





'''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			:	Optional[int]								=  logging.get_logger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		ConvNextVaConfig()

  if "atto" in checkpoint_url:
    UpperCAmelCase  							=		[2, 2, 6, 2]
    UpperCAmelCase  							=		[40, 80, 160, 320]
  if "femto" in checkpoint_url:
    UpperCAmelCase  							=		[2, 2, 6, 2]
    UpperCAmelCase  							=		[48, 96, 192, 384]
  if "pico" in checkpoint_url:
    UpperCAmelCase  							=		[2, 2, 6, 2]
    UpperCAmelCase  							=		[64, 128, 256, 512]
  if "nano" in checkpoint_url:
    UpperCAmelCase  							=		[2, 2, 8, 2]
    UpperCAmelCase  							=		[80, 160, 320, 640]
  if "tiny" in checkpoint_url:
    UpperCAmelCase  							=		[3, 3, 9, 3]
    UpperCAmelCase  							=		[96, 192, 384, 768]
  if "base" in checkpoint_url:
    UpperCAmelCase  							=		[3, 3, 27, 3]
    UpperCAmelCase  							=		[128, 256, 512, 1024]
  if "large" in checkpoint_url:
    UpperCAmelCase  							=		[3, 3, 27, 3]
    UpperCAmelCase  							=		[192, 384, 768, 1536]
  if "huge" in checkpoint_url:
    UpperCAmelCase  							=		[3, 3, 27, 3]
    UpperCAmelCase  							=		[352, 704, 1408, 2816]

  UpperCAmelCase  							=		1000
  UpperCAmelCase  							=		'imagenet-1k-id2label.json'
  UpperCAmelCase  							=		(1, 1000)

  UpperCAmelCase  							=		'huggingface/label-files'
  UpperCAmelCase  							=		num_labels
  UpperCAmelCase  							=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				repo_type='dataset'  )    ,				'r'  )  )
  UpperCAmelCase  							=		{int(SCREAMING_SNAKE_CASE  ): 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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]  ):
  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  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCAmelCase  							=		Image.open(requests.get(SCREAMING_SNAKE_CASE    ,				stream=SCREAMING_SNAKE_CASE  ).raw  )
  return im


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str  ):
  if "224" in checkpoint_url:
    UpperCAmelCase  							=		224
    UpperCAmelCase  							=		224 / 256
  elif "384" in checkpoint_url:
    UpperCAmelCase  							=		384
    UpperCAmelCase  							=		None
  else:
    UpperCAmelCase  							=		512
    UpperCAmelCase  							=		None

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


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

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

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

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

    model.save_pretrained(SCREAMING_SNAKE_CASE  )
    preprocessor.save_pretrained(SCREAMING_SNAKE_CASE  )

  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(SCREAMING_SNAKE_CASE  )
    preprocessor.push_to_hub(SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
  _a			:	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')

  _a			:	str								=  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 pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_a			:	int								=  logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  _a			:	List[str]								=  object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		value
  def __str__(       self       )							->			Any:


    """simple docstring"""
    return self.to_string()
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    logger.error(
        'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'       )
    return self._value





  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    logger.error(
        'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'       )
    return str(self._value       )




class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    return self._value





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


    """simple docstring"""
    return self._value




class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  def __init__(       self				,	a_       )							->			int:


    """simple docstring"""
    super().__init__(a_       )

    if not is_vision_available():
      raise ImportError('PIL must be installed in order to handle images.'       )

    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None

    if isinstance(a_				,	a_       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	(str, pathlib.Path)       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	torch.Tensor       ):
      UpperCAmelCase  							=		value
    else:
      raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(a_       )}'''       )
  def   snake_case_     (       self				,	a_=None				,	a_=None       )							->			Dict:


    """simple docstring"""
    from IPython.display import Image, display

    display(Image(self.to_string()       )       )
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    if self._raw is not None:
      return self._raw

    if self._path is not None:
      UpperCAmelCase  							=		Image.open(self._path       )
      return self._raw





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


    """simple docstring"""
    if self._path is not None:
      return self._path

    if self._raw is not None:
      UpperCAmelCase  							=		tempfile.mkdtemp()
      UpperCAmelCase  							=		os.path.join(a_				,	str(uuid.uuida()       ) + '.png'       )
      self._raw.save(self._path       )

      return self._path

    if self._tensor is not None:
      UpperCAmelCase  							=		self._tensor.cpu().detach().numpy()

      # There is likely simpler than load into image into save
      UpperCAmelCase  							=		Image.fromarray((array * 2_5_5).astype(np.uinta       )       )

      UpperCAmelCase  							=		tempfile.mkdtemp()
      UpperCAmelCase  							=		os.path.join(a_				,	str(uuid.uuida()       ) + '.png'       )

      img.save(self._path       )

      return self._path




class     lowercase_		(						a	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=1_6_0_0_0       )							->			str:


    """simple docstring"""
    super().__init__(a_       )

    if not is_soundfile_availble():
      raise ImportError('soundfile must be installed in order to handle audio.'       )

    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None

    UpperCAmelCase  							=		samplerate

    if isinstance(a_				,	(str, pathlib.Path)       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	torch.Tensor       ):
      UpperCAmelCase  							=		value
    else:
      raise ValueError(F'''Unsupported audio type: {type(a_       )}'''       )
  def   snake_case_     (       self				,	a_=None				,	a_=None       )							->			Dict:


    """simple docstring"""
    from IPython.display import Audio, display

    display(Audio(self.to_string()				,	rate=self.samplerate       )       )
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    if self._tensor is not None:
      return self._tensor

    if self._path is not None:
      UpperCAmelCase							, UpperCAmelCase  							=		sf.read(self._path       )
      UpperCAmelCase  							=		torch.tensor(a_       )
      return self._tensor





  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    if self._path is not None:
      return self._path

    if self._tensor is not None:
      UpperCAmelCase  							=		tempfile.mkdtemp()
      UpperCAmelCase  							=		os.path.join(a_				,	str(uuid.uuida()       ) + '.wav'       )
      sf.write(self._path				,	self._tensor				,	samplerate=self.samplerate       )
      return self._path




_a			:	Optional[int]								=  {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_a			:	Tuple								=  {str: AgentText}

if is_vision_available():
  _a			:	List[str]								=  AgentImage


def  lowerCamelCase__						(  *SCREAMING_SNAKE_CASE :				List[str]    ,				**SCREAMING_SNAKE_CASE :				Tuple  ):
  UpperCAmelCase  							=		[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ) else arg) for arg in args]
  UpperCAmelCase  							=		{k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ) else v) for k, v in kwargs.items()}
  return args, kwargs



def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=None  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    UpperCAmelCase  							=		{}
    for i, (k, v) in enumerate(outputs.items()  ):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          UpperCAmelCase  							=		AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE  )
        else:
          UpperCAmelCase  							=		AgentType(SCREAMING_SNAKE_CASE  )

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(SCREAMING_SNAKE_CASE    ,				_k  ):
            UpperCAmelCase  							=		_v(SCREAMING_SNAKE_CASE  )
        if k not in decoded_outputs:
          UpperCAmelCase  							=		AgentType[v]

  elif isinstance(SCREAMING_SNAKE_CASE    ,				(list, tuple)  ):
    UpperCAmelCase  							=		type(SCREAMING_SNAKE_CASE  )()
    for i, v in enumerate(SCREAMING_SNAKE_CASE  ):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE  )  )
        else:
          decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE  )  )
      else:
        # If the class does not have defined output, then we map according to the type
        UpperCAmelCase  							=		False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(SCREAMING_SNAKE_CASE    ,				_k  ):
            decoded_outputs.append(_v(SCREAMING_SNAKE_CASE  )  )
            UpperCAmelCase  							=		True

        if not found:
          decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE  )  )

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      UpperCAmelCase  							=		AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE  )

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(SCREAMING_SNAKE_CASE    ,				_k  ):
          return _v(SCREAMING_SNAKE_CASE  )
      return AgentType(SCREAMING_SNAKE_CASE  )

  return decoded_outputs


