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       a__      (					a							)		->	List[Any]:
       A_							:       int	      =				ConvNextConfig()

       if "tiny" in checkpoint_url:
              A_							:       List[Any]	      =				[3, 3, 9, 3]
              A_							:       Union[str, Any]	      =				[9_6, 1_9_2, 3_8_4, 7_6_8]
       if "small" in checkpoint_url:
              A_							:       List[Any]	      =				[3, 3, 2_7, 3]
              A_							:       str	      =				[9_6, 1_9_2, 3_8_4, 7_6_8]
       if "base" in checkpoint_url:
              A_							:       Optional[int]	      =				[3, 3, 2_7, 3]
              A_							:       Union[str, Any]	      =				[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
       if "large" in checkpoint_url:
              A_							:       Dict	      =				[3, 3, 2_7, 3]
              A_							:       Any	      =				[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
       if "xlarge" in checkpoint_url:
              A_							:       Optional[Any]	      =				[3, 3, 2_7, 3]
              A_							:       Optional[int]	      =				[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

       if "1k" in checkpoint_url:
              A_							:       Any	      =				1_0_0_0
              A_							:       Any	      =				'''imagenet-1k-id2label.json'''
              A_							:       Any	      =				(1, 1_0_0_0)
       else:
              A_							:       Dict	      =				2_1_8_4_1
              A_							:       Dict	      =				'''imagenet-22k-id2label.json'''
              A_							:       Optional[Any]	      =				(1, 2_1_8_4_1)

       A_							:       int	      =				'''huggingface/label-files'''
       A_							:       List[Any]	      =				num_labels
       A_							:       List[Any]	      =				json.load(open(hf_hub_download(a  ,   a  ,   repo_type='''dataset'''							)  ,   '''r'''							)							)
       A_							:       List[Any]	      =				{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]
       A_							:       Dict	      =				idalabel
       A_							:       int	      =				{v: k for k, v in idalabel.items()}
       A_							:       int	      =				hidden_sizes
       A_							:       Optional[Any]	      =				depths

       return config, expected_shape


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

       return name


def       a__      (					)		->	Optional[Any]:
       A_							:       Any	      =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A_							:       List[Any]	      =				Image.open(requests.get(a  ,   stream=a							).raw							)
       return im


@torch.no_grad()
def       a__      (					a  ,   a							)		->	Dict:
       A_      ,							A_							:       Dict	      =				get_convnext_config(a							)
       # load original state_dict from URL
       A_							:       List[str]	      =				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_							:       int	      =				val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              A_							:       Any	      =				state_dict.pop(a							)
              if not key.startswith('''classifier'''							):
                     A_							:       str	      =				'''convnext.''' + key
              A_							:       Union[str, Any]	      =				val

       # load HuggingFace model
       A_							:       List[str]	      =				ConvNextForImageClassification(a							)
       model.load_state_dict(a							)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       A_							:       Dict	      =				2_2_4 if '''224''' in checkpoint_url else 3_8_4
       A_							:       List[str]	      =				ConvNextImageProcessor(size=a							)
       A_							:       str	      =				image_processor(images=prepare_img()  ,   return_tensors='''pt'''							).pixel_values

       A_							:       int	      =				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_							:       Optional[int]	      =				torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
              A_							:       Tuple	      =				torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
              A_							:       Any	      =				torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
              A_							:       str	      =				torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
              A_							:       Dict	      =				torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
              A_							:       int	      =				torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
              A_							:       List[Any]	      =				torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
              A_							:       Optional[Any]	      =				torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
              A_							:       List[str]	      =				torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
              A_							:       str	      =				torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
              A_							:       Any	      =				torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
              A_							:       str	      =				torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
              A_							:       int	      =				torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
              A_							:       Dict	      =				torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]							)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
              A_							:       Any	      =				torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]							)
       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_							:       str	      =				'''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)



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				tempfile.mkdtemp()

        A_							:       str	      =				CLIPImageProcessor()
        A_							:       List[Any]	      =				BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']      )

        A_							:       Optional[int]	      =				GitProcessor(__magic_name__ ,       __magic_name__      )

        processor.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).tokenizer
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta      )]

        A_							:       Union[str, Any]	      =				[Image.fromarray(np.moveaxis(__magic_name__ ,       0 ,       -1      )      ) for x in image_inputs]

        return image_inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        A_							:       int	      =				self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''      )
        A_							:       str	      =				self.get_image_processor(do_normalize=__magic_name__ ,       padding_value=1.0      )

        A_							:       int	      =				GitProcessor.from_pretrained(
            self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__magic_name__ ,       padding_value=1.0      )

        self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()      )
        self.assertIsInstance(processor.tokenizer ,       __magic_name__      )

        self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       str	      =				self.prepare_image_inputs()

        A_							:       List[str]	      =				image_processor(__magic_name__ ,       return_tensors='''np'''      )
        A_							:       int	      =				processor(images=__magic_name__ ,       return_tensors='''np'''      )

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1e-2      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       str	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''

        A_							:       Tuple	      =				processor(text=__magic_name__      )

        A_							:       List[Any]	      =				tokenizer(__magic_name__ ,       return_token_type_ids=__magic_name__      )

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				'''lower newer'''
        A_							:       Dict	      =				self.prepare_image_inputs()

        A_							:       Union[str, Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )

        # test if it raises when no input is passed
        with pytest.raises(__magic_name__      ):
               processor()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A_							:       List[Any]	      =				processor.batch_decode(__magic_name__      )
        A_							:       Dict	      =				tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       str	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''
        A_							:       List[str]	      =				self.prepare_image_inputs()

        A_							:       Optional[Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )


