




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()
lowercase_		=							logging.get_logger(__name__)







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Optional[Any]:






					"""simple docstring"""






					__magic_name__ :							int		       =							ConvNextConfig()

					if "tiny" in checkpoint_url:
										__magic_name__ :							str		       =							[3, 3, 9, 3]
										__magic_name__ :							Dict		       =							[96, 192, 384, 768]
					if "small" in checkpoint_url:
										__magic_name__ :							List[Any]		       =							[3, 3, 27, 3]
										__magic_name__ :							Any		       =							[96, 192, 384, 768]
					if "base" in checkpoint_url:
										__magic_name__ :							Dict		       =							[3, 3, 27, 3]
										__magic_name__ :							str		       =							[128, 256, 512, 1024]
					if "large" in checkpoint_url:
										__magic_name__ :							Union[str, Any]		       =							[3, 3, 27, 3]
										__magic_name__ :							Any		       =							[192, 384, 768, 1536]
					if "xlarge" in checkpoint_url:
										__magic_name__ :							int		       =							[3, 3, 27, 3]
										__magic_name__ :							Optional[int]		       =							[256, 512, 1024, 2048]

					if "1k" in checkpoint_url:
										__magic_name__ :							Any		       =							1000
										__magic_name__ :							List[Any]		       =							'''imagenet-1k-id2label.json'''
										__magic_name__ :							List[str]		       =							(1, 1000)
					else:
										__magic_name__ :							Union[str, Any]		       =							2_1841
										__magic_name__ :							str		       =							'''imagenet-22k-id2label.json'''
										__magic_name__ :							Dict		       =							(1, 2_1841)

					__magic_name__ :							Optional[Any]		       =							'''huggingface/label-files'''
					__magic_name__ :							Optional[int]		       =							num_labels
					__magic_name__ :							List[Any]		       =							json.load(open(hf_hub_download(UpperCAmelCase,     UpperCAmelCase,     repo_type='''dataset'''	),     '''r'''	)	)
					__magic_name__ :							int		       =							{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[9205]
										del idalabel[1_5027]
					__magic_name__ :							Optional[int]		       =							idalabel
					__magic_name__ :							Dict		       =							{v: k for k, v in idalabel.items()}
					__magic_name__ :							Optional[Any]		       =							hidden_sizes
					__magic_name__ :							int		       =							depths

					return config, expected_shape







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Optional[Any]:






					"""simple docstring"""






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

					return name







def 			lowerCAmelCase  (     )  ->int:






					"""simple docstring"""






					__magic_name__ :							str		       =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__ :							int		       =							Image.open(requests.get(UpperCAmelCase,     stream=UpperCAmelCase	).raw	)
					return im







@torch.no_grad()
def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Optional[int]:






					"""simple docstring"""






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

					# load HuggingFace model
					__magic_name__ :							Optional[Any]		       =							ConvNextForImageClassification(UpperCAmelCase	)
					model.load_state_dict(UpperCAmelCase	)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					__magic_name__ :							str		       =							224 if '''224''' in checkpoint_url else 384
					__magic_name__ :							Dict		       =							ConvNextImageProcessor(size=UpperCAmelCase	)
					__magic_name__ :							List[Any]		       =							image_processor(images=prepare_img(),     return_tensors='''pt'''	).pixel_values

					__magic_name__ :							List[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":
										__magic_name__ :							List[Any]		       =							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":
										__magic_name__ :							Dict		       =							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":
										__magic_name__ :							str		       =							torch.tensor([0.45_25, 0.75_39, 0.03_08]	)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
										__magic_name__ :							Any		       =							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":
										__magic_name__ :							int		       =							torch.tensor([0.41_74, -0.09_89, 0.14_89]	)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
										__magic_name__ :							Union[str, Any]		       =							torch.tensor([0.25_13, -0.13_49, -0.16_13]	)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
										__magic_name__ :							Dict		       =							torch.tensor([1.29_80, 0.36_31, -0.11_98]	)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
										__magic_name__ :							Optional[int]		       =							torch.tensor([1.29_63, 0.12_27, 0.17_23]	)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
										__magic_name__ :							Optional[Any]		       =							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":
										__magic_name__ :							Optional[Any]		       =							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":
										__magic_name__ :							Tuple		       =							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":
										__magic_name__ :							int		       =							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":
										__magic_name__ :							str		       =							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":
										__magic_name__ :							str		       =							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":
										__magic_name__ :							Optional[Any]		       =							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],     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...'''	)
					__magic_name__ :							List[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__":
				lowercase_		=							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.''',
				)

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









import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase_		=							get_logger(__name__)

# Datasets
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/datasets'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
lowercase_		=							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
lowercase_		=							'''https://cdn-datasets.huggingface.co/datasets/metric'''
lowercase_		=							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
lowercase_		=							os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
lowercase_		=							HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowercase_		=							'''main'''

lowercase_		=							version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowercase_		=							{'''1''', '''ON''', '''YES''', '''TRUE'''}
lowercase_		=							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
lowercase_		=							version.parse(importlib_metadata.version('''dill'''))
lowercase_		=							version.parse(importlib_metadata.version('''pandas'''))
lowercase_		=							version.parse(importlib_metadata.version('''pyarrow'''))

lowercase_		=							os.environ.get('''USE_TF''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
lowercase_		=							os.environ.get('''USE_JAX''', '''AUTO''').upper()

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''torch''') is not None
				if TORCH_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''torch'''))
												logger.info(f"PyTorch version {TORCH_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling PyTorch because USE_TF is set''')

lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
				lowercase_		=							importlib.util.find_spec('''tensorflow''') is not None
				if TF_AVAILABLE:
								# For the metadata, we have to look for both tensorflow and tensorflow-cpu
								for package in [
								    "tensorflow",
								    "tensorflow-cpu",
								    "tensorflow-gpu",
								    "tf-nightly",
								    "tf-nightly-cpu",
								    "tf-nightly-gpu",
								    "intel-tensorflow",
								    "tensorflow-rocm",
								    "tensorflow-macos",
								]:
												try:
																lowercase_		=							version.parse(importlib_metadata.version(package))
												except importlib_metadata.PackageNotFoundError:
																continue
												else:
																break
								else:
												lowercase_		=							False
				if TF_AVAILABLE:
								if TF_VERSION.major < 2:
												logger.info(f"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
												lowercase_		=							False
								else:
												logger.info(f"TensorFlow version {TF_VERSION} available.")
else:
				logger.info('''Disabling Tensorflow because USE_TORCH is set''')


lowercase_		=							'''N/A'''
lowercase_		=							False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
				lowercase_		=							importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
				if JAX_AVAILABLE:
								try:
												lowercase_		=							version.parse(importlib_metadata.version('''jax'''))
												logger.info(f"JAX version {JAX_VERSION} available.")
								except importlib_metadata.PackageNotFoundError:
												pass
else:
				logger.info('''Disabling JAX because USE_JAX is set to False''')


lowercase_		=							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
lowercase_		=							'''N/A'''
lowercase_		=							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
				try:
								lowercase_		=							version.parse(importlib_metadata.version('''apache_beam'''))
								lowercase_		=							True
								logger.info(f"Apache Beam version {BEAM_VERSION} available.")
				except importlib_metadata.PackageNotFoundError:
								pass
else:
				logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
lowercase_		=							importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
lowercase_		=							importlib.util.find_spec('''PIL''') is not None
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
lowercase_		=							importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
lowercase_		=							importlib.util.find_spec('''rarfile''') is not None
lowercase_		=							importlib.util.find_spec('''zstandard''') is not None
lowercase_		=							importlib.util.find_spec('''lz4''') is not None
lowercase_		=							importlib.util.find_spec('''py7zr''') is not None


# Cache location
lowercase_		=							'''~/.cache'''
lowercase_		=							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
lowercase_		=							os.path.join(XDG_CACHE_HOME, '''huggingface''')
lowercase_		=							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''datasets''')
lowercase_		=							Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''metrics''')
lowercase_		=							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

lowercase_		=							os.path.join(HF_CACHE_HOME, '''modules''')
lowercase_		=							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

lowercase_		=							'''downloads'''
lowercase_		=							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

lowercase_		=							'''extracted'''
lowercase_		=							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowercase_		=							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowercase_		=							(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowercase_		=							1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowercase_		=							10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowercase_		=							4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowercase_		=							'''500MB'''

# Parquet configuration
lowercase_		=							100
lowercase_		=							100
lowercase_		=							100

# Offline mode
lowercase_		=							os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowercase_		=							0  # Disabled
lowercase_		=							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowercase_		=							'''dataset.arrow'''
lowercase_		=							'''indices.arrow'''
lowercase_		=							'''state.json'''
lowercase_		=							'''dataset_info.json'''
lowercase_		=							'''dataset_infos.json'''
lowercase_		=							'''LICENSE'''
lowercase_		=							'''metric_info.json'''
lowercase_		=							'''dataset_dict.json'''

lowercase_		=							'''datasets_modules'''

lowercase_		=							255

# Streaming
lowercase_		=							20
lowercase_		=							5

# Datasets without script
lowercase_		=							200
lowercase_		=							10
lowercase_		=							200

# Progress bars
lowercase_		=							0.0_5  # 20 progress updates per sec



