import argparse
import gc
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase__    							= 1_6
UpperCamelCase__    							= 3_2




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return int(x / 2**20							)





class 					A							:


   def __enter__(self					:						Dict		)						->    int:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         return self





   def __exit__(self					:						List[str]	,  *__UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         UpperCAmelCase__								=  torch.cuda.max_memory_allocated()
         UpperCAmelCase__								=  bamb(self.end - self.begin		)
         UpperCAmelCase__								=  bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def        lowerCAmelCase_			(					__A,  __A = 16,  __A = "bert-base-cased",  __A = 320,  __A = 160,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      UpperCAmelCase__								=  load_dataset(
          "glue",  "mrpc",  split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}							)

      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
      UpperCAmelCase__								=  datasets.map(
          __A,  batched=__A,  remove_columns=["idx", "sentence1", "sentence2"],  load_from_cache_file=__A							)

      # 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.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__A,  padding="max_length",  max_length=128,  return_tensors="pt"							)
            return tokenizer.pad(__A,  padding="longest",  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




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()

      # 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__								=  args.model_name_or_path

      set_seed(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  get_dataloaders(__A,  __A,  __A,  args.n_train,  args.n_val							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase__								=  AutoModelForSequenceClassification.from_pretrained(__A,  return_dict=__A							)

      # Instantiate optimizer
      UpperCAmelCase__								=  (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCAmelCase__								=  optimizer_cls(params=model.parameters(),  lr=__A							)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase__								=  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCAmelCase__								=  1
      UpperCAmelCase__								=  (len(__A							) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCAmelCase__								=  get_linear_schedule_with_warmup(
                optimizer=__A,  num_warmup_steps=0,  num_training_steps=__A,  )
      else:
            UpperCAmelCase__								=  DummyScheduler(__A,  total_num_steps=__A,  warmup_num_steps=0							)

      # 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							)

      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase__								=  0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCAmelCase__								=  0

      # Now we train the model
      UpperCAmelCase__								=  {}
      for epoch in range(__A,  __A							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__A							):
                        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()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin							)							)							)
            accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used							)							)
            accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked							)							)
            accelerator.print(
                "Total Peak Memory consumed during the train (max): {}".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
            UpperCAmelCase__								=  tracemalloc.peaked + bamb(tracemalloc.begin							)
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir,  "peak_memory_utilization.json"							),  "w"							) as f:
                  json.dump(__A,  __A							)




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
      parser.add_argument(
          "--model_name_or_path",  type=__A,  default="bert-base-cased",  help="Path to pretrained model or model identifier from huggingface.co/models.",  required=__A,  )
      parser.add_argument(
          "--output_dir",  type=__A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
      parser.add_argument(
          "--peak_memory_upper_bound",  type=__A,  default=__A,  help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.",  )
      parser.add_argument(
          "--n_train",  type=__A,  default=320,  help="Number of training examples to use.",  )
      parser.add_argument(
          "--n_val",  type=__A,  default=160,  help="Number of validation examples to use.",  )
      parser.add_argument(
          "--num_epochs",  type=__A,  default=1,  help="Number of train epochs.",  )
      UpperCAmelCase__								=  parser.parse_args()
      UpperCAmelCase__								=  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__A,  __A							)


if __name__ == "__main__":
      main()




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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Any:







      '''simple docstring'''


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

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "deit.embeddings.cls_token"),
              ("dist_token", "deit.embeddings.distillation_token"),
              ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "deit.embeddings.position_embeddings"),
          ]							)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("norm.weight", "layernorm.weight"),
                    ("norm.bias", "layernorm.bias"),
                    ("pre_logits.fc.weight", "pooler.dense.weight"),
                    ("pre_logits.fc.bias", "pooler.dense.bias"),
                ]							)

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

      return rename_keys




def        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''simple docstring'''


      for i in range(config.num_hidden_layers							):
            if base_model:
                  UpperCAmelCase__								=  ""
            else:
                  UpperCAmelCase__								=  "deit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            UpperCAmelCase__								=  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""							)
            UpperCAmelCase__								=  state_dict.pop(f"""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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase__								=  Image.open(requests.get(__A,  stream=__A							).raw							)
      return im




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  DeiTConfig()
      # all deit models have fine-tuned heads
      UpperCAmelCase__								=  False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  "huggingface/label-files"
      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
      UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
      UpperCAmelCase__								=  idalabel
      UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
      UpperCAmelCase__								=  int(deit_name[-6:-4]							)
      UpperCAmelCase__								=  int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith("tiny"							):
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  3
      elif deit_name[9:].startswith("small"							):
            UpperCAmelCase__								=  384
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  6
      if deit_name[9:].startswith("base"							):
            pass
      elif deit_name[4:].startswith("large"							):
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16

      # load original model from timm
      UpperCAmelCase__								=  timm.create_model(__A,  pretrained=__A							)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      UpperCAmelCase__								=  timm_model.state_dict()
      UpperCAmelCase__								=  create_rename_keys(__A,  __A							)
      for src, dest in rename_keys:
            rename_key(__A,  __A,  __A							)
      read_in_q_k_v(__A,  __A,  __A							)

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      UpperCAmelCase__								=  int(
          (256 / 224) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      UpperCAmelCase__								=  DeiTImageProcessor(size=__A,  crop_size=config.image_size							)
      UpperCAmelCase__								=  image_processor(images=prepare_img(),  return_tensors="pt"							)
      UpperCAmelCase__								=  encoding["pixel_values"]
      UpperCAmelCase__								=  model(__A							)

      UpperCAmelCase__								=  timm_model(__A							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__A,  outputs.logits,  atol=1e-3							)

      Path(__A							).mkdir(exist_ok=__A							)
      print(f"""Saving model {deit_name} 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__":
      UpperCamelCase__    							= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--deit_name',
          default='vit_deit_base_distilled_patch16_224',
          type=str,
          help='Name of the DeiT 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.'
      )

      UpperCamelCase__    							= parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



