



'''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()
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      ConvNextVaConfig()

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

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      1000
		SCREAMING_SNAKE_CASE_						:List[str]										=      'imagenet-1k-id2label.json'
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      (1, 1000)

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

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

		return config, expected_shape







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):







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

		return name







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:int										=      'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE_						:List[str]										=      Image.open(requests.get(_UpperCamelCase ,		stream=_UpperCamelCase     ).raw     )
		return im







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):







		if "224" in checkpoint_url:
				SCREAMING_SNAKE_CASE_						:Tuple										=      224
				SCREAMING_SNAKE_CASE_						:str										=      224 / 256
		elif "384" in checkpoint_url:
				SCREAMING_SNAKE_CASE_						:Any										=      384
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      None
		else:
				SCREAMING_SNAKE_CASE_						:List[Any]										=      512
				SCREAMING_SNAKE_CASE_						:Dict										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		print('Downloading original model from checkpoint...'     )
		# define ConvNeXTV2 configuration based on URL
		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      get_convnextva_config(_UpperCamelCase     )
		# load original state_dict from URL
		SCREAMING_SNAKE_CASE_						:int										=      torch.hub.load_state_dict_from_url(_UpperCamelCase     )['model']

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

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_						:str										=      ConvNextVaForImageClassification(_UpperCamelCase     )
		model.load_state_dict(_UpperCamelCase     )
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		SCREAMING_SNAKE_CASE_						:Tuple										=      convert_preprocessor(_UpperCamelCase     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      preprocessor(images=prepare_img() ,		return_tensors='pt'     )
		SCREAMING_SNAKE_CASE_						:List[str]										=      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":
				SCREAMING_SNAKE_CASE_						:int										=      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":
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      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":
				SCREAMING_SNAKE_CASE_						:List[str]										=      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":
				SCREAMING_SNAKE_CASE_						: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":
				SCREAMING_SNAKE_CASE_						:List[str]										=      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":
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      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":
				SCREAMING_SNAKE_CASE_						:Dict										=      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":
				SCREAMING_SNAKE_CASE_						:Dict										=      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":
				SCREAMING_SNAKE_CASE_						: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":
				SCREAMING_SNAKE_CASE_						:int										=      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":
				SCREAMING_SNAKE_CASE_						: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":
				SCREAMING_SNAKE_CASE_						:List[Any]										=      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":
				SCREAMING_SNAKE_CASE_						:int										=      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":
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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":
				SCREAMING_SNAKE_CASE_						:List[Any]										=      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":
				SCREAMING_SNAKE_CASE_						:str										=      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":
				SCREAMING_SNAKE_CASE_						:Any										=      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":
				SCREAMING_SNAKE_CASE_						: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     )

		SCREAMING_SNAKE_CASE_						: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(_UpperCamelCase     )
				preprocessor.push_to_hub(_UpperCamelCase     )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : Union[str, 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""")

							SCREAMING_SNAKE_CASE__     : Tuple				=   parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)




'''simple docstring'''

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
__lowercase:		Tuple    						=  "\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
__lowercase:		Optional[int]    						=  "\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
__lowercase:		List[str]    						=  ""

# TODO: Add the licence for the dataset here if you can find it
__lowercase:		List[Any]    						=  ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
__lowercase:		List[str]    						=  {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}







class 			UpperCAmelCase			(							datasets.GeneratorBasedBuilder):
 _lowerCamelCase			:       List[str]            =       datasets.Version('1.1.0')

 # This is an example of a dataset with multiple configurations.
 # If you don't want/need to define several sub-sets in your dataset,
 # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

 # If you need to make complex sub-parts in the datasets with configurable options
 # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
 # BUILDER_CONFIG_CLASS = MyBuilderConfig

 # You will be able to load one or the other configurations in the following list with
 # data = datasets.load_dataset('my_dataset', 'first_domain')
 # data = datasets.load_dataset('my_dataset', 'second_domain')
 _lowerCamelCase			:       Dict            =       [
     datasets.BuilderConfig(name='first_domain'		,      version=SCREAMING_SNAKE_CASE__		,      description='This part of my dataset covers a first domain'),
     datasets.BuilderConfig(name='second_domain'		,      version=SCREAMING_SNAKE_CASE__		,      description='This part of my dataset covers a second domain'),
 ]

 _lowerCamelCase			:       Any            =       'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
       UpperCamelCase__			    =    datasets.Features(
           {
               "sentence": datasets.Value("string"       ),
               "option1": datasets.Value("string"       ),
               "answer": datasets.Value("string"       )
               # These are the features of your dataset like images, labels ...
           }       )
    else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
       UpperCamelCase__			    =    datasets.Features(
           {
               "sentence": datasets.Value("string"       ),
               "option2": datasets.Value("string"       ),
               "second_domain_answer": datasets.Value("string"       )
               # These are the features of your dataset like images, labels ...
           }       )
    return datasets.DatasetInfo(
        # This is the description that will appear on the datasets page.
        description=_DESCRIPTION,       features=a_,       homepage=_HOMEPAGE,       license=_LICENSE,       citation=_CITATION,       )

 def 		lowercase_    (			self			:						Any,       a_			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    _URLS[self.config.name]
    UpperCamelCase__			    =    dl_manager.download_and_extract(a_       )
    return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,       gen_kwargs={
                "filepath": os.path.join(a_,       "train.jsonl"       ),
                "split": "train",
            },       ),
        datasets.SplitGenerator(
            name=datasets.Split.VALIDATION,       gen_kwargs={
                "filepath": os.path.join(a_,       "dev.jsonl"       ),
                "split": "dev",
            },       ),
        datasets.SplitGenerator(
            name=datasets.Split.TEST,       gen_kwargs={
                "filepath": os.path.join(a_,       "test.jsonl"       ),
                "split": "test"
            },       ),
    ]

 def 		lowercase_    (			self			:						str,       a_			:						Optional[Any],       a_			:						Tuple       ):





    """simple docstring"""



    with open(a_,       encoding="utf-8"       ) as f:
       for key, row in enumerate(a_       ):
          UpperCamelCase__			    =    json.loads(a_       )
          if self.config.name == "first_domain":
             # Yields examples as (key, example) tuples
             yield key, {
                 "sentence": data["sentence"],
                 "option1": data["option1"],
                 "answer": "" if split == "test" else data["answer"],
             }
          else:
             yield key, {
                 "sentence": data["sentence"],
                 "option2": data["option2"],
                 "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
             }