

'''simple docstring'''
import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]							)			-> List[str]:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   ConvNextConfig()

	if "tiny" in checkpoint_url:
		UpperCAmelCase_			:	Union[str, Any]      =   [3, 3, 9, 3]
		UpperCAmelCase_			:	List[Any]      =   [96, 1_92, 3_84, 7_68]
	if "small" in checkpoint_url:
		UpperCAmelCase_			:	Optional[Any]      =   [3, 3, 27, 3]
		UpperCAmelCase_			:	Tuple      =   [96, 1_92, 3_84, 7_68]
	if "base" in checkpoint_url:
		UpperCAmelCase_			:	Tuple      =   [3, 3, 27, 3]
		UpperCAmelCase_			:	str      =   [1_28, 2_56, 5_12, 10_24]
	if "large" in checkpoint_url:
		UpperCAmelCase_			:	List[Any]      =   [3, 3, 27, 3]
		UpperCAmelCase_			:	Optional[Any]      =   [1_92, 3_84, 7_68, 15_36]
	if "xlarge" in checkpoint_url:
		UpperCAmelCase_			:	List[str]      =   [3, 3, 27, 3]
		UpperCAmelCase_			:	str      =   [2_56, 5_12, 10_24, 20_48]

	if "1k" in checkpoint_url:
		UpperCAmelCase_			:	Union[str, Any]      =   10_00
		UpperCAmelCase_			:	str      =   'imagenet-1k-id2label.json'
		UpperCAmelCase_			:	str      =   (1, 10_00)
	else:
		UpperCAmelCase_			:	Union[str, Any]      =   2_18_41
		UpperCAmelCase_			:	str      =   'imagenet-22k-id2label.json'
		UpperCAmelCase_			:	int      =   (1, 2_18_41)

	UpperCAmelCase_			:	List[Any]      =   'huggingface/label-files'
	UpperCAmelCase_			:	List[str]      =   num_labels
	UpperCAmelCase_			:	List[str]      =   json.load(open(hf_hub_download(_lowerCamelCase     ,							_lowerCamelCase     ,							repo_type="dataset"							)     ,							"r"							)							)
	UpperCAmelCase_			:	List[Any]      =   {int(_lowerCamelCase							): v for k, v in idalabel.items()}
	if "1k" not in checkpoint_url:
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[92_05]
		del idalabel[1_50_27]
	UpperCAmelCase_			:	int      =   idalabel
	UpperCAmelCase_			:	Union[str, Any]      =   {v: k for k, v in idalabel.items()}
	UpperCAmelCase_			:	Union[str, Any]      =   hidden_sizes
	UpperCAmelCase_			:	int      =   depths

	return config, expected_shape


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[Any]							)			-> Dict:


	"""simple docstring"""





	if "downsample_layers.0.0" in name:
		UpperCAmelCase_			:	Tuple      =   name.replace("downsample_layers.0.0"     ,							"embeddings.patch_embeddings"							)
	if "downsample_layers.0.1" in name:
		UpperCAmelCase_			:	str      =   name.replace("downsample_layers.0.1"     ,							"embeddings.norm"							)  # we rename to layernorm later on
	if "downsample_layers.1.0" in name:
		UpperCAmelCase_			:	Tuple      =   name.replace("downsample_layers.1.0"     ,							"stages.1.downsampling_layer.0"							)
	if "downsample_layers.1.1" in name:
		UpperCAmelCase_			:	Optional[int]      =   name.replace("downsample_layers.1.1"     ,							"stages.1.downsampling_layer.1"							)
	if "downsample_layers.2.0" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("downsample_layers.2.0"     ,							"stages.2.downsampling_layer.0"							)
	if "downsample_layers.2.1" in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("downsample_layers.2.1"     ,							"stages.2.downsampling_layer.1"							)
	if "downsample_layers.3.0" in name:
		UpperCAmelCase_			:	Optional[Any]      =   name.replace("downsample_layers.3.0"     ,							"stages.3.downsampling_layer.0"							)
	if "downsample_layers.3.1" in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("downsample_layers.3.1"     ,							"stages.3.downsampling_layer.1"							)
	if "stages" in name and "downsampling_layer" not in name:
		# stages.0.0. for instance should be renamed to stages.0.layers.0.
		UpperCAmelCase_			:	Dict      =   name[: len("stages.0"							)] + '.layers' + name[len("stages.0"							) :]
	if "stages" in name:
		UpperCAmelCase_			:	List[str]      =   name.replace("stages"     ,							"encoder.stages"							)
	if "norm" in name:
		UpperCAmelCase_			:	Optional[Any]      =   name.replace("norm"     ,							"layernorm"							)
	if "gamma" in name:
		UpperCAmelCase_			:	List[Any]      =   name.replace("gamma"     ,							"layer_scale_parameter"							)
	if "head" in name:
		UpperCAmelCase_			:	Dict      =   name.replace("head"     ,							"classifier"							)

	return name


def       a__    (  )			-> str:


	"""simple docstring"""





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


@torch.no_grad()
def       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]							)			-> Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   get_convnext_config(_lowerCamelCase							)
	# load original state_dict from URL
	UpperCAmelCase_			:	List[Any]      =   torch.hub.load_state_dict_from_url(_lowerCamelCase							)['model']
	# rename keys
	for key in state_dict.copy().keys():
		UpperCAmelCase_			:	Optional[int]      =   state_dict.pop(_lowerCamelCase							)
		UpperCAmelCase_			:	List[Any]      =   val
	# add prefix to all keys expect classifier head
	for key in state_dict.copy().keys():
		UpperCAmelCase_			:	Any      =   state_dict.pop(_lowerCamelCase							)
		if not key.startswith("classifier"							):
			UpperCAmelCase_			:	Optional[Any]      =   'convnext.' + key
		UpperCAmelCase_			:	Tuple      =   val

	# load HuggingFace model
	UpperCAmelCase_			:	Optional[int]      =   ConvNextForImageClassification(_lowerCamelCase							)
	model.load_state_dict(_lowerCamelCase							)
	model.eval()

	# Check outputs on an image, prepared by ConvNextImageProcessor
	UpperCAmelCase_			:	Optional[int]      =   2_24 if '224' in checkpoint_url else 3_84
	UpperCAmelCase_			:	int      =   ConvNextImageProcessor(size=_lowerCamelCase							)
	UpperCAmelCase_			:	int      =   image_processor(images=prepare_img()     ,							return_tensors="pt"							).pixel_values

	UpperCAmelCase_			:	Any      =   model(_lowerCamelCase							).logits

	# note: the logits below were obtained without center cropping
	if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
		UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor([-0.1_210, -0.6_605, 0.1_918]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
		UpperCAmelCase_			:	Union[str, Any]      =   torch.tensor([-0.4_473, -0.1_847, -0.6_365]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
		UpperCAmelCase_			:	int      =   torch.tensor([0.4_525, 0.7_539, 0.0_308]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
		UpperCAmelCase_			:	Optional[int]      =   torch.tensor([0.3_561, 0.6_350, -0.0_384]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
		UpperCAmelCase_			:	List[Any]      =   torch.tensor([0.4_174, -0.0_989, 0.1_489]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
		UpperCAmelCase_			:	Dict      =   torch.tensor([0.2_513, -0.1_349, -0.1_613]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
		UpperCAmelCase_			:	int      =   torch.tensor([1.2_980, 0.3_631, -0.1_198]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
		UpperCAmelCase_			:	str      =   torch.tensor([1.2_963, 0.1_227, 0.1_723]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
		UpperCAmelCase_			:	Any      =   torch.tensor([1.7_956, 0.8_390, 0.2_820]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
		UpperCAmelCase_			:	str      =   torch.tensor([-0.2_822, -0.0_502, -0.0_878]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
		UpperCAmelCase_			:	List[str]      =   torch.tensor([-0.5_672, -0.0_730, -0.4_348]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
		UpperCAmelCase_			:	List[Any]      =   torch.tensor([0.2_681, 0.2_365, 0.6_246]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
		UpperCAmelCase_			:	int      =   torch.tensor([-0.2_642, 0.3_931, 0.5_116]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
		UpperCAmelCase_			:	str      =   torch.tensor([-0.6_677, -0.1_873, -0.8_379]							)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
		UpperCAmelCase_			:	int      =   torch.tensor([-0.7_749, -0.2_967, -0.6_444]							)
	else:
		raise ValueError(F'''Unknown URL: {checkpoint_url}'''							)

	assert torch.allclose(logits[0, :3]     ,							_lowerCamelCase     ,							atol=1E-3							)
	assert logits.shape == expected_shape

	Path(_lowerCamelCase							).mkdir(exist_ok=_lowerCamelCase							)
	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(_lowerCamelCase							)
	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(_lowerCamelCase							)

	print("Pushing model to the hub..."							)
	UpperCAmelCase_			:	Optional[int]      =   'convnext'
	if "tiny" in checkpoint_url:
		model_name += "-tiny"
	elif "small" in checkpoint_url:
		model_name += "-small"
	elif "base" in checkpoint_url:
		model_name += "-base"
	elif "xlarge" in checkpoint_url:
		model_name += "-xlarge"
	elif "large" in checkpoint_url:
		model_name += "-large"
	if "224" in checkpoint_url:
		model_name += "-224"
	elif "384" in checkpoint_url:
		model_name += "-384"
	if "22k" in checkpoint_url and "1k" not in checkpoint_url:
		model_name += "-22k"
	if "22k" in checkpoint_url and "1k" in checkpoint_url:
		model_name += "-22k-1k"

	model.push_to_hub(
	    repo_path_or_name=Path(_lowerCamelCase     ,							_lowerCamelCase							)     ,							organization="nielsr"     ,							commit_message="Add model"     ,							)


if __name__ == "__main__":
					_lowerCamelCase	 =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
					    type=str,
					    help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)

					_lowerCamelCase	 =			parser.parse_args()
					convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












"""simple docstring"""
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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_			: Tuple				=			32






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase = 16				):
 lowerCamelCase__     :			Union[str, Any]     				=						AutoTokenizer.from_pretrained('bert-base-cased'				)
 lowerCamelCase__     :			Optional[int]     				=						load_dataset('glue'			,   'mrpc'				)

 def tokenize_function(_lowerCamelCase				):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase__     :			List[str]     				=						tokenizer(examples['sentence1']			,   examples['sentence2']			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase				)
  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():
  lowerCamelCase__     :			List[Any]     				=						datasets.map(
      _lowerCamelCase			,   batched=_lowerCamelCase			,   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
 lowerCamelCase__     :			List[Any]     				=						tokenized_datasets.rename_column('label'			,   'labels'				)

 def collate_fn(_lowerCamelCase				):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   lowerCamelCase__     :			Any     				=						16
  elif accelerator.mixed_precision != "no":
   lowerCamelCase__     :			Any     				=						8
  else:
   lowerCamelCase__     :			Dict     				=						None

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

 # Instantiate dataloaders.
 lowerCamelCase__     :			List[str]     				=						DataLoader(
     tokenized_datasets['train']			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase				)
 lowerCamelCase__     :			Tuple     				=						DataLoader(
     tokenized_datasets['validation']			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase				)

 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_			: List[str]				=			mocked_dataloaders  # noqa: F811






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'			,   _lowerCamelCase				) == "1":
  lowerCamelCase__     :			List[str]     				=						2
 # Initialize accelerator
 lowerCamelCase__     :			List[str]     				=						Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision				)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase__     :			Any     				=						config['lr']
 lowerCamelCase__     :			List[str]     				=						int(config['num_epochs']				)
 lowerCamelCase__     :			Optional[Any]     				=						int(config['seed']				)
 lowerCamelCase__     :			int     				=						int(config['batch_size']				)

 lowerCamelCase__     :			Union[str, Any]     				=						evaluate.load('glue'			,   'mrpc'				)

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(_lowerCamelCase				)				)
 def inner_training_loop(_lowerCamelCase				):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  lowerCamelCase__     :			List[str]     				=						observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  lowerCamelCase__     :			Any     				=						num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(_lowerCamelCase				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase__     :			Dict     				=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			,   return_dict=_lowerCamelCase				)

  # 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).
  lowerCamelCase__     :			Optional[Any]     				=						model.to(accelerator.device				)

  # Instantiate optimizer
  lowerCamelCase__     :			int     				=						AdamW(params=model.parameters()			,   lr=_lowerCamelCase				)
  lowerCamelCase__				,     lowerCamelCase__     :			List[Any]     				=						get_dataloaders(_lowerCamelCase			,   _lowerCamelCase				)

  # Instantiate scheduler
  lowerCamelCase__     :			int     				=						get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase			,   num_warmup_steps=100			,   num_training_steps=(len(_lowerCamelCase				) * num_epochs)			,   )

  # 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.
  lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			Optional[int]     				=						accelerator.prepare(
      _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)

  # Now we train the model
  for epoch in range(_lowerCamelCase				):
   model.train()
   for step, batch in enumerate(_lowerCamelCase				):
    # And perform gradient accumulation
    with accelerator.accumulate(_lowerCamelCase				):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device				)
     lowerCamelCase__     :			Tuple     				=						model(**_lowerCamelCase				)
     lowerCamelCase__     :			Optional[Any]     				=						outputs.loss
     accelerator.backward(_lowerCamelCase				)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

   model.eval()
   for step, batch in enumerate(_lowerCamelCase				):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device				)
    with torch.no_grad():
     lowerCamelCase__     :			int     				=						model(**_lowerCamelCase				)
    lowerCamelCase__     :			str     				=						outputs.logits.argmax(dim=-1				)
    lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						accelerator.gather_for_metrics((predictions, batch['labels'])				)
    metric.add_batch(
        predictions=_lowerCamelCase			,   references=_lowerCamelCase			,   )

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			Any     				=						argparse.ArgumentParser(description='Simple example of training script.'				)
 parser.add_argument(
     '--mixed_precision'			,   type=_lowerCamelCase			,   default=_lowerCamelCase			,   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.'				)
 lowerCamelCase__     :			List[Any]     				=						parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 lowerCamelCase__     :			Tuple     				=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
 training_function(_lowerCamelCase			,   _lowerCamelCase				)


if __name__ == "__main__":
      main()

