


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

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					ConvNextVaConfig()

						if "atto" in checkpoint_url:
												__lowerCAmelCase											=					[2, 2, 6, 2]
												__lowerCAmelCase											=					[4_0, 8_0, 1_6_0, 3_2_0]
						if "femto" in checkpoint_url:
												__lowerCAmelCase											=					[2, 2, 6, 2]
												__lowerCAmelCase											=					[4_8, 9_6, 1_9_2, 3_8_4]
						if "pico" in checkpoint_url:
												__lowerCAmelCase											=					[2, 2, 6, 2]
												__lowerCAmelCase											=					[6_4, 1_2_8, 2_5_6, 5_1_2]
						if "nano" in checkpoint_url:
												__lowerCAmelCase											=					[2, 2, 8, 2]
												__lowerCAmelCase											=					[8_0, 1_6_0, 3_2_0, 6_4_0]
						if "tiny" in checkpoint_url:
												__lowerCAmelCase											=					[3, 3, 9, 3]
												__lowerCAmelCase											=					[9_6, 1_9_2, 3_8_4, 7_6_8]
						if "base" in checkpoint_url:
												__lowerCAmelCase											=					[3, 3, 2_7, 3]
												__lowerCAmelCase											=					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
						if "large" in checkpoint_url:
												__lowerCAmelCase											=					[3, 3, 2_7, 3]
												__lowerCAmelCase											=					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
						if "huge" in checkpoint_url:
												__lowerCAmelCase											=					[3, 3, 2_7, 3]
												__lowerCAmelCase											=					[3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

						__lowerCAmelCase											=					1_0_0_0
						__lowerCAmelCase											=					'imagenet-1k-id2label.json'
						__lowerCAmelCase											=					(1, 1_0_0_0)

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

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

						return config, expected_shape

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

						return name

def 				__magic_name__(    ):
						__lowerCAmelCase											=					'http://images.cocodataset.org/val2017/000000039769.jpg'
						__lowerCAmelCase											=					Image.open(requests.get(SCREAMING_SNAKE_CASE_,					stream=SCREAMING_SNAKE_CASE_).raw)
						return im

def 				__magic_name__(    lowerCamelCase):
						if "224" in checkpoint_url:
												__lowerCAmelCase											=					2_2_4
												__lowerCAmelCase											=					2_2_4 / 2_5_6
						elif "384" in checkpoint_url:
												__lowerCAmelCase											=					3_8_4
												__lowerCAmelCase											=					None
						else:
												__lowerCAmelCase											=					5_1_2
												__lowerCAmelCase											=					None

						return ConvNextImageProcessor(
						    size=SCREAMING_SNAKE_CASE_,					crop_pct=SCREAMING_SNAKE_CASE_,					image_mean=[0.4_85, 0.4_56, 0.4_06],					image_std=[0.2_29, 0.2_24, 0.2_25],					resample=PILImageResampling.BICUBIC,					)

@torch.no_grad()
def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						print('''Downloading original model from checkpoint...''')
						# define ConvNeXTV2 configuration based on URL
						__lowerCAmelCase											=					get_convnextva_config(SCREAMING_SNAKE_CASE_)
						# load original state_dict from URL
						__lowerCAmelCase											=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_)['model']

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

						# load HuggingFace model
						__lowerCAmelCase											=					ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE_)
						model.load_state_dict(SCREAMING_SNAKE_CASE_)
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						__lowerCAmelCase											=					convert_preprocessor(SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase											=					preprocessor(images=prepare_img(),					return_tensors='''pt''')
						__lowerCAmelCase											=					model(**SCREAMING_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":
												__lowerCAmelCase											=					torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												__lowerCAmelCase											=					torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09])
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												__lowerCAmelCase											=					torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26])
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}""")

						assert torch.allclose(logits[0, :5],					SCREAMING_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(SCREAMING_SNAKE_CASE_):
																		os.mkdir(SCREAMING_SNAKE_CASE_)

												model.save_pretrained(SCREAMING_SNAKE_CASE_)
												preprocessor.save_pretrained(SCREAMING_SNAKE_CASE_)

						__lowerCAmelCase											=					'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(SCREAMING_SNAKE_CASE_)
												preprocessor.push_to_hub(SCREAMING_SNAKE_CASE_)


if __name__ == "__main__":
	_UpperCAmelCase					:			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""")

	_UpperCAmelCase					:			Optional[int]				     =					parser.parse_args()
	convert_convnextva_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
	)


'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A										= logging.get_logger(__name__)

_A										= ['UtteranceLevel']

@torch.no_grad()
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location='cpu'				)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''				)

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 _A										= argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 _A										= parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




