



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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


UpperCAmelCase__			:   Optional[Any]      			=    {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['MBart50Tokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   int      			=    ['MBart50TokenizerFast']


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa import MBartaaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
					import sys

					UpperCAmelCase__			:   str      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
