


"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

snake_case__						:   List[Any]			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Tuple	):
      if isinstance(_snake_case       ,      (list, tuple)	) and isinstance(videos[0]       ,      (list, tuple)	) and is_valid_image(videos[0][0]	):
            return videos

      elif isinstance(_snake_case       ,      (list, tuple)	) and is_valid_image(videos[0]	):
            return [videos]

      elif is_valid_image(_snake_case	):
            return [[videos]]

      raise ValueError(f'''Could not make batched video from {videos}'''	)






class 						snake_case_(				a__	):
     __UpperCamelCase     =    ['''pixel_values''']






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BILINEAR    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Union[int, float] = 1 / 2_5_5    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , **UpperCamelCase_     :    Tuple    , ):
           super().__init__(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      size if size is not None else {'''shortest_edge''': 2_5_6}
           lowerCAmelCase    :		Optional[Any]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           lowerCAmelCase    :		Dict				=      get_size_dict(UpperCamelCase_    , param_name='''crop_size''' )

           lowerCAmelCase    :		Any				=      do_resize
           lowerCAmelCase    :		Union[str, Any]				=      size
           lowerCAmelCase    :		List[str]				=      do_center_crop
           lowerCAmelCase    :		int				=      crop_size
           lowerCAmelCase    :		Dict				=      resample
           lowerCAmelCase    :		Dict				=      do_rescale
           lowerCAmelCase    :		Any				=      rescale_factor
           lowerCAmelCase    :		List[Any]				=      offset
           lowerCAmelCase    :		Tuple				=      do_normalize
           lowerCAmelCase    :		Optional[Any]				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCAmelCase    :		List[Any]				=      image_std if image_std is not None else IMAGENET_STANDARD_STD






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BILINEAR    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		Optional[int]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           if "shortest_edge" in size:
                 lowerCAmelCase    :		List[str]				=      get_resize_output_image_size(UpperCamelCase_    , size['''shortest_edge''']    , default_to_square=UpperCamelCase_ )
           elif "height" in size and "width" in size:
                 lowerCAmelCase    :		Any				=      (size['''height'''], size['''width'''])
           else:
                 raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
           return resize(UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Union[str, Any]    , ):
           lowerCAmelCase    :		Tuple				=      get_size_dict(UpperCamelCase_ )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
           return center_crop(UpperCamelCase_    , size=(size['''height'''], size['''width'''])    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any]    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Union[int, float]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		List[str]				=      image.astype(np.floataa )
           if offset:
                 lowerCAmelCase    :		Union[str, Any]				=      image - (scale / 2)
           return rescale(UpperCamelCase_    , scale=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Union[float, List[float]]    , UpperCamelCase_     :    Union[float, List[float]]    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Any    , ):
           return normalize(UpperCamelCase_    , mean=UpperCamelCase_    , std=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    ImageInput    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[ChannelDimension] = ChannelDimension.FIRST    , ):
           if do_resize and size is None or resample is None:
                 raise ValueError('''Size and resample must be specified if do_resize is True.''' )

           if do_center_crop and crop_size is None:
                 raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

           if offset and not do_rescale:
                 raise ValueError('''For offset, do_rescale must also be set to True.''' )

           # All transformations expect numpy arrays.
           lowerCAmelCase    :		List[str]				=      to_numpy_array(UpperCamelCase_ )

           if do_resize:
                 lowerCAmelCase    :		Optional[int]				=      self.resize(image=UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_ )

           if do_center_crop:
                 lowerCAmelCase    :		List[str]				=      self.center_crop(UpperCamelCase_    , size=UpperCamelCase_ )

           if do_rescale:
                 lowerCAmelCase    :		str				=      self.rescale(image=UpperCamelCase_    , scale=UpperCamelCase_    , offset=UpperCamelCase_ )

           if do_normalize:
                 lowerCAmelCase    :		Optional[int]				=      self.normalize(image=UpperCamelCase_    , mean=UpperCamelCase_    , std=UpperCamelCase_ )

           lowerCAmelCase    :		str				=      to_channel_dimension_format(UpperCamelCase_    , UpperCamelCase_ )
           return image






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    ImageInput    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , UpperCamelCase_     :    ChannelDimension = ChannelDimension.FIRST    , **UpperCamelCase_     :    List[str]    , ):
           lowerCAmelCase    :		str				=      do_resize if do_resize is not None else self.do_resize
           lowerCAmelCase    :		Any				=      resample if resample is not None else self.resample
           lowerCAmelCase    :		int				=      do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCAmelCase    :		int				=      do_rescale if do_rescale is not None else self.do_rescale
           lowerCAmelCase    :		int				=      rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCAmelCase    :		str				=      offset if offset is not None else self.offset
           lowerCAmelCase    :		Optional[int]				=      do_normalize if do_normalize is not None else self.do_normalize
           lowerCAmelCase    :		Dict				=      image_mean if image_mean is not None else self.image_mean
           lowerCAmelCase    :		Any				=      image_std if image_std is not None else self.image_std

           lowerCAmelCase    :		List[str]				=      size if size is not None else self.size
           lowerCAmelCase    :		Tuple				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      crop_size if crop_size is not None else self.crop_size
           lowerCAmelCase    :		Any				=      get_size_dict(UpperCamelCase_    , param_name='''crop_size''' )

           if not valid_images(UpperCamelCase_ ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

           lowerCAmelCase    :		List[str]				=      make_batched(UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      [
               [
                   self._preprocess_image(
                       image=UpperCamelCase_    , do_resize=UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_    , do_center_crop=UpperCamelCase_    , crop_size=UpperCamelCase_    , do_rescale=UpperCamelCase_    , rescale_factor=UpperCamelCase_    , offset=UpperCamelCase_    , do_normalize=UpperCamelCase_    , image_mean=UpperCamelCase_    , image_std=UpperCamelCase_    , data_format=UpperCamelCase_    , )
                   for img in video
               ]
               for video in videos
           ]

           lowerCAmelCase    :		Optional[Any]				=      {'''pixel_values''': videos}
           return BatchFeature(data=UpperCamelCase_    , tensor_type=UpperCamelCase_ )










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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_a      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						for i in range(config.num_hidden_layers	):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")	)
												rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")	)
												rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")	)
												rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")	)
												rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")	)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('cls_token', 'vit.embeddings.cls_token'),
						        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
						        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
						        ('pos_embed', 'vit.embeddings.position_embeddings'),
						    ]	)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]	)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												_UpperCAmelCase									=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'	) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('norm.weight', 'vit.layernorm.weight'),
												        ('norm.bias', 'vit.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]	)

						return rename_keys


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False	)->  List[str]:





						"""simple docstring"""



						for i in range(config.num_hidden_layers	):
												if base_model:
																		_UpperCAmelCase									=			''
												else:
																		_UpperCAmelCase									=			'vit.'
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""	)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""	)
												# next, add query, keys and values (in that order) to the state dict
												_UpperCAmelCase									=			in_proj_weight[
												    : config.hidden_size, :
												]
												_UpperCAmelCase									=			in_proj_bias[: config.hidden_size]
												_UpperCAmelCase									=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCAmelCase									=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_UpperCAmelCase									=			in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCAmelCase									=			in_proj_bias[-config.hidden_size :]


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,		__lowerCAmelCase	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			dct.pop(__lowerCAmelCase	)
						_UpperCAmelCase									=			val


def __A    (      )->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCAmelCase									=			Image.open(requests.get(__lowerCAmelCase ,		stream=__lowerCAmelCase	).raw	)
						return im


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=True	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												_UpperCAmelCase									=			8
						# set labels if required
						if not base_model:
												_UpperCAmelCase									=			1_000
												_UpperCAmelCase									=			'huggingface/label-files'
												_UpperCAmelCase									=			'imagenet-1k-id2label.json'
												_UpperCAmelCase									=			json.load(open(hf_hub_download(__lowerCAmelCase ,		__lowerCAmelCase ,		repo_type='dataset'	) ,		'r'	)	)
												_UpperCAmelCase									=			{int(__lowerCAmelCase	): v for k, v in idalabel.items()}
												_UpperCAmelCase									=			idalabel
												_UpperCAmelCase									=			{v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												_UpperCAmelCase									=			384
												_UpperCAmelCase									=			1_536
												_UpperCAmelCase									=			12
												_UpperCAmelCase									=			6

						# load original model from torch hub
						_UpperCAmelCase									=			torch.hub.load('facebookresearch/dino:main' ,		__lowerCAmelCase	)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						_UpperCAmelCase									=			original_model.state_dict()
						if base_model:
												remove_classification_head_(__lowerCAmelCase	)
						_UpperCAmelCase									=			create_rename_keys(__lowerCAmelCase ,		base_model=__lowerCAmelCase	)
						for src, dest in rename_keys:
												rename_key(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
						read_in_q_k_v(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)

						# load HuggingFace model
						if base_model:
												_UpperCAmelCase									=			ViTModel(__lowerCAmelCase ,		add_pooling_layer=__lowerCAmelCase	).eval()
						else:
												_UpperCAmelCase									=			ViTForImageClassification(__lowerCAmelCase	).eval()
						model.load_state_dict(__lowerCAmelCase	)

						# Check outputs on an image, prepared by ViTImageProcessor
						_UpperCAmelCase									=			ViTImageProcessor()
						_UpperCAmelCase									=			image_processor(images=prepare_img() ,		return_tensors='pt'	)
						_UpperCAmelCase									=			encoding['pixel_values']
						_UpperCAmelCase									=			model(__lowerCAmelCase	)

						if base_model:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert torch.allclose(__lowerCAmelCase ,		outputs.last_hidden_state[:, 0, :] ,		atol=1E-1	)
						else:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(__lowerCAmelCase ,		outputs.logits ,		atol=1E-3	)

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


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''dino_vitb16''',
		    type=str,
		    help='''Name of the model trained with DINO you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--base_model''',
		    action='''store_true''',
		    help='''Whether to only convert the base model (no projection head weights).''',
		)

		parser.set_defaults(base_model=True)
		_a      	=  parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
