




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__A						:		Any          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				return TestCommand(
				    args.dataset						,			args.name						,			args.cache_dir						,			args.data_dir						,			args.all_configs						,			args.save_info or args.save_infos						,			args.ignore_verifications						,			args.force_redownload						,			args.clear_cache						,			)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= False  # to tell pytest it's not a test class




					@staticmethod
					def     _lowercase						(   _lowercase:		ArgumentParser):


									'''simple docstring'''


									__lowerCAmelCase					=  parser.add_parser("""test""",     help="""Test dataset implementation.""")
									test_parser.add_argument("""--name""",     type=_lowercase,     default=_lowercase,     help="""Dataset processing name""")
									test_parser.add_argument(
									    """--cache_dir""",     type=_lowercase,     default=_lowercase,     help="""Cache directory where the datasets are stored.""",     )
									test_parser.add_argument(
									    """--data_dir""",     type=_lowercase,     default=_lowercase,     help="""Can be used to specify a manual directory to get the files from.""",     )
									test_parser.add_argument("""--all_configs""",     action="""store_true""",     help="""Test all dataset configurations""")
									test_parser.add_argument(
									    """--save_info""",     action="""store_true""",     help="""Save the dataset infos in the dataset card (README.md)""")
									test_parser.add_argument(
									    """--ignore_verifications""",     action="""store_true""",     help="""Run the test without checksums and splits checks.""",     )
									test_parser.add_argument("""--force_redownload""",     action="""store_true""",     help="""Force dataset redownload""")
									test_parser.add_argument(
									    """--clear_cache""",     action="""store_true""",     help="""Remove downloaded files and cached datasets after each config test""",     )
									# aliases
									test_parser.add_argument("""--save_infos""",     action="""store_true""",     help="""alias to save_info""")
									test_parser.add_argument("""dataset""",     type=_lowercase,     help="""Name of the dataset to download""")
									test_parser.set_defaults(func=_test_command_factory)




					def __init__(   self:		Optional[Any],     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  dataset
									__lowerCAmelCase					=  name
									__lowerCAmelCase					=  cache_dir
									__lowerCAmelCase					=  data_dir
									__lowerCAmelCase					=  all_configs
									__lowerCAmelCase					=  save_infos
									__lowerCAmelCase					=  ignore_verifications
									__lowerCAmelCase					=  force_redownload
									__lowerCAmelCase					=  clear_cache
									if clear_cache and not cache_dir:
													print(
													    """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
													    """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
													    """Please provide a --cache_dir that will be used to test the dataset script.""")
													exit(1)
									if save_infos:
													__lowerCAmelCase					=  True







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									fl_logger().setLevel(_lowercase)
									if self._name is not None and self._all_configs:
													print("""Both parameters `config` and `all_configs` can't be used at once.""")
													exit(1)
									__lowerCAmelCase	,				__lowerCAmelCase					=  self._dataset, self._name
									__lowerCAmelCase					=  dataset_module_factory(_lowercase)
									__lowerCAmelCase					=  import_main_class(module.module_path)
									__lowerCAmelCase					=  len(builder_cls.BUILDER_CONFIGS) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

									def get_builders() -> Generator[DatasetBuilder, None, None]:
													if self._all_configs and builder_cls.BUILDER_CONFIGS:
																	for i, config in enumerate(builder_cls.BUILDER_CONFIGS):
																					if "config_name" in module.builder_kwargs:
																									yield builder_cls(
																									    cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
																					else:
																									yield builder_cls(
																									    config_name=config.name,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
													else:
																	if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs)
																	else:
																					yield builder_cls(
																					    config_name=_lowercase,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )

									for j, builder in enumerate(get_builders()):
													print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})''')
													__lowerCAmelCase					=  os.path.exists(
													    os.path.join(builder.get_imported_module_dir(),     datasets.config.DATASETDICT_INFOS_FILENAME))  # record checksums only if we need to update a (deprecated) dataset_infos.json
													builder.download_and_prepare(
													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
													    if not self._force_redownload
													    else DownloadMode.FORCE_REDOWNLOAD,     verification_mode=VerificationMode.NO_CHECKS
													    if self._ignore_verifications
													    else VerificationMode.ALL_CHECKS,     try_from_hf_gcs=_lowercase,     )
													builder.as_dataset()
													if self._save_infos:
																	builder._save_infos()

													# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
													# The dataset_infos are saved in the YAML part of the README.md

													# Let's move it to the original directory of the dataset script, to allow the user to
													# upload them on S3 at the same time afterwards.
													if self._save_infos:
																	__lowerCAmelCase					=  os.path.join(builder_cls.get_imported_module_dir(),     """README.md""")
																	__lowerCAmelCase					=  Path(_lowercase).name + """.py"""
																	__lowerCAmelCase					=  os.path.join(_lowercase,     _lowercase)
																	if os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  os.path.dirname(_lowercase)
																	elif os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  path
																	elif os.path.isdir(_lowercase):  # for local directories containing only data files
																					__lowerCAmelCase					=  path
																	else:  # in case of a remote dataset
																					__lowerCAmelCase					=  None
																					print(f'''Dataset card saved at {dataset_readme_path}''')

																	# Move dataset_info back to the user
																	if dataset_dir is not None:
																					__lowerCAmelCase					=  os.path.join(_lowercase,     """README.md""")
																					copyfile(_lowercase,     _lowercase)
																					print(f'''Dataset card saved at {user_dataset_readme_path}''')

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
													if self._clear_cache:
																	if os.path.isdir(builder._cache_dir):
																					logger.warning(f'''Clearing cache at {builder._cache_dir}''')
																					rmtree(builder._cache_dir)
																	__lowerCAmelCase					=  os.path.join(self._cache_dir,     datasets.config.DOWNLOADED_DATASETS_DIR)
																	if os.path.isdir(_lowercase):
																					logger.warning(f'''Clearing cache at {download_dir}''')
																					rmtree(_lowercase)

									print("""Test successful.""")










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__			:   Optional[Any]      			=    logging.get_logger(__name__)


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Optional[Any]     					=	ConvNextConfig()

				if "tiny" in checkpoint_url:
								_UpperCAmelCase						:  Any     					=	[3, 3, 9, 3]
								_UpperCAmelCase						:  int     					=	[96, 192, 384, 768]
				if "small" in checkpoint_url:
								_UpperCAmelCase						:  str     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  List[Any]     					=	[96, 192, 384, 768]
				if "base" in checkpoint_url:
								_UpperCAmelCase						:  Dict     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  Tuple     					=	[128, 256, 512, 1_024]
				if "large" in checkpoint_url:
								_UpperCAmelCase						:  Optional[int]     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  Optional[int]     					=	[192, 384, 768, 1_536]
				if "xlarge" in checkpoint_url:
								_UpperCAmelCase						:  Tuple     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  int     					=	[256, 512, 1_024, 2_048]

				if "1k" in checkpoint_url:
								_UpperCAmelCase						:  int     					=	1_000
								_UpperCAmelCase						:  Union[str, Any]     					=	'''imagenet-1k-id2label.json'''
								_UpperCAmelCase						:  Any     					=	(1, 1_000)
				else:
								_UpperCAmelCase						:  Union[str, Any]     					=	21_841
								_UpperCAmelCase						:  int     					=	'''imagenet-22k-id2label.json'''
								_UpperCAmelCase						:  List[str]     					=	(1, 21_841)

				_UpperCAmelCase						:  List[str]     					=	'''huggingface/label-files'''
				_UpperCAmelCase						:  Any     					=	num_labels
				_UpperCAmelCase						:  str     					=	json.load(open(hf_hub_download(_UpperCamelCase      ,					_UpperCamelCase      ,					repo_type='''dataset'''			)      ,					'''r'''			)			)
				_UpperCAmelCase						:  List[Any]     					=	{int(_UpperCamelCase			): 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_205]
								del idalabel[15_027]
				_UpperCAmelCase						:  List[str]     					=	idalabel
				_UpperCAmelCase						:  Any     					=	{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:  Union[str, Any]     					=	hidden_sizes
				_UpperCAmelCase						:  int     					=	depths

				return config, expected_shape


def 	_A	(   _UpperCamelCase			):
				if "downsample_layers.0.0" in name:
								_UpperCAmelCase						:  Any     					=	name.replace('''downsample_layers.0.0'''      ,					'''embeddings.patch_embeddings'''			)
				if "downsample_layers.0.1" in name:
								_UpperCAmelCase						:  int     					=	name.replace('''downsample_layers.0.1'''      ,					'''embeddings.norm'''			)  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								_UpperCAmelCase						:  Optional[Any]     					=	name.replace('''downsample_layers.1.0'''      ,					'''stages.1.downsampling_layer.0'''			)
				if "downsample_layers.1.1" in name:
								_UpperCAmelCase						:  Any     					=	name.replace('''downsample_layers.1.1'''      ,					'''stages.1.downsampling_layer.1'''			)
				if "downsample_layers.2.0" in name:
								_UpperCAmelCase						:  int     					=	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						:  Union[str, Any]     					=	name.replace('''downsample_layers.3.0'''      ,					'''stages.3.downsampling_layer.0'''			)
				if "downsample_layers.3.1" in name:
								_UpperCAmelCase						:  Union[str, 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						:  Any     					=	name[: len('''stages.0'''			)] + '''.layers''' + name[len('''stages.0'''			) :]
				if "stages" in name:
								_UpperCAmelCase						:  Dict     					=	name.replace('''stages'''      ,					'''encoder.stages'''			)
				if "norm" in name:
								_UpperCAmelCase						:  int     					=	name.replace('''norm'''      ,					'''layernorm'''			)
				if "gamma" in name:
								_UpperCAmelCase						:  str     					=	name.replace('''gamma'''      ,					'''layer_scale_parameter'''			)
				if "head" in name:
								_UpperCAmelCase						:  Optional[Any]     					=	name.replace('''head'''      ,					'''classifier'''			)

				return name


def 	_A	(   ):
				_UpperCAmelCase						:  Tuple     					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase						:  Union[str, Any]     					=	Image.open(requests.get(_UpperCamelCase      ,					stream=_UpperCamelCase			).raw			)
				return im


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

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

				# Check outputs on an image, prepared by ConvNextImageProcessor
				_UpperCAmelCase						:  Optional[Any]     					=	224 if '''224''' in checkpoint_url else 384
				_UpperCAmelCase						:  int     					=	ConvNextImageProcessor(size=_UpperCamelCase			)
				_UpperCAmelCase						:  Union[str, Any]     					=	image_processor(images=prepare_img()      ,					return_tensors='''pt'''			).pixel_values

				_UpperCAmelCase						:  Any     					=	model(_UpperCamelCase			).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						:  Optional[int]     					=	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						:  Optional[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						:  List[str]     					=	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						:  Any     					=	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						:  str     					=	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						:  Tuple     					=	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						:  Dict     					=	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						:  Optional[int]     					=	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						:  str     					=	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						:  List[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[Any]     					=	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						:  str     					=	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						:  List[str]     					=	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						:  Tuple     					=	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						:  Any     					=	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]      ,					_UpperCamelCase      ,					atol=1e-3			)
				assert logits.shape == expected_shape

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

				print('''Pushing model to the hub...'''			)
				_UpperCAmelCase						:  Union[str, Any]     					=	'''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(_UpperCamelCase      ,					_UpperCamelCase			)      ,					organization='''nielsr'''      ,					commit_message='''Add model'''      ,					)


if __name__ == "__main__":
					UpperCAmelCase__			:   List[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__			:   int      			=    parser.parse_args()
					convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
