
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()
__magic_name__           =					logging.get_logger(__name__)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			ConvNextConfig()

    if "tiny" in checkpoint_url:
        snake_case__  =			[3, 3, 9, 3]
        snake_case__  =			[96, 192, 384, 768]
    if "small" in checkpoint_url:
        snake_case__  =			[3, 3, 27, 3]
        snake_case__  =			[96, 192, 384, 768]
    if "base" in checkpoint_url:
        snake_case__  =			[3, 3, 27, 3]
        snake_case__  =			[128, 256, 512, 1_024]
    if "large" in checkpoint_url:
        snake_case__  =			[3, 3, 27, 3]
        snake_case__  =			[192, 384, 768, 1_536]
    if "xlarge" in checkpoint_url:
        snake_case__  =			[3, 3, 27, 3]
        snake_case__  =			[256, 512, 1_024, 2_048]

    if "1k" in checkpoint_url:
        snake_case__  =			1_000
        snake_case__  =			"imagenet-1k-id2label.json"
        snake_case__  =			(1, 1_000)
    else:
        snake_case__  =			21_841
        snake_case__  =			"imagenet-22k-id2label.json"
        snake_case__  =			(1, 21_841)

    snake_case__  =			"huggingface/label-files"
    snake_case__  =			num_labels
    snake_case__  =			json.load(open(hf_hub_download(__lowerCAmelCase      ,						__lowerCAmelCase      ,						repo_type="dataset"							)      ,						"r"							)							)
    snake_case__  =			{int(__lowerCAmelCase							): 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_205]
        del idalabel[15_027]
    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 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    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 		SCREAMING_SNAKE_CASE__      ( ):
    snake_case__  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case__  =			Image.open(requests.get(__lowerCAmelCase      ,						stream=__lowerCAmelCase							).raw							)
    return im





@torch.no_grad()
def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  ,   snake_case__  =			get_convnext_config(__lowerCAmelCase							)
    # load original state_dict from URL
    snake_case__  =			torch.hub.load_state_dict_from_url(__lowerCAmelCase							)["model"]
    # rename keys
    for key in state_dict.copy().keys():
        snake_case__  =			state_dict.pop(__lowerCAmelCase							)
        snake_case__  =			val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        snake_case__  =			state_dict.pop(__lowerCAmelCase							)
        if not key.startswith("classifier"							):
            snake_case__  =			"convnext." + key
        snake_case__  =			val

    # load HuggingFace model
    snake_case__  =			ConvNextForImageClassification(__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    snake_case__  =			224 if "224" in checkpoint_url else 384
    snake_case__  =			ConvNextImageProcessor(size=__lowerCAmelCase							)
    snake_case__  =			image_processor(images=prepare_img()      ,						return_tensors="pt"							).pixel_values

    snake_case__  =			model(__lowerCAmelCase							).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]      ,						__lowerCAmelCase      ,						atol=1E-3							)
    assert logits.shape == expected_shape

    Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
    print(F"""Saving model to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)

    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(__lowerCAmelCase      ,						__lowerCAmelCase							)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						)


if __name__ == "__main__":
     __magic_name__           =					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.''',
     )

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





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			load_tool("image-captioning"	)
          self.tool.setup()
          snake_case__  =			load_tool("image-captioning"						,		remote=lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



