

'''simple docstring'''

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()
lowerCAmelCase_     =      logging.get_logger(__name__)

def      __magic_name__							(			A				)      ->		List[str]:
	snake_case							=					ConvNextConfig()

	if "tiny" in checkpoint_url:
		snake_case							=					[3, 3, 9, 3]
		snake_case							=					[9_6, 1_9_2, 3_8_4, 7_6_8]
	if "small" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[9_6, 1_9_2, 3_8_4, 7_6_8]
	if "base" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
	if "large" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
	if "xlarge" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

	if "1k" in checkpoint_url:
		snake_case							=					1_0_0_0
		snake_case							=					'imagenet-1k-id2label.json'
		snake_case							=					(1, 1_0_0_0)
	else:
		snake_case							=					2_1_8_4_1
		snake_case							=					'imagenet-22k-id2label.json'
		snake_case							=					(1, 2_1_8_4_1)

	snake_case							=					'huggingface/label-files'
	snake_case							=					num_labels
	snake_case							=					json.load(open(hf_hub_download(A       , A       , repo_type='dataset'				)       , 'r'				)				)
	snake_case							=					{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[9_2_0_5]
		del idalabel[1_5_0_2_7]
	snake_case							=					idalabel
	snake_case							=					{v: k for k, v in idalabel.items()}
	snake_case							=					hidden_sizes
	snake_case							=					depths

	return config, expected_shape

def      __magic_name__							(			A				)      ->		str:
	if "downsample_layers.0.0" in name:
		snake_case							=					name.replace('downsample_layers.0.0'       , 'embeddings.patch_embeddings'				)
	if "downsample_layers.0.1" in name:
		snake_case							=					name.replace('downsample_layers.0.1'       , 'embeddings.norm'				)  # we rename to layernorm later on
	if "downsample_layers.1.0" in name:
		snake_case							=					name.replace('downsample_layers.1.0'       , 'stages.1.downsampling_layer.0'				)
	if "downsample_layers.1.1" in name:
		snake_case							=					name.replace('downsample_layers.1.1'       , 'stages.1.downsampling_layer.1'				)
	if "downsample_layers.2.0" in name:
		snake_case							=					name.replace('downsample_layers.2.0'       , 'stages.2.downsampling_layer.0'				)
	if "downsample_layers.2.1" in name:
		snake_case							=					name.replace('downsample_layers.2.1'       , 'stages.2.downsampling_layer.1'				)
	if "downsample_layers.3.0" in name:
		snake_case							=					name.replace('downsample_layers.3.0'       , 'stages.3.downsampling_layer.0'				)
	if "downsample_layers.3.1" in name:
		snake_case							=					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.
		snake_case							=					name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
	if "stages" in name:
		snake_case							=					name.replace('stages'       , 'encoder.stages'				)
	if "norm" in name:
		snake_case							=					name.replace('norm'       , 'layernorm'				)
	if "gamma" in name:
		snake_case							=					name.replace('gamma'       , 'layer_scale_parameter'				)
	if "head" in name:
		snake_case							=					name.replace('head'       , 'classifier'				)

	return name

def      __magic_name__							(			)      ->		int:
	snake_case							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case							=					Image.open(requests.get(A       , stream=A				).raw				)
	return im

@torch.no_grad()
def      __magic_name__							(			A       , A				)      ->		Dict:
	snake_case			,			snake_case							=					get_convnext_config(A				)
	# load original state_dict from URL
	snake_case							=					torch.hub.load_state_dict_from_url(A				)['model']
	# rename keys
	for key in state_dict.copy().keys():
		snake_case							=					state_dict.pop(A				)
		snake_case							=					val
	# add prefix to all keys expect classifier head
	for key in state_dict.copy().keys():
		snake_case							=					state_dict.pop(A				)
		if not key.startswith('classifier'				):
			snake_case							=					'convnext.' + key
		snake_case							=					val

	# load HuggingFace model
	snake_case							=					ConvNextForImageClassification(A				)
	model.load_state_dict(A				)
	model.eval()

	# Check outputs on an image, prepared by ConvNextImageProcessor
	snake_case							=					2_2_4 if '224' in checkpoint_url else 3_8_4
	snake_case							=					ConvNextImageProcessor(size=A				)
	snake_case							=					image_processor(images=prepare_img()       , return_tensors='pt'				).pixel_values

	snake_case							=					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":
		snake_case							=					torch.tensor([-0.1_210, -0.6_605, 0.1_918]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
		snake_case							=					torch.tensor([-0.4_473, -0.1_847, -0.6_365]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
		snake_case							=					torch.tensor([0.4_525, 0.7_539, 0.0_308]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
		snake_case							=					torch.tensor([0.3_561, 0.6_350, -0.0_384]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
		snake_case							=					torch.tensor([0.4_174, -0.0_989, 0.1_489]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
		snake_case							=					torch.tensor([0.2_513, -0.1_349, -0.1_613]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
		snake_case							=					torch.tensor([1.2_980, 0.3_631, -0.1_198]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
		snake_case							=					torch.tensor([1.2_963, 0.1_227, 0.1_723]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
		snake_case							=					torch.tensor([1.7_956, 0.8_390, 0.2_820]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
		snake_case							=					torch.tensor([-0.2_822, -0.0_502, -0.0_878]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
		snake_case							=					torch.tensor([-0.5_672, -0.0_730, -0.4_348]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
		snake_case							=					torch.tensor([0.2_681, 0.2_365, 0.6_246]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
		snake_case							=					torch.tensor([-0.2_642, 0.3_931, 0.5_116]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
		snake_case							=					torch.tensor([-0.6_677, -0.1_873, -0.8_379]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
		snake_case							=					torch.tensor([-0.7_749, -0.2_967, -0.6_444]				)
	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...'				)
	snake_case							=					'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__":
						lowerCAmelCase_     =      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.",
						)

						lowerCAmelCase_     =      parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""






import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


UpperCamelCase_						   =   logging.get_logger(__name__)





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Tuple     					=     """hybrid-clip"""
   a_					:		Optional[Any]     					=     True







   def __init__( self       ,							__UpperCAmelCase=5_12       ,							**__UpperCAmelCase)   ->Dict:
      super().__init__(**__UpperCAmelCase)

      if "text_config" not in kwargs:
         raise ValueError("`text_config` can not be `None`.")

      if "vision_config" not in kwargs:
         raise ValueError("`vision_config` can not be `None`.")

      a_						   =     kwargs.pop("text_config")
      a_						   =     kwargs.pop("vision_config")

      a_						   =     text_config.pop("model_type")
      a_						   =     vision_config.pop("model_type")

      from transformers import AutoConfig

      a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      if vision_model_type == "clip":
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase).vision_config
      elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a_						   =     CLIPVisionConfig(**__UpperCAmelCase)
      else:
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      a_						   =     projection_dim
      a_						   =     1.0







   @classmethod
   def    UpperCAmelCase__	( cls       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Optional[int]:
      return cls(text_config=text_config.to_dict()       ,							vision_config=vision_config.to_dict()       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     copy.deepcopy(self.__dict__)
      a_						   =     self.text_config.to_dict()
      a_						   =     self.vision_config.to_dict()
      a_						   =     self.__class__.model_type
      return output