



import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A__						:						str          =      logging.get_logger(__name__)


def     UpperCamelCase(						__UpperCamelCase							:	Tuple				):
		lowerCAmelCase_    :  int             =				ConvNextVaConfig()

		if "atto" in checkpoint_url:
				lowerCAmelCase_    :  Optional[int]             =				[2, 2, 6, 2]
				lowerCAmelCase_    :  Dict             =				[40, 80, 160, 320]
		if "femto" in checkpoint_url:
				lowerCAmelCase_    :  Optional[Any]             =				[2, 2, 6, 2]
				lowerCAmelCase_    :  Any             =				[48, 96, 192, 384]
		if "pico" in checkpoint_url:
				lowerCAmelCase_    :  Union[str, Any]             =				[2, 2, 6, 2]
				lowerCAmelCase_    :  Optional[int]             =				[64, 128, 256, 512]
		if "nano" in checkpoint_url:
				lowerCAmelCase_    :  Dict             =				[2, 2, 8, 2]
				lowerCAmelCase_    :  Union[str, Any]             =				[80, 160, 320, 640]
		if "tiny" in checkpoint_url:
				lowerCAmelCase_    :  Tuple             =				[3, 3, 9, 3]
				lowerCAmelCase_    :  List[str]             =				[96, 192, 384, 768]
		if "base" in checkpoint_url:
				lowerCAmelCase_    :  List[Any]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Any             =				[128, 256, 512, 1024]
		if "large" in checkpoint_url:
				lowerCAmelCase_    :  List[Any]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Optional[int]             =				[192, 384, 768, 1536]
		if "huge" in checkpoint_url:
				lowerCAmelCase_    :  int             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Dict             =				[352, 704, 1408, 2816]

		lowerCAmelCase_    :  Any             =				1000
		lowerCAmelCase_    :  Optional[int]             =				'''imagenet-1k-id2label.json'''
		lowerCAmelCase_    :  str             =				(1, 1000)

		lowerCAmelCase_    :  Optional[int]             =				'''huggingface/label-files'''
		lowerCAmelCase_    :  Any             =				num_labels
		lowerCAmelCase_    :  Optional[Any]             =				json.load(open(hf_hub_download(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)  ,'''r'''				)				)
		lowerCAmelCase_    :  Optional[Any]             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}

		lowerCAmelCase_    :  Tuple             =				idalabel
		lowerCAmelCase_    :  List[str]             =				{v: k for k, v in idalabel.items()}
		lowerCAmelCase_    :  Optional[Any]             =				hidden_sizes
		lowerCAmelCase_    :  int             =				depths

		return config, expected_shape


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

		return name


def     UpperCamelCase(						):
		lowerCAmelCase_    :  List[Any]             =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_    :  int             =				Image.open(requests.get(__UpperCamelCase  ,stream=__UpperCamelCase				).raw				)
		return im


def     UpperCamelCase(						__UpperCamelCase							:	str				):
		if "224" in checkpoint_url:
				lowerCAmelCase_    :  Dict             =				224
				lowerCAmelCase_    :  List[str]             =				224 / 256
		elif "384" in checkpoint_url:
				lowerCAmelCase_    :  List[str]             =				384
				lowerCAmelCase_    :  Any             =				None
		else:
				lowerCAmelCase_    :  int             =				512
				lowerCAmelCase_    :  int             =				None

		return ConvNextImageProcessor(
		    size=__UpperCamelCase  ,crop_pct=__UpperCamelCase  ,image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]  ,resample=PILImageResampling.BICUBIC  ,)


@torch.no_grad()
def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Union[str, Any]				):
		print('''Downloading original model from checkpoint...'''				)
		# define ConvNeXTV2 configuration based on URL
		lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				get_convnextva_config(__UpperCamelCase				)
		# load original state_dict from URL
		lowerCAmelCase_    :  List[str]             =				torch.hub.load_state_dict_from_url(__UpperCamelCase				)['''model''']

		print('''Converting model parameters...'''				)
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_    :  Dict             =				state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase_    :  str             =				val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict.pop(__UpperCamelCase				)
				if not key.startswith('''classifier'''				):
						lowerCAmelCase_    :  int             =				'''convnextv2.''' + key
				lowerCAmelCase_    :  str             =				val

		# load HuggingFace model
		lowerCAmelCase_    :  int             =				ConvNextVaForImageClassification(__UpperCamelCase				)
		model.load_state_dict(__UpperCamelCase				)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCAmelCase_    :  Dict             =				convert_preprocessor(__UpperCamelCase				)
		lowerCAmelCase_    :  Optional[int]             =				preprocessor(images=prepare_img()  ,return_tensors='''pt'''				)
		lowerCAmelCase_    :  Tuple             =				model(**__UpperCamelCase				).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
				lowerCAmelCase_    :  int             =				torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
				lowerCAmelCase_    :  Dict             =				torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
				lowerCAmelCase_    :  Optional[int]             =				torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
				lowerCAmelCase_    :  str             =				torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
				lowerCAmelCase_    :  str             =				torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
				lowerCAmelCase_    :  Any             =				torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
				lowerCAmelCase_    :  Dict             =				torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]				)
		else:
				raise ValueError(f"""Unknown URL: {checkpoint_url}"""				)

		assert torch.allclose(logits[0, :5]  ,__UpperCamelCase  ,atol=1e-3				)
		assert logits.shape == expected_shape
		print('''Model outputs match the original results!'''				)

		if save_model:
				print('''Saving model to local...'''				)
				# Create folder to save model
				if not os.path.isdir(__UpperCamelCase				):
						os.mkdir(__UpperCamelCase				)

				model.save_pretrained(__UpperCamelCase				)
				preprocessor.save_pretrained(__UpperCamelCase				)

		lowerCAmelCase_    :  List[str]             =				'''convnextv2'''
		if "atto" in checkpoint_url:
				model_name += "-atto"
		if "femto" in checkpoint_url:
				model_name += "-femto"
		if "pico" in checkpoint_url:
				model_name += "-pico"
		if "nano" in checkpoint_url:
				model_name += "-nano"
		elif "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "large" in checkpoint_url:
				model_name += "-large"
		elif "huge" in checkpoint_url:
				model_name += "-huge"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		elif "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"
		elif "1k" in checkpoint_url:
				model_name += "-1k"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		elif "512" in checkpoint_url:
				model_name += "-512"

		if push_to_hub:
				print(f"""Pushing {model_name} to the hub..."""				)
				model.push_to_hub(__UpperCamelCase				)
				preprocessor.push_to_hub(__UpperCamelCase				)


if __name__ == "__main__":
						A__						:						Optional[int]          =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
						    type=str,
						    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''model''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

						A__						:						List[str]          =      parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





