
"""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        = logging.get_logger(__name__)




def     a						(							snake_case__: Optional[int] ):


				'''simple docstring'''
				lowercase_					  = ConvNextVaConfig()

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

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

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

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

				return config, expected_shape




def     a						(							snake_case__: Dict ):


				'''simple docstring'''

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

				return name




def     a						(							):


				'''simple docstring'''
				lowercase_					  = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase_					  = Image.open(requests.get(snake_case__						,       stream=snake_case__ ).raw )
				return im




def     a						(							snake_case__: Union[str, Any] ):


				'''simple docstring'''

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

				return ConvNextImageProcessor(
				    size=snake_case__						,       crop_pct=snake_case__						,       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     a						(							snake_case__: List[str]						,       snake_case__: Optional[Any]						,       snake_case__: Optional[Any]						,       snake_case__: str ):


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

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

				# load HuggingFace model
				lowercase_					  = ConvNextVaForImageClassification(snake_case__ )
				model.load_state_dict(snake_case__ )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				lowercase_					  = convert_preprocessor(snake_case__ )
				lowercase_					  = preprocessor(images=prepare_img()						,       return_tensors='''pt''' )
				lowercase_					  = model(**snake_case__ ).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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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":
								lowercase_					  = 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]						,       snake_case__						,       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(snake_case__ ):
												os.mkdir(snake_case__ )

								model.save_pretrained(snake_case__ )
								preprocessor.save_pretrained(snake_case__ )

				lowercase_					  = '''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(snake_case__ )
								preprocessor.push_to_hub(snake_case__ )


if __name__ == "__main__":
						__a        = 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        = parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




