



'''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()
A_				:					int							=      logging.get_logger(__name__)




def        snake_case_   (	lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					ConvNextVaConfig()

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

       _UpperCAmelCase    :		Optional[Any]     	=					1000
       _UpperCAmelCase    :		str     	=					"""imagenet-1k-id2label.json"""
       _UpperCAmelCase    :		Tuple     	=					(1, 1000)

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

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

       return config, expected_shape




def        snake_case_   (	lowerCAmelCase_						)->						str:





       '''simple docstring'''


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

       return name




def        snake_case_   (	)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[Any]     	=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase    :		Optional[int]     	=					Image.open(requests.get(lowerCAmelCase_			,  stream=lowerCAmelCase_						).raw						)
       return im




def        snake_case_   (	lowerCAmelCase_						)->						str:





       '''simple docstring'''


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

       return ConvNextImageProcessor(
           size=lowerCAmelCase_			,  crop_pct=lowerCAmelCase_			,  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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  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    :		str     	=					torch.hub.load_state_dict_from_url(lowerCAmelCase_						)["""model"""]

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

       # load HuggingFace model
       _UpperCAmelCase    :		List[str]     	=					ConvNextVaForImageClassification(lowerCAmelCase_						)
       model.load_state_dict(lowerCAmelCase_						)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       _UpperCAmelCase    :		Any     	=					convert_preprocessor(lowerCAmelCase_						)
       _UpperCAmelCase    :		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    :		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":
              _UpperCAmelCase    :		Optional[Any]     	=					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":
              _UpperCAmelCase    :		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":
              _UpperCAmelCase    :		Optional[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":
              _UpperCAmelCase    :		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":
              _UpperCAmelCase    :		Tuple     	=					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":
              _UpperCAmelCase    :		int     	=					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":
              _UpperCAmelCase    :		str     	=					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":
              _UpperCAmelCase    :		Tuple     	=					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":
              _UpperCAmelCase    :		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":
              _UpperCAmelCase    :		Optional[int]     	=					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":
              _UpperCAmelCase    :		str     	=					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":
              _UpperCAmelCase    :		Union[str, Any]     	=					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":
              _UpperCAmelCase    :		Tuple     	=					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":
              _UpperCAmelCase    :		int     	=					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":
              _UpperCAmelCase    :		Optional[int]     	=					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":
              _UpperCAmelCase    :		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":
              _UpperCAmelCase    :		str     	=					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]			,  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__":
   A_				:					Tuple							=      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_				:					str							=      parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )










"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






