




'''simple docstring'''
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()
A							:      Optional[Any] 	=       logging.get_logger(__name__)
def 				snake_case_  (   a__		:	List[Any]			):



    """simple docstring"""




    __lowercase							=						ConvNextConfig()

    if "tiny" in checkpoint_url:
        __lowercase							=						[3, 3, 9, 3]
        __lowercase							=						[96, 1_92, 3_84, 7_68]
    if "small" in checkpoint_url:
        __lowercase							=						[3, 3, 27, 3]
        __lowercase							=						[96, 1_92, 3_84, 7_68]
    if "base" in checkpoint_url:
        __lowercase							=						[3, 3, 27, 3]
        __lowercase							=						[1_28, 2_56, 5_12, 10_24]
    if "large" in checkpoint_url:
        __lowercase							=						[3, 3, 27, 3]
        __lowercase							=						[1_92, 3_84, 7_68, 15_36]
    if "xlarge" in checkpoint_url:
        __lowercase							=						[3, 3, 27, 3]
        __lowercase							=						[2_56, 5_12, 10_24, 20_48]

    if "1k" in checkpoint_url:
        __lowercase							=						10_00
        __lowercase							=						"""imagenet-1k-id2label.json"""
        __lowercase							=						(1, 10_00)
    else:
        __lowercase							=						2_18_41
        __lowercase							=						"""imagenet-22k-id2label.json"""
        __lowercase							=						(1, 2_18_41)

    __lowercase							=						"""huggingface/label-files"""
    __lowercase							=						num_labels
    __lowercase							=						json.load(open(hf_hub_download(__UpperCAmelCase    ,__UpperCAmelCase    ,repo_type="""dataset"""			)    ,"""r"""			)			)
    __lowercase							=						{int(__UpperCAmelCase			): 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[92_05]
        del idalabel[1_50_27]
    __lowercase							=						idalabel
    __lowercase							=						{v: k for k, v in idalabel.items()}
    __lowercase							=						hidden_sizes
    __lowercase							=						depths

    return config, expected_shape
def 				snake_case_  (   a__		:	int			):



    """simple docstring"""




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

    return name
def 				snake_case_  (   ):



    """simple docstring"""




    __lowercase							=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __lowercase							=						Image.open(requests.get(__UpperCAmelCase    ,stream=__UpperCAmelCase			).raw			)
    return im
@torch.no_grad()
def 				snake_case_  (   a__		:	List[Any]    ,a__		:	List[Any]			):



    """simple docstring"""




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

    # load HuggingFace model
    __lowercase							=						ConvNextForImageClassification(__UpperCAmelCase			)
    model.load_state_dict(__UpperCAmelCase			)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    __lowercase							=						2_24 if """224""" in checkpoint_url else 3_84
    __lowercase							=						ConvNextImageProcessor(size=__UpperCAmelCase			)
    __lowercase							=						image_processor(images=prepare_img()    ,return_tensors="""pt"""			).pixel_values

    __lowercase							=						model(__UpperCAmelCase			).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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":
        __lowercase							=						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]    ,__UpperCAmelCase    ,atol=1e-3			)
    assert logits.shape == expected_shape

    Path(__UpperCAmelCase			).mkdir(exist_ok=__UpperCAmelCase			)
    print(f'Saving model to {pytorch_dump_folder_path}'			)
    model.save_pretrained(__UpperCAmelCase			)
    print(f'Saving image processor to {pytorch_dump_folder_path}'			)
    image_processor.save_pretrained(__UpperCAmelCase			)

    print("""Pushing model to the hub..."""			)
    __lowercase							=						"""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(__UpperCAmelCase    ,__UpperCAmelCase			)    ,organization="""nielsr"""    ,commit_message="""Add model"""    ,)


if __name__ == "__main__":
  A							:      Optional[int] 	=       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.""",
  )

  A							:      List[Any] 	=       parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 							a_  (		datasets.BuilderConfig				):
						UpperCamelCase__      :						Optional[Callable]				       =None
						UpperCamelCase__      :						Optional[dict]				       =None
						UpperCamelCase__      :						Optional[datasets.Features]				       =None

						def 	__a       (    self	:List[str])		->					List[str]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase_     							=     {}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						List[str]				       =GeneratorConfig

						def 	__a       (    self	:List[str])		->					List[Any]:
												return datasets.DatasetInfo(features=self.config.features)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:List[str])		->					Optional[Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs=self.config.gen_kwargs)]




						def 	__a       (    self	:Optional[int]   ,						**_lowercase	:str)		->					Union[str, Any]:
												for idx, ex in enumerate(self.config.generator(**_lowercase)):
																		yield idx, ex





