




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()
lowercase__      :			List[str]           =					logging.get_logger(__name__)

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     Optional[Any]:
     a      =      ConvNextVaConfig()

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

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

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

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

     return config, expected_shape

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

     return name

def 					SCREAMING_SNAKE_CASE							(   )			->     Any:
     a      =      "http://images.cocodataset.org/val2017/000000039769.jpg"
     a      =      Image.open(requests.get(lowerCamelCase_		,					stream=lowerCamelCase_).raw)
     return im

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     Any:
     if "224" in checkpoint_url:
          a      =      2_24
          a      =      2_24 / 2_56
     elif "384" in checkpoint_url:
          a      =      3_84
          a      =      None
     else:
          a      =      5_12
          a      =      None

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

@torch.no_grad()
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)			->     str:
     print("Downloading original model from checkpoint...")
     # define ConvNeXTV2 configuration based on URL
     a  ,      a      =      get_convnextva_config(lowerCamelCase_)
     # load original state_dict from URL
     a      =      torch.hub.load_state_dict_from_url(lowerCamelCase_)["model"]

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

     # load HuggingFace model
     a      =      ConvNextVaForImageClassification(lowerCamelCase_)
     model.load_state_dict(lowerCamelCase_)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     a      =      convert_preprocessor(lowerCamelCase_)
     a      =      preprocessor(images=prepare_img()		,					return_tensors="pt")
     a      =      model(**lowerCamelCase_).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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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":
          a      =      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]		,					lowerCamelCase_		,					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(lowerCamelCase_):
               os.mkdir(lowerCamelCase_)

          model.save_pretrained(lowerCamelCase_)
          preprocessor.save_pretrained(lowerCamelCase_)

     a      =      "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(lowerCamelCase_)
          preprocessor.push_to_hub(lowerCamelCase_)


if __name__ == "__main__":
   lowercase__      :			List[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")

   lowercase__      :			int           =					parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )








'''simple docstring'''



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    =			OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

_SCREAMING_SNAKE_CASE    =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def       _lowerCAmelCase  ( lowerCamelCase_	:     str ):
			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									__lowercase   =							model_type_to_module_name(lowerCamelCase_ )

									__lowercase   =							importlib.import_module(f".{module_name}"						,				'''transformers.models''' )
									try:
												return getattr(lowerCamelCase_						,				lowerCamelCase_ )
									except AttributeError:
												continue

			for processor in PROCESSOR_MAPPING._extra_content.values():
						if getattr(lowerCamelCase_						,				'''__name__'''						,				lowerCamelCase_ ) == class_name:
									return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			__lowercase   =							importlib.import_module('''transformers''' )
			if hasattr(lowerCamelCase_						,				lowerCamelCase_ ):
						return getattr(lowerCamelCase_						,				lowerCamelCase_ )

			return None






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self       )							->							List[Any]:







				'''simple docstring'''





				raise EnvironmentError(
				    '''AutoProcessor is designed to be instantiated '''
				    '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''       )


	@classmethod
	@replace_list_option_in_docstrings(_lowerCamelCase       )
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase   ,**_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							kwargs.pop('''config'''   ,_lowerCamelCase       )
				__lowercase   =							kwargs.pop('''trust_remote_code'''   ,_lowerCamelCase       )
				__lowercase   =							True

				__lowercase   =							None
				__lowercase   =							None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				__lowercase   =							{
				    key: kwargs[key] for key in inspect.signature(_lowerCamelCase       ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				__lowercase   =							get_file_from_repo(
				    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
				if preprocessor_config_file is not None:
							__lowercase  ,						__lowercase   =							ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
				if preprocessor_config_file is not None and processor_class is None:
							__lowercase  ,						__lowercase   =							FeatureExtractionMixin.get_feature_extractor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Next, let's check whether the processor class is saved in a tokenizer
							__lowercase   =							get_file_from_repo(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							if tokenizer_config_file is not None:
										with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as reader:
													__lowercase   =							json.load(_lowerCamelCase       )

										__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
										if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
													__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Otherwise, load config, if it can be loaded.
							if not isinstance(_lowerCamelCase   ,_lowerCamelCase       ):
										__lowercase   =							AutoConfig.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )

							# And check if the config contains the processor class.
							__lowercase   =							getattr(_lowerCamelCase   ,'''processor_class'''   ,_lowerCamelCase       )
							if hasattr(_lowerCamelCase   ,'''auto_map'''       ) and "AutoProcessor" in config.auto_map:
										__lowercase   =							config.auto_map['''AutoProcessor''']

				if processor_class is not None:
							__lowercase   =							processor_class_from_name(_lowerCamelCase       )

				__lowercase   =							processor_auto_map is not None
				__lowercase   =							processor_class is not None or type(_lowerCamelCase       ) in PROCESSOR_MAPPING
				__lowercase   =							resolve_trust_remote_code(
				    _lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )

				if has_remote_code and trust_remote_code:
							__lowercase   =							get_class_from_dynamic_module(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							kwargs.pop('''code_revision'''   ,_lowerCamelCase       )
							if os.path.isdir(_lowerCamelCase       ):
										processor_class.register_for_auto_class()
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				elif processor_class is not None:
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(_lowerCamelCase       ) in PROCESSOR_MAPPING:
							return PROCESSOR_MAPPING[type(_lowerCamelCase       )].from_pretrained(_lowerCamelCase   ,**_lowerCamelCase       )

				# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
				# tokenizer.
				try:
							return AutoTokenizer.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				except Exception:
							try:
										return AutoImageProcessor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

							try:
										return AutoFeatureExtractor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

				raise ValueError(
				    f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
				    '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
				    '''the files of at least one of those processing classes.'''       )


	@staticmethod
	def   _UpperCAmelCase   (_lowerCamelCase   ,_lowerCamelCase       )							->							int:







				'''simple docstring'''





				PROCESSOR_MAPPING.register(_lowerCamelCase   ,_lowerCamelCase       )





