


"""simple docstring"""







import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowercase    					=      """true"""


def      lowercase			(				A_  ,   A_=82  ,   A_=16       )->      Tuple:
  '''simple docstring'''







  set_seed(42       )
  a						:		Dict      							=			RegressionModel()
  a						:		Tuple      							=			deepcopy(A_       )
  a						:		List[str]      							=			RegressionDataset(length=A_       )
  a						:		List[Any]      							=			DataLoader(A_  ,   batch_size=A_       )
  model.to(accelerator.device       )
  a							,		a						:		int      							=			accelerator.prepare(A_  ,   A_       )
  return model, ddp_model, dataloader


def      lowercase			(				A_  ,   A_=False       )->      List[Any]:
  '''simple docstring'''







  a						:		Tuple      							=			AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"       )
  a						:		List[str]      							=			load_dataset("glue"  ,   "mrpc"  ,   split="validation"       )

  def tokenize_function(A_       ):
    a						:		Union[str, Any]      							=			tokenizer(examples["sentence1"]  ,   examples["sentence2"]  ,   truncation=A_  ,   max_length=A_       )
    return outputs

  with accelerator.main_process_first():
    a						:		Union[str, Any]      							=			dataset.map(
        A_  ,   batched=A_  ,   remove_columns=["idx", "sentence1", "sentence2"]  ,   )

  a						:		Any      							=			tokenized_datasets.rename_column("label"  ,   "labels"       )

  def collate_fn(A_       ):
    if use_longest:
      return tokenizer.pad(A_  ,   padding="longest"  ,   return_tensors="pt"       )
    return tokenizer.pad(A_  ,   padding="max_length"  ,   max_length=128  ,   return_tensors="pt"       )

  return DataLoader(A_  ,   shuffle=A_  ,   collate_fn=A_  ,   batch_size=16       )


def      lowercase			(				A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  a						:		Tuple      							=			Accelerator(dispatch_batches=A_  ,   split_batches=A_       )
  a						:		List[str]      							=			get_dataloader(A_  ,   not dispatch_batches       )
  a						:		List[Any]      							=			AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"  ,   return_dict=A_       )
  a							,		a						:		List[str]      							=			accelerator.prepare(A_  ,   A_       )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def      lowercase			(				A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  a						:		Dict      							=			[]
  for batch in dataloader:
    a							,		a						:		Optional[int]      							=			batch.values()
    with torch.no_grad():
      a						:		Union[str, Any]      							=			model(A_       )
      a							,		a						:		str      							=			accelerator.gather_for_metrics((logit, target)       )
      logits_and_targets.append((logit, target)       )
  a							,		a						:		Any      							=			[], []
  for logit, targ in logits_and_targets:
    logits.append(A_       )
    targs.append(A_       )
  a							,		a						:		List[str]      							=			torch.cat(A_       ), torch.cat(A_       )
  return logits, targs


def      lowercase			(				A_  ,   A_=82  ,   A_=False  ,   A_=False  ,   A_=16       )->      str:
  '''simple docstring'''







  a							,		a							,		a						:		Tuple      							=			get_basic_setup(A_  ,   A_  ,   A_       )
  a							,		a						:		Dict      							=			generate_predictions(A_  ,   A_  ,   A_       )
  assert (
      len(A_       ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_       )}'''


def      lowercase			(				A_ = False  ,   A_ = False       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Any      							=			evaluate.load("glue"  ,   "mrpc"       )
  a							,		a						:		List[str]      							=			get_mrpc_setup(A_  ,   A_       )
  # First do baseline
  a							,		a							,		a						:		Any      							=			setup["no"]
  model.to(A_       )
  model.eval()
  for batch in dataloader:
    batch.to(A_       )
    with torch.inference_mode():
      a						:		Optional[int]      							=			model(**A_       )
    a						:		Dict      							=			outputs.logits.argmax(dim=-1       )
    metric.add_batch(predictions=A_  ,   references=batch["labels"]       )
  a						:		Optional[int]      							=			metric.compute()

  # Then do distributed
  a							,		a							,		a						:		List[str]      							=			setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      a						:		Tuple      							=			model(**A_       )
    a						:		Dict      							=			outputs.logits.argmax(dim=-1       )
    a						:		List[str]      							=			batch["labels"]
    a							,		a						:		Tuple      							=			accelerator.gather_for_metrics((preds, references)       )
    metric.add_batch(predictions=A_  ,   references=A_       )
  a						:		List[Any]      							=			metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]  ,   distributed[key]       ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def      lowercase			(				)->      List[str]:
  '''simple docstring'''







  a						:		int      							=			Accelerator(split_batches=A_  ,   dispatch_batches=A_       )
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print("**Testing gather_for_metrics**"       )
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''       )
        test_mrpc(A_  ,   A_       )
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test torch metrics**"       )
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      a						:		Optional[Any]      							=			Accelerator(split_batches=A_  ,   dispatch_batches=A_       )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''       )
      test_torch_metrics(A_  ,   99       )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**"       )
  a						:		Dict      							=			Accelerator()
  test_torch_metrics(A_  ,   512       )
  accelerator.state._reset_state()


def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()











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()
_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    "huggingface/label-files"
							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Tuple    =    json.load(open(hf_hub_download(_lowerCamelCase				, _lowerCamelCase				, repo_type="dataset"  )				, "r"  )  )
							_lowerCAmelCase     :							Tuple    =    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
							_lowerCAmelCase     :							Union[str, Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    "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"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

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

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

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

							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__":
						_snake_case		=  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.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
