




'''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 						snake_case_							(_a     :							Any					):
    UpperCAmelCase      	=		ConvNextVaConfig()

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

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

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

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

    return config, expected_shape





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

    return name





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





def 						snake_case_							(_a     :							Tuple					):
    if "224" in checkpoint_url:
        UpperCAmelCase      	=		2_2_4
        UpperCAmelCase      	=		2_2_4 / 2_5_6
    elif "384" in checkpoint_url:
        UpperCAmelCase      	=		3_8_4
        UpperCAmelCase      	=		None
    else:
        UpperCAmelCase      	=		5_1_2
        UpperCAmelCase      	=		None

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





@torch.no_grad()
def 						snake_case_							(_a     :							List[Any]   ,       _a     :							int   ,       _a     :							int   ,       _a     :							Any					):
    print('''Downloading original model from checkpoint...'''					)
    # define ConvNeXTV2 configuration based on URL
    UpperCAmelCase	,					UpperCAmelCase      	=		get_convnextva_config(_a					)
    # load original state_dict from URL
    UpperCAmelCase      	=		torch.hub.load_state_dict_from_url(_a					)['''model''']

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

    # load HuggingFace model
    UpperCAmelCase      	=		ConvNextVaForImageClassification(_a					)
    model.load_state_dict(_a					)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    UpperCAmelCase      	=		convert_preprocessor(_a					)
    UpperCAmelCase      	=		preprocessor(images=prepare_img()   ,       return_tensors='''pt'''					)
    UpperCAmelCase      	=		model(**_a					).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":
        UpperCAmelCase      	=		torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
        UpperCAmelCase      	=		torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]					)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
        UpperCAmelCase      	=		torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]					)
    else:
        raise ValueError(F"Unknown URL: {checkpoint_url}"					)

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

        model.save_pretrained(_a					)
        preprocessor.save_pretrained(_a					)

    UpperCAmelCase      	=		'''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(_a					)
        preprocessor.push_to_hub(_a					)


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
   )










'''simple docstring'''







import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


@require_tokenizers
class       _a  (     unittest.TestCase			):
       __a    :					Optional[int]           = EsmTokenizer
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase      	=		tempfile.mkdtemp()
           # fmt: off
           UpperCAmelCase      	=		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
           # fmt: on
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           with open(self.vocab_file       ,    '''w'''       ,    encoding='''utf-8'''    ) as vocab_writer:
               vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )
       def    A				(     self						:     Union[str, Any]       ,    **lowercase						:     Tuple    ):



           '''simple docstring'''

           return [self.get_tokenizer(**lowercase    )]
       def    A				(     self						:     Optional[Any]       ,    **lowercase						:     str    ):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **lowercase    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		tokenizer.tokenize('''LAGVS'''    )
           self.assertListEqual(lowercase       ,    ['''L''', '''A''', '''G''', '''V''', '''S''']    )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    [4, 5, 6, 7, 8]    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		'''LAGVS'''
           self.assertListEqual(tokenizer.encode(lowercase    )       ,    [0, 4, 5, 6, 7, 8, 2]    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(self.vocab_file    )

           UpperCAmelCase      	=		['''LAGVS''', '''WCB''']
           UpperCAmelCase      	=		tokenizer(lowercase       ,    padding=lowercase    )['''input_ids''']

           self.assertListEqual(lowercase       ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]    )







       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_tokenizers(fast=lowercase    )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"    ):
                   UpperCAmelCase      	=		'''<unk>'''
                   UpperCAmelCase      	=		'''<mask>'''

                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
                   UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )

                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(len(lowercase    )       ,    1    )
                   self.assertEqual(token_a[0]       ,    lowercase    )
                   self.assertEqual(token_a[0]       ,    lowercase    )




