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 the experiment tracking capability,
# 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
#
########################################################################

A   :     str       =				16
A   :     List[Any]       =				32


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase = 1_6 ):
      SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-cased" )
      SCREAMING_SNAKE_CASE_      =     load_dataset("glue"   ,					"mrpc" )

      def tokenize_function(__UpperCamelCase ):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      =     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():
            SCREAMING_SNAKE_CASE_      =     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
      SCREAMING_SNAKE_CASE_      =     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.
            SCREAMING_SNAKE_CASE_      =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_      =     1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_      =     8
            else:
                  SCREAMING_SNAKE_CASE_      =     None

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

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

      A   :     Tuple       =				mocked_dataloaders  # noqa: F811


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,					__UpperCamelCase ) == "1":
            SCREAMING_SNAKE_CASE_      =     2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            SCREAMING_SNAKE_CASE_      =     Accelerator(
                cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with="all"   ,					project_dir=args.project_dir )
      else:
            SCREAMING_SNAKE_CASE_      =     Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_      =     config["lr"]
      SCREAMING_SNAKE_CASE_      =     int(config["num_epochs"] )
      SCREAMING_SNAKE_CASE_      =     int(config["seed"] )
      SCREAMING_SNAKE_CASE_      =     int(config["batch_size"] )
      set_seed(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     get_dataloaders(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     evaluate.load("glue"   ,					"mrpc" )

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

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_      =     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).
      SCREAMING_SNAKE_CASE_      =     model.to(accelerator.device )

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_      =     AdamW(params=model.parameters()   ,					lr=__UpperCamelCase )

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_      =     get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase   ,					num_warmup_steps=1_0_0   ,					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.
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     accelerator.prepare(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            SCREAMING_SNAKE_CASE_      =     os.path.split(__UpperCamelCase )[-1].split("." )[0]
            accelerator.init_trackers(__UpperCamelCase   ,					__UpperCamelCase )

      # Now we train the model
      for epoch in range(__UpperCamelCase ):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  SCREAMING_SNAKE_CASE_      =     0
            for step, batch in enumerate(__UpperCamelCase ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device )
                  SCREAMING_SNAKE_CASE_      =     model(**__UpperCamelCase )
                  SCREAMING_SNAKE_CASE_      =     outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  SCREAMING_SNAKE_CASE_      =     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` (the default).
                  batch.to(accelerator.device )
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE_      =     model(**__UpperCamelCase )
                  SCREAMING_SNAKE_CASE_      =     outputs.logits.argmax(dim=-1 )
                  SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     accelerator.gather_for_metrics((predictions, batch["labels"]) )
                  metric.add_batch(
                      predictions=__UpperCamelCase   ,					references=__UpperCamelCase   ,					)

            SCREAMING_SNAKE_CASE_      =     metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''   ,					__UpperCamelCase )

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          "accuracy": eval_metric["accuracy"],
                          "f1": eval_metric["f1"],
                          "train_loss": total_loss.item() / len(__UpperCamelCase ),
                          "epoch": epoch,
                      }   ,					step=__UpperCamelCase   ,					)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     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." )
      parser.add_argument(
          "--with_tracking"   ,					action="store_true"   ,					help="Whether to load in all available experiment trackers from the environment and use them for logging."   ,					)
      parser.add_argument(
          "--project_dir"   ,					type=__UpperCamelCase   ,					default="logs"   ,					help="Location on where to store experiment tracking logs` and relevent project information"   ,					)
      SCREAMING_SNAKE_CASE_      =     parser.parse_args()
      SCREAMING_SNAKE_CASE_      =     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase   ,					__UpperCamelCase )


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()
A   :     str       =				logging.get_logger(__name__)


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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )
      return im


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_      =     False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_      =     1_0_0_0
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     idalabel
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-6:-4] )
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-3:] )
      # size of the architecture
      if deit_name[9:].startswith("tiny" ):
            SCREAMING_SNAKE_CASE_      =     1_9_2
            SCREAMING_SNAKE_CASE_      =     7_6_8
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     3
      elif deit_name[9:].startswith("small" ):
            SCREAMING_SNAKE_CASE_      =     3_8_4
            SCREAMING_SNAKE_CASE_      =     1_5_3_6
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     6
      if deit_name[9:].startswith("base" ):
            pass
      elif deit_name[4:].startswith("large" ):
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6

      # load original model from timm
      SCREAMING_SNAKE_CASE_      =     timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase )
      timm_model.eval()

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

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_      =     DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
      model.load_state_dict(__UpperCamelCase )

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_      =     int(
          (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     timm_model(__UpperCamelCase )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__UpperCamelCase   ,					outputs.logits   ,					atol=1E-3 )

      Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
      print(F'''Saving model {deit_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 )


if __name__ == "__main__":
      A   :     Tuple       =				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."
      )

      A   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


