

'''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				:   Tuple      				=  logging.get_logger(__name__)


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any       )   ->    List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=ConvNextConfig()

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

     if "1k" in checkpoint_url:
          _SCREAMING_SNAKE_CASE												=10_00
          _SCREAMING_SNAKE_CASE												='imagenet-1k-id2label.json'
          _SCREAMING_SNAKE_CASE												=(1, 10_00)
     else:
          _SCREAMING_SNAKE_CASE												=2_18_41
          _SCREAMING_SNAKE_CASE												='imagenet-22k-id2label.json'
          _SCREAMING_SNAKE_CASE												=(1, 2_18_41)

     _SCREAMING_SNAKE_CASE												='huggingface/label-files'
     _SCREAMING_SNAKE_CASE												=num_labels
     _SCREAMING_SNAKE_CASE												=json.load(open(hf_hub_download(lowerCAmelCase__							,				lowerCAmelCase__							,				repo_type='dataset'       )							,				'r'       )       )
     _SCREAMING_SNAKE_CASE												={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]
     _SCREAMING_SNAKE_CASE												=idalabel
     _SCREAMING_SNAKE_CASE												={v: k for k, v in idalabel.items()}
     _SCREAMING_SNAKE_CASE												=hidden_sizes
     _SCREAMING_SNAKE_CASE												=depths

     return config, expected_shape


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict       )   ->    List[str]:
     """simple docstring"""




     if "downsample_layers.0.0" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.0.0'							,				'embeddings.patch_embeddings'       )
     if "downsample_layers.0.1" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.0.1'							,				'embeddings.norm'       )  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.1.0'							,				'stages.1.downsampling_layer.0'       )
     if "downsample_layers.1.1" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.1.1'							,				'stages.1.downsampling_layer.1'       )
     if "downsample_layers.2.0" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.2.0'							,				'stages.2.downsampling_layer.0'       )
     if "downsample_layers.2.1" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.2.1'							,				'stages.2.downsampling_layer.1'       )
     if "downsample_layers.3.0" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('downsample_layers.3.0'							,				'stages.3.downsampling_layer.0'       )
     if "downsample_layers.3.1" in name:
          _SCREAMING_SNAKE_CASE												=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.
          _SCREAMING_SNAKE_CASE												=name[: len('stages.0'       )] + '.layers' + name[len('stages.0'       ) :]
     if "stages" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('stages'							,				'encoder.stages'       )
     if "norm" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('norm'							,				'layernorm'       )
     if "gamma" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('gamma'							,				'layer_scale_parameter'       )
     if "head" in name:
          _SCREAMING_SNAKE_CASE												=name.replace('head'							,				'classifier'       )

     return name


def  _lowerCAmelCase      (		)   ->    Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												='http://images.cocodataset.org/val2017/000000039769.jpg'
     _SCREAMING_SNAKE_CASE												=Image.open(requests.get(lowerCAmelCase__							,				stream=lowerCAmelCase__       ).raw       )
     return im


@torch.no_grad()
def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  int       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=get_convnext_config(lowerCAmelCase__       )
     # load original state_dict from URL
     _SCREAMING_SNAKE_CASE												=torch.hub.load_state_dict_from_url(lowerCAmelCase__       )['model']
     # rename keys
     for key in state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE												=state_dict.pop(lowerCAmelCase__       )
          _SCREAMING_SNAKE_CASE												=val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE												=state_dict.pop(lowerCAmelCase__       )
          if not key.startswith('classifier'       ):
               _SCREAMING_SNAKE_CASE												='convnext.' + key
          _SCREAMING_SNAKE_CASE												=val

     # load HuggingFace model
     _SCREAMING_SNAKE_CASE												=ConvNextForImageClassification(lowerCAmelCase__       )
     model.load_state_dict(lowerCAmelCase__       )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     _SCREAMING_SNAKE_CASE												=2_24 if '224' in checkpoint_url else 3_84
     _SCREAMING_SNAKE_CASE												=ConvNextImageProcessor(size=lowerCAmelCase__       )
     _SCREAMING_SNAKE_CASE												=image_processor(images=prepare_img()							,				return_tensors='pt'       ).pixel_values

     _SCREAMING_SNAKE_CASE												=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":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.12_10, -0.66_05, 0.19_18]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.44_73, -0.18_47, -0.63_65]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([0.45_25, 0.75_39, 0.03_08]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([0.35_61, 0.63_50, -0.03_84]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([0.41_74, -0.09_89, 0.14_89]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([0.25_13, -0.13_49, -0.16_13]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([1.29_80, 0.36_31, -0.11_98]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([1.29_63, 0.12_27, 0.17_23]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([1.79_56, 0.83_90, 0.28_20]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.28_22, -0.05_02, -0.08_78]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.56_72, -0.07_30, -0.43_48]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([0.26_81, 0.23_65, 0.62_46]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.26_42, 0.39_31, 0.51_16]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.66_77, -0.18_73, -0.83_79]       )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          _SCREAMING_SNAKE_CASE												=torch.tensor([-0.77_49, -0.29_67, -0.64_44]       )
     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...'       )
     _SCREAMING_SNAKE_CASE												='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				:   int      				=  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				:   List[Any]      				=  parser.parse_args()
     convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase				:   int      				=  logging.getLogger(__name__)







@dataclass
class 							A__					:
  A__					=       field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
  A__					=       field(default=A__  ,     metadata={'help': 'Set this flag to use fast tokenization.'}						)
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  A__					=       field(
      default=A__  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,     )


def  _lowerCAmelCase      (		)   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

     if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir       )
         and os.listdir(training_args.output_dir       )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              ' --overwrite_output_dir to overcome.'       )

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,				)
     logger.warning(
         'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,				training_args.local_rank							,				training_args.device							,				training_args.n_gpu							,				bool(training_args.local_rank != -1       )							,				training_args.fpaa							,				)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'							,				_UpperCamelCase       )

     # Prepare Question-Answering task
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _SCREAMING_SNAKE_CASE												=AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)
     _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				use_fast=_UpperCamelCase							,				)
     _SCREAMING_SNAKE_CASE												=AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path							,				from_tf=bool('.ckpt' in model_args.model_name_or_path       )							,				config=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)

     # Get datasets
     _SCREAMING_SNAKE_CASE												=hasattr(model.config							,				'lang2id'       )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir       )
         if training_args.do_train
         else None
     )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				mode='dev'							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)
         if training_args.do_eval
         else None
     )

     # Data collator
     _SCREAMING_SNAKE_CASE												=DataCollatorWithPadding(_UpperCamelCase							,				pad_to_multiple_of=8       ) if training_args.fpaa else None

     # Initialize our Trainer
     _SCREAMING_SNAKE_CASE												=Trainer(
         model=_UpperCamelCase							,				args=_UpperCamelCase							,				train_dataset=_UpperCamelCase							,				eval_dataset=_UpperCamelCase							,				data_collator=_UpperCamelCase							,				)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
