
'''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__				:	Dict					=						logging.get_logger(__name__)







def 					A       ( UpperCamelCase_   :       Dict )   ->    List[str]:







			'''simple docstring'''



			lowerCAmelCase__									=							ConvNextVaConfig()

			if "atto" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[40, 80, 1_60, 3_20]
			if "femto" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[48, 96, 1_92, 3_84]
			if "pico" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[64, 1_28, 2_56, 5_12]
			if "nano" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 8, 2]
						lowerCAmelCase__									=							[80, 1_60, 3_20, 6_40]
			if "tiny" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 9, 3]
						lowerCAmelCase__									=							[96, 1_92, 3_84, 7_68]
			if "base" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[1_28, 2_56, 5_12, 10_24]
			if "large" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[1_92, 3_84, 7_68, 15_36]
			if "huge" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[3_52, 7_04, 14_08, 28_16]

			lowerCAmelCase__									=							10_00
			lowerCAmelCase__									=							"imagenet-1k-id2label.json"
			lowerCAmelCase__									=							(1, 10_00)

			lowerCAmelCase__									=							"huggingface/label-files"
			lowerCAmelCase__									=							num_labels
			lowerCAmelCase__									=							json.load(open(hf_hub_download(UpperCamelCase_  ,		UpperCamelCase_  ,		repo_type="dataset" )  ,		"r" ) )
			lowerCAmelCase__									=							{int(UpperCamelCase_ ): 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 					A       ( UpperCamelCase_   :       Tuple )   ->    int:







			'''simple docstring'''



			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 					A       ( )   ->    Any:







			'''simple docstring'''



			lowerCAmelCase__									=							"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase__									=							Image.open(requests.get(UpperCamelCase_  ,		stream=UpperCamelCase_ ).raw )
			return im







def 					A       ( UpperCamelCase_   :       Any )   ->    Tuple:







			'''simple docstring'''



			if "224" in checkpoint_url:
						lowerCAmelCase__									=							2_24
						lowerCAmelCase__									=							2_24 / 2_56
			elif "384" in checkpoint_url:
						lowerCAmelCase__									=							3_84
						lowerCAmelCase__									=							None
			else:
						lowerCAmelCase__									=							5_12
						lowerCAmelCase__									=							None

			return ConvNextImageProcessor(
			    size=UpperCamelCase_  ,		crop_pct=UpperCamelCase_  ,		image_mean=[0.485, 0.456, 0.406]  ,		image_std=[0.229, 0.224, 0.225]  ,		resample=PILImageResampling.BICUBIC  ,		)







@torch.no_grad()
def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       Optional[Any] )   ->    str:







			'''simple docstring'''



			print("Downloading original model from checkpoint..." )
			# define ConvNeXTV2 configuration based on URL
			lowerCAmelCase__		,lowerCAmelCase__									=							get_convnextva_config(UpperCamelCase_ )
			# load original state_dict from URL
			lowerCAmelCase__									=							torch.hub.load_state_dict_from_url(UpperCamelCase_ )["model"]

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

			# load HuggingFace model
			lowerCAmelCase__									=							ConvNextVaForImageClassification(UpperCamelCase_ )
			model.load_state_dict(UpperCamelCase_ )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			lowerCAmelCase__									=							convert_preprocessor(UpperCamelCase_ )
			lowerCAmelCase__									=							preprocessor(images=prepare_img()  ,		return_tensors="pt" )
			lowerCAmelCase__									=							model(**UpperCamelCase_ ).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([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826] )
			else:
						raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

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

						model.save_pretrained(UpperCamelCase_ )
						preprocessor.save_pretrained(UpperCamelCase_ )

			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(UpperCamelCase_ )
						preprocessor.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
		UpperCAmelCase__				:	Optional[Any]					=						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__				:	List[str]					=						parser.parse_args()
		convert_convnextva_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
		)







'''simple docstring'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





