
"""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()
__lowerCAmelCase							:  Any 				=		logging.get_logger(__name__)

def __snake_case (      UpperCamelCase    )       ->    Union[str, Any]:


		"""simple docstring"""





		a__   		=     ConvNextVaConfig()

		if "atto" in checkpoint_url:
				a__   		=     [2, 2, 6, 2]
				a__   		=     [40, 80, 160, 320]
		if "femto" in checkpoint_url:
				a__   		=     [2, 2, 6, 2]
				a__   		=     [48, 96, 192, 384]
		if "pico" in checkpoint_url:
				a__   		=     [2, 2, 6, 2]
				a__   		=     [64, 128, 256, 512]
		if "nano" in checkpoint_url:
				a__   		=     [2, 2, 8, 2]
				a__   		=     [80, 160, 320, 640]
		if "tiny" in checkpoint_url:
				a__   		=     [3, 3, 9, 3]
				a__   		=     [96, 192, 384, 768]
		if "base" in checkpoint_url:
				a__   		=     [3, 3, 27, 3]
				a__   		=     [128, 256, 512, 1_024]
		if "large" in checkpoint_url:
				a__   		=     [3, 3, 27, 3]
				a__   		=     [192, 384, 768, 1_536]
		if "huge" in checkpoint_url:
				a__   		=     [3, 3, 27, 3]
				a__   		=     [352, 704, 1_408, 2_816]

		a__   		=     1_000
		a__   		=     '''imagenet-1k-id2label.json'''
		a__   		=     (1, 1_000)

		a__   		=     '''huggingface/label-files'''
		a__   		=     num_labels
		a__   		=     json.load(open(hf_hub_download(UpperCamelCase						,					UpperCamelCase						,					repo_type='''dataset'''    )						,					'''r'''    )    )
		a__   		=     {int(UpperCamelCase    ): v for k, v in idalabel.items()}

		a__   		=     idalabel
		a__   		=     {v: k for k, v in idalabel.items()}
		a__   		=     hidden_sizes
		a__   		=     depths

		return config, expected_shape

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if "downsample_layers.0.0" in name:
				a__   		=     name.replace('''downsample_layers.0.0'''						,					'''embeddings.patch_embeddings'''    )
		if "downsample_layers.0.1" in name:
				a__   		=     name.replace('''downsample_layers.0.1'''						,					'''embeddings.norm'''    )  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				a__   		=     name.replace('''downsample_layers.1.0'''						,					'''stages.1.downsampling_layer.0'''    )
		if "downsample_layers.1.1" in name:
				a__   		=     name.replace('''downsample_layers.1.1'''						,					'''stages.1.downsampling_layer.1'''    )
		if "downsample_layers.2.0" in name:
				a__   		=     name.replace('''downsample_layers.2.0'''						,					'''stages.2.downsampling_layer.0'''    )
		if "downsample_layers.2.1" in name:
				a__   		=     name.replace('''downsample_layers.2.1'''						,					'''stages.2.downsampling_layer.1'''    )
		if "downsample_layers.3.0" in name:
				a__   		=     name.replace('''downsample_layers.3.0'''						,					'''stages.3.downsampling_layer.0'''    )
		if "downsample_layers.3.1" in name:
				a__   		=     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.
				a__   		=     name[: len('''stages.0'''    )] + '''.layers''' + name[len('''stages.0'''    ) :]
		if "gamma" in name:
				a__   		=     name.replace('''gamma'''						,					'''weight'''    )
		if "beta" in name:
				a__   		=     name.replace('''beta'''						,					'''bias'''    )
		if "stages" in name:
				a__   		=     name.replace('''stages'''						,					'''encoder.stages'''    )
		if "norm" in name:
				a__   		=     name.replace('''norm'''						,					'''layernorm'''    )
		if "head" in name:
				a__   		=     name.replace('''head'''						,					'''classifier'''    )

		return name

def __snake_case (      )       ->    Any:


		"""simple docstring"""





		a__   		=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		a__   		=     Image.open(requests.get(UpperCamelCase						,					stream=UpperCamelCase    ).raw    )
		return im

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if "224" in checkpoint_url:
				a__   		=     224
				a__   		=     224 / 256
		elif "384" in checkpoint_url:
				a__   		=     384
				a__   		=     None
		else:
				a__   		=     512
				a__   		=     None

		return ConvNextImageProcessor(
		    size=UpperCamelCase						,					crop_pct=UpperCamelCase						,					image_mean=[0.4_85, 0.4_56, 0.4_06]						,					image_std=[0.2_29, 0.2_24, 0.2_25]						,					resample=PILImageResampling.BICUBIC						,					)

@torch.no_grad()
def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    Tuple:


		"""simple docstring"""





		print('''Downloading original model from checkpoint...'''    )
		# define ConvNeXTV2 configuration based on URL
		a__		,	a__   		=     get_convnextva_config(UpperCamelCase    )
		# load original state_dict from URL
		a__   		=     torch.hub.load_state_dict_from_url(UpperCamelCase    )['''model''']

		print('''Converting model parameters...'''    )
		# rename keys
		for key in state_dict.copy().keys():
				a__   		=     state_dict.pop(UpperCamelCase    )
				a__   		=     val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				a__   		=     state_dict.pop(UpperCamelCase    )
				if not key.startswith('''classifier'''    ):
						a__   		=     '''convnextv2.''' + key
				a__   		=     val

		# load HuggingFace model
		a__   		=     ConvNextVaForImageClassification(UpperCamelCase    )
		model.load_state_dict(UpperCamelCase    )
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		a__   		=     convert_preprocessor(UpperCamelCase    )
		a__   		=     preprocessor(images=prepare_img()						,					return_tensors='''pt'''    )
		a__   		=     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":
				a__   		=     torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
				a__   		=     torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
				a__   		=     torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
				a__   		=     torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
				a__   		=     torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
				a__   		=     torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
				a__   		=     torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
				a__   		=     torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
				a__   		=     torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
				a__   		=     torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
				a__   		=     torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
				a__   		=     torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]    )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
				a__   		=     torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]    )
		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    )

		a__   		=     '''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__":
					__lowerCAmelCase							:  List[Any] 				=		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''')

					__lowerCAmelCase							:  int 				=		parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)






"""simple docstring"""




from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Optional[Any]         =		'MarkupLMFeatureExtractor'
	snake_case__							:		Tuple         =		('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
	snake_case__							:		Dict         =		True


	def __call__(  self :Any       ,    __magic_name__ :Dict=None       ,    __magic_name__ :Dict=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :bool = True       ,    __magic_name__ :Union[bool, str, PaddingStrategy] = False       ,    __magic_name__ :Union[bool, str, TruncationStrategy] = None       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :int = 0       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = True       ,    __magic_name__ :Optional[Union[str, TensorType]] = None       ,    **__magic_name__ :Optional[Any]       ,    )			->   BatchEncoding:

			'''simple docstring'''






			if self.parse_html:
					if html_strings is None:
							raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''			)

					if nodes is not None or xpaths is not None or node_labels is not None:
							raise ValueError(
							    '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''			)

					a__   		=     self.feature_extractor(__magic_name__			)
					a__   		=     features['''nodes''']
					a__   		=     features['''xpaths''']
			else:
					if html_strings is not None:
							raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''			)
					if nodes is None or xpaths is None:
							raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''			)

        # # second, apply the tokenizer
			if questions is not None and self.parse_html:
					if isinstance(__magic_name__       ,    __magic_name__			):
							a__   		=     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

			a__   		=     self.tokenizer(
			    text=questions if questions is not None else nodes       ,    text_pair=nodes if questions is not None else None       ,    xpaths=__magic_name__       ,    node_labels=__magic_name__       ,    add_special_tokens=__magic_name__       ,    padding=__magic_name__       ,    truncation=__magic_name__       ,    max_length=__magic_name__       ,    stride=__magic_name__       ,    pad_to_multiple_of=__magic_name__       ,    return_token_type_ids=__magic_name__       ,    return_attention_mask=__magic_name__       ,    return_overflowing_tokens=__magic_name__       ,    return_special_tokens_mask=__magic_name__       ,    return_offsets_mapping=__magic_name__       ,    return_length=__magic_name__       ,    verbose=__magic_name__       ,    return_tensors=__magic_name__       ,    **__magic_name__       ,    )

			return encoded_inputs


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Optional[int]       ,    **__magic_name__ :Any			)			->   int:

			'''simple docstring'''






			return self.tokenizer.batch_decode(*__magic_name__       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   Dict:

			'''simple docstring'''






			return self.tokenizer.decode(*__magic_name__       ,    **__magic_name__			)






	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.tokenizer.model_input_names
			return tokenizer_input_names




