
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__	:  List[str]      =   logging.get_logger(__name__)






def 						lowerCamelCase__							(      a   )   ->     List[Any]:
			_A:						Any	    = ConvNextConfig()

			if "tiny" in checkpoint_url:
						_A:						Any	    = [3, 3, 9, 3]
						_A:						List[Any]	    = [96, 1_92, 3_84, 7_68]
			if "small" in checkpoint_url:
						_A:						List[Any]	    = [3, 3, 27, 3]
						_A:						int	    = [96, 1_92, 3_84, 7_68]
			if "base" in checkpoint_url:
						_A:						int	    = [3, 3, 27, 3]
						_A:						Tuple	    = [1_28, 2_56, 5_12, 10_24]
			if "large" in checkpoint_url:
						_A:						Tuple	    = [3, 3, 27, 3]
						_A:						str	    = [1_92, 3_84, 7_68, 15_36]
			if "xlarge" in checkpoint_url:
						_A:						str	    = [3, 3, 27, 3]
						_A:						Tuple	    = [2_56, 5_12, 10_24, 20_48]

			if "1k" in checkpoint_url:
						_A:						Union[str, Any]	    = 10_00
						_A:						List[Any]	    = '''imagenet-1k-id2label.json'''
						_A:						Union[str, Any]	    = (1, 10_00)
			else:
						_A:						Optional[Any]	    = 2_18_41
						_A:						str	    = '''imagenet-22k-id2label.json'''
						_A:						Optional[int]	    = (1, 2_18_41)

			_A:						Any	    = '''huggingface/label-files'''
			_A:						Any	    = num_labels
			_A:						Tuple	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
			_A:						int	    = {int(a   ): v for k, v in idalabel.items()}
			if "1k" not in checkpoint_url:
						# this dataset contains 21843 labels but the model only has 21841
						# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
						del idalabel[92_05]
						del idalabel[1_50_27]
			_A:						str	    = idalabel
			_A:						List[str]	    = {v: k for k, v in idalabel.items()}
			_A:						Tuple	    = hidden_sizes
			_A:						List[str]	    = depths

			return config, expected_shape






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

			return name






def 						lowerCamelCase__							(      )   ->     Any:
			_A:						Union[str, Any]	    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			_A:						Union[str, Any]	    = Image.open(requests.get(a	,	stream=a   ).raw   )
			return im






@torch.no_grad()
def 						lowerCamelCase__							(      a	,	a   )   ->     List[Any]:
			_A:						int	    = get_convnext_config(a   )
			# load original state_dict from URL
			_A:						Any	    = torch.hub.load_state_dict_from_url(a   )['''model''']
			# rename keys
			for key in state_dict.copy().keys():
						_A:						List[str]	    = state_dict.pop(a   )
						_A:						Tuple	    = val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						_A:						List[Any]	    = state_dict.pop(a   )
						if not key.startswith('''classifier'''   ):
									_A:						Dict	    = '''convnext.''' + key
						_A:						Optional[int]	    = val

			# load HuggingFace model
			_A:						Dict	    = ConvNextForImageClassification(a   )
			model.load_state_dict(a   )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			_A:						Optional[int]	    = 2_24 if '''224''' in checkpoint_url else 3_84
			_A:						int	    = ConvNextImageProcessor(size=a   )
			_A:						Union[str, Any]	    = image_processor(images=prepare_img()	,	return_tensors='''pt'''   ).pixel_values

			_A:						str	    = model(a   ).logits

			# note: the logits below were obtained without center cropping
			if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
						_A:						Dict	    = torch.tensor([-0.1210, -0.6605, 0.1918]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
						_A:						Union[str, Any]	    = torch.tensor([-0.4473, -0.1847, -0.6365]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
						_A:						List[Any]	    = torch.tensor([0.4525, 0.7539, 0.0308]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
						_A:						Optional[int]	    = torch.tensor([0.3561, 0.6350, -0.0384]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
						_A:						int	    = torch.tensor([0.4174, -0.0989, 0.1489]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
						_A:						List[Any]	    = torch.tensor([0.2513, -0.1349, -0.1613]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
						_A:						List[Any]	    = torch.tensor([1.2980, 0.3631, -0.1198]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
						_A:						str	    = torch.tensor([1.2963, 0.1227, 0.1723]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
						_A:						Union[str, Any]	    = torch.tensor([1.7956, 0.8390, 0.2820]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
						_A:						Tuple	    = torch.tensor([-0.2822, -0.0502, -0.0878]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
						_A:						Tuple	    = torch.tensor([-0.5672, -0.0730, -0.4348]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
						_A:						Union[str, Any]	    = torch.tensor([0.2681, 0.2365, 0.6246]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
						_A:						Optional[int]	    = torch.tensor([-0.2642, 0.3931, 0.5116]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
						_A:						Any	    = torch.tensor([-0.6677, -0.1873, -0.8379]   )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
						_A:						str	    = torch.tensor([-0.7749, -0.2967, -0.6444]   )
			else:
						raise ValueError(f"""Unknown URL: {checkpoint_url}"""   )

			assert torch.allclose(logits[0, :3]	,	a	,	atol=1E-3   )
			assert logits.shape == expected_shape

			Path(a   ).mkdir(exist_ok=a   )
			print(f"""Saving model to {pytorch_dump_folder_path}"""   )
			model.save_pretrained(a   )
			print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
			image_processor.save_pretrained(a   )

			print('''Pushing model to the hub...'''   )
			_A:						Any	    = '''convnext'''
			if "tiny" in checkpoint_url:
						model_name += "-tiny"
			elif "small" in checkpoint_url:
						model_name += "-small"
			elif "base" in checkpoint_url:
						model_name += "-base"
			elif "xlarge" in checkpoint_url:
						model_name += "-xlarge"
			elif "large" in checkpoint_url:
						model_name += "-large"
			if "224" in checkpoint_url:
						model_name += "-224"
			elif "384" in checkpoint_url:
						model_name += "-384"
			if "22k" in checkpoint_url and "1k" not in checkpoint_url:
						model_name += "-22k"
			if "22k" in checkpoint_url and "1k" in checkpoint_url:
						model_name += "-22k-1k"

			model.push_to_hub(
			    repo_path_or_name=Path(a	,	a   )	,	organization='''nielsr'''	,	commit_message='''Add model'''	,	)


if __name__ == "__main__":
			UpperCAmelCase__	:  Optional[int]      =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_url',
			    default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
			    type=str,
			    help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the output PyTorch model directory.',
			)

			UpperCAmelCase__	:  List[str]      =   parser.parse_args()
			convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



