     print(f"Saving image processor to {pytorch_dump_folder_path}"							)
     image_processor.save_pretrained(A							)


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
         type=str,
         help='''URL of the checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __magic_name__								=parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




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
#
########################################################################


__magic_name__								=16
__magic_name__								=32







def  __UpperCamelCase    (						A ,							A = 16							):
     UpperCamelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     UpperCamelCase__										=      load_dataset('''glue''' ,							'''mrpc'''							)

     def tokenize_function(A							):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__										=      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__										=      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__										=      tokenized_datasets.rename_column('''label''' ,							'''labels'''							)

     def collate_fn(A							):
          # 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(
              A ,							padding='''longest''' ,							max_length=A ,							pad_to_multiple_of=A ,							return_tensors='''pt''' ,							)

     # Instantiate dataloaders.
     UpperCamelCase__										=      DataLoader(
         tokenized_datasets['''train'''] ,							shuffle=A ,							collate_fn=A ,							batch_size=A							)
     UpperCamelCase__										=      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

     __magic_name__								=mocked_dataloaders  # noqa: F811







def  __UpperCamelCase    (						A ,							A							):
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							A							) == "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'''							)

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

     set_seed(A							)
     UpperCamelCase__						,       UpperCamelCase__										=      get_dataloaders(A ,							A							)
     # 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=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__										=      model.to(accelerator.device							)

     # Instantiate optimizer
     UpperCamelCase__										=      AdamW(params=model.parameters() ,							lr=A							)

     # Instantiate scheduler
     UpperCamelCase__										=      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__										=      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__										=      model(**A							)
               UpperCamelCase__										=      outputs.loss
               UpperCamelCase__										=      loss / gradient_accumulation_steps
               accelerator.backward(A							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCamelCase__										=      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__										=      model(**A							)
               UpperCamelCase__										=      outputs.logits.argmax(dim=-1							)
               UpperCamelCase__						,       UpperCamelCase__										=      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__										=      predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         UpperCamelCase__										=      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__										=      metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:" ,							A							)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      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__										=      parser.parse_args()
     UpperCamelCase__										=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A ,							A							)


if __name__ == "__main__":
     main()



