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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : str							):
     _A								=     ConvNextConfig()

     if "tiny" in checkpoint_url:
          _A								=     [3, 3, 9, 3]
          _A								=     [9_6, 1_9_2, 3_8_4, 7_6_8]
     if "small" in checkpoint_url:
          _A								=     [3, 3, 2_7, 3]
          _A								=     [9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          _A								=     [3, 3, 2_7, 3]
          _A								=     [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          _A								=     [3, 3, 2_7, 3]
          _A								=     [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "xlarge" in checkpoint_url:
          _A								=     [3, 3, 2_7, 3]
          _A								=     [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

     if "1k" in checkpoint_url:
          _A								=     1_0_0_0
          _A								=     'imagenet-1k-id2label.json'
          _A								=     (1, 1_0_0_0)
     else:
          _A								=     2_1_8_4_1
          _A								=     'imagenet-22k-id2label.json'
          _A								=     (1, 2_1_8_4_1)

     _A								=     'huggingface/label-files'
     _A								=     num_labels
     _A								=     json.load(open(hf_hub_download(__snake_case      ,		__snake_case      ,		repo_type='dataset'							)      ,		'r'							)							)
     _A								=     {int(__snake_case							): 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[9_2_0_5]
          del idalabel[1_5_0_2_7]
     _A								=     idalabel
     _A								=     {v: k for k, v in idalabel.items()}
     _A								=     hidden_sizes
     _A								=     depths

     return config, expected_shape





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

     return name





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





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

     # load HuggingFace model
     _A								=     ConvNextForImageClassification(__snake_case							)
     model.load_state_dict(__snake_case							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     _A								=     2_2_4 if '224' in checkpoint_url else 3_8_4
     _A								=     ConvNextImageProcessor(size=__snake_case							)
     _A								=     image_processor(images=prepare_img()      ,		return_tensors='pt'							).pixel_values

     _A								=     model(__snake_case							).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          _A								=     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":
          _A								=     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":
          _A								=     torch.tensor([0.45_25, 0.75_39, 0.03_08]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          _A								=     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":
          _A								=     torch.tensor([0.41_74, -0.09_89, 0.14_89]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          _A								=     torch.tensor([0.25_13, -0.13_49, -0.16_13]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          _A								=     torch.tensor([1.29_80, 0.36_31, -0.11_98]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          _A								=     torch.tensor([1.29_63, 0.12_27, 0.17_23]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          _A								=     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":
          _A								=     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":
          _A								=     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":
          _A								=     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":
          _A								=     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":
          _A								=     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":
          _A								=     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]      ,		__snake_case      ,		atol=1e-3							)
     assert logits.shape == expected_shape

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

     print('Pushing model to the hub...'							)
     _A								=     '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(__snake_case      ,		__snake_case							)      ,		organization='nielsr'      ,		commit_message='Add model'      ,		)


if __name__ == "__main__":
       _UpperCAmelCase						:    Any     					=					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.''',
       )

       _UpperCAmelCase						:    str     					=					parser.parse_args()
       convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        # Build iterable dataset
        if self.streaming:
             _A								=     self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
             _A								=     None
             _A								=     None
             _A								=     None
             _A								=     None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     batch.to_pandas().to_csv(
            path_or_buf=UpperCamelCase__,   header=header if (offset == 0) else False,   index=UpperCamelCase__,   **UpperCamelCase__    )
        return csv_str.encode(self.encoding    )

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,   len(self.dataset    ),   self.batch_size    ),   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     len(self.dataset    ), self.batch_size
             with multiprocessing.Pool(self.num_proc    ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv,   [(offset, header, index, to_csv_kwargs) for offset in range(0,   UpperCamelCase__,   UpperCamelCase__    )],   ),   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                       written += file_obj.write(UpperCamelCase__    )

        return written



