



'''simple docstring'''
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()
_UpperCamelCase											=			logging.get_logger(__name__)


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[int]										=     ConvNextVaConfig()

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

			__UpperCAmelCase   :      Union[str, Any]										=     1000
			__UpperCAmelCase   :      Union[str, Any]										=     """imagenet-1k-id2label.json"""
			__UpperCAmelCase   :      int										=     (1, 1000)

			__UpperCAmelCase   :      Dict										=     """huggingface/label-files"""
			__UpperCAmelCase   :      Dict										=     num_labels
			__UpperCAmelCase   :      Union[str, Any]										=     json.load(open(hf_hub_download(lowerCAmelCase__			,							lowerCAmelCase__			,							repo_type="""dataset""" )			,							"""r""" ) )
			__UpperCAmelCase   :      Dict										=     {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}

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

			return config, expected_shape


def   lowercase_	(   lowerCAmelCase__	:  Any ):

			"""simple docstring"""





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

			return name


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__UpperCAmelCase   :      Union[str, Any]										=     Image.open(requests.get(lowerCAmelCase__			,							stream=lowerCAmelCase__ ).raw )
			return im


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





			if "224" in checkpoint_url:
						__UpperCAmelCase   :      int										=     224
						__UpperCAmelCase   :      List[str]										=     224 / 256
			elif "384" in checkpoint_url:
						__UpperCAmelCase   :      Tuple										=     384
						__UpperCAmelCase   :      str										=     None
			else:
						__UpperCAmelCase   :      Dict										=     512
						__UpperCAmelCase   :      int										=     None

			return ConvNextImageProcessor(
			    size=lowerCAmelCase__			,							crop_pct=lowerCAmelCase__			,							image_mean=[0.485, 0.456, 0.406]			,							image_std=[0.229, 0.224, 0.225]			,							resample=PILImageResampling.BICUBIC			,							)


@torch.no_grad()
def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Any ):

			"""simple docstring"""





			print("""Downloading original model from checkpoint...""" )
			# define ConvNeXTV2 configuration based on URL
			__UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     get_convnextva_config(lowerCAmelCase__ )
			# load original state_dict from URL
			__UpperCAmelCase   :      Tuple										=     torch.hub.load_state_dict_from_url(lowerCAmelCase__ )["""model"""]

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

			# load HuggingFace model
			__UpperCAmelCase   :      str										=     ConvNextVaForImageClassification(lowerCAmelCase__ )
			model.load_state_dict(lowerCAmelCase__ )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			__UpperCAmelCase   :      Union[str, Any]										=     convert_preprocessor(lowerCAmelCase__ )
			__UpperCAmelCase   :      Optional[int]										=     preprocessor(images=prepare_img()			,							return_tensors="""pt""" )
			__UpperCAmelCase   :      List[Any]										=     model(**lowerCAmelCase__ ).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":
						__UpperCAmelCase   :      int										=     torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						__UpperCAmelCase   :      List[Any]										=     torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						__UpperCAmelCase   :      Tuple										=     torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						__UpperCAmelCase   :      Optional[Any]										=     torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						__UpperCAmelCase   :      str										=     torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						__UpperCAmelCase   :      Tuple										=     torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						__UpperCAmelCase   :      int										=     torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						__UpperCAmelCase   :      Optional[Any]										=     torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						__UpperCAmelCase   :      Tuple										=     torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						__UpperCAmelCase   :      List[Any]										=     torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						__UpperCAmelCase   :      str										=     torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						__UpperCAmelCase   :      Optional[Any]										=     torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						__UpperCAmelCase   :      int										=     torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						__UpperCAmelCase   :      int										=     torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						__UpperCAmelCase   :      List[str]										=     torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						__UpperCAmelCase   :      Any										=     torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						__UpperCAmelCase   :      Dict										=     torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826] )
			else:
						raise ValueError(f'Unknown URL: {checkpoint_url}' )

			assert torch.allclose(logits[0, :5]			,							lowerCAmelCase__			,							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(lowerCAmelCase__ ):
									os.mkdir(lowerCAmelCase__ )

						model.save_pretrained(lowerCAmelCase__ )
						preprocessor.save_pretrained(lowerCAmelCase__ )

			__UpperCAmelCase   :      List[Any]										=     """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(lowerCAmelCase__ )
						preprocessor.push_to_hub(lowerCAmelCase__ )


if __name__ == "__main__":
					_UpperCamelCase											=			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''')

					_UpperCamelCase											=			parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




