





'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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





def   lowerCAmelCase_				( __a			,					__a=False	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   int    						=[]
  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"),
            ("pre_logits.fc.weight", "pooler.dense.weight"),
            ("pre_logits.fc.bias", "pooler.dense.bias"),
        ]	)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCamelCase__:   Any    						=[(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   lowerCAmelCase_				( __a			,					__a			,					__a=False	)  ->	Optional[int]:







  """simple docstring"""





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





def   lowerCAmelCase_				( __a	)  ->	List[Any]:







  """simple docstring"""





  lowerCamelCase__:   Tuple    						=["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(__a			,					__a	)





def   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	Dict:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						=dct.pop(__a	)
  lowerCamelCase__:   Tuple    						=val





def   lowerCAmelCase_				( )  ->	Optional[int]:







  """simple docstring"""





  lowerCamelCase__:   Any    						="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:   int    						=Image.open(requests.get(__a			,					stream=__a	).raw	)
  return im





@torch.no_grad()
def   lowerCAmelCase_				( __a			,					__a	)  ->	Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=ViTConfig()
  lowerCamelCase__:   Tuple    						=False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCamelCase__:   Optional[Any]    						=True
    lowerCamelCase__:   Dict    						=int(vit_name[-12:-10]	)
    lowerCamelCase__:   str    						=int(vit_name[-9:-6]	)
  else:
    lowerCamelCase__:   Any    						=1000
    lowerCamelCase__:   Optional[Any]    						="huggingface/label-files"
    lowerCamelCase__:   str    						="imagenet-1k-id2label.json"
    lowerCamelCase__:   Optional[Any]    						=json.load(open(hf_hub_download(__a			,					__a			,					repo_type="dataset"	)			,					"r"	)	)
    lowerCamelCase__:   Union[str, Any]    						={int(__a	): v for k, v in idalabel.items()}
    lowerCamelCase__:   Optional[int]    						=idalabel
    lowerCamelCase__:   Union[str, Any]    						={v: k for k, v in idalabel.items()}
    lowerCamelCase__:   Dict    						=int(vit_name[-6:-4]	)
    lowerCamelCase__:   Union[str, Any]    						=int(vit_name[-3:]	)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("tiny"	):
      lowerCamelCase__:   Tuple    						=192
      lowerCamelCase__:   Dict    						=768
      lowerCamelCase__:   Tuple    						=12
      lowerCamelCase__:   List[str]    						=3
    elif vit_name[9:].startswith("small"	):
      lowerCamelCase__:   Union[str, Any]    						=384
      lowerCamelCase__:   Optional[Any]    						=1536
      lowerCamelCase__:   str    						=12
      lowerCamelCase__:   Any    						=6
    else:
      pass
  else:
    if vit_name[4:].startswith("small"	):
      lowerCamelCase__:   Optional[int]    						=768
      lowerCamelCase__:   Optional[int]    						=2304
      lowerCamelCase__:   int    						=8
      lowerCamelCase__:   Optional[int]    						=8
    elif vit_name[4:].startswith("base"	):
      pass
    elif vit_name[4:].startswith("large"	):
      lowerCamelCase__:   Optional[int]    						=1024
      lowerCamelCase__:   Union[str, Any]    						=4096
      lowerCamelCase__:   str    						=24
      lowerCamelCase__:   Any    						=16
    elif vit_name[4:].startswith("huge"	):
      lowerCamelCase__:   Tuple    						=1280
      lowerCamelCase__:   Dict    						=5120
      lowerCamelCase__:   Tuple    						=32
      lowerCamelCase__:   Union[str, Any]    						=16

    # load original model from timm
  lowerCamelCase__:   List[Any]    						=timm.create_model(__a			,					pretrained=__a	)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCamelCase__:   Optional[Any]    						=timm_model.state_dict()
  if base_model:
    remove_classification_head_(__a	)
  lowerCamelCase__:   Dict    						=create_rename_keys(__a			,					__a	)
  for src, dest in rename_keys:
    rename_key(__a			,					__a			,					__a	)
  read_in_q_k_v(__a			,					__a			,					__a	)

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCamelCase__:   str    						=ViTModel(__a	).eval()
  else:
    lowerCamelCase__:   List[str]    						=ViTForImageClassification(__a	).eval()
  model.load_state_dict(__a	)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCamelCase__:   Dict    						=DeiTImageProcessor(size=config.image_size	)
  else:
    lowerCamelCase__:   Union[str, Any]    						=ViTImageProcessor(size=config.image_size	)
  lowerCamelCase__:   str    						=image_processor(images=prepare_img()			,					return_tensors="pt"	)
  lowerCamelCase__:   Union[str, Any]    						=encoding["pixel_values"]
  lowerCamelCase__:   Tuple    						=model(__a	)

  if base_model:
    lowerCamelCase__:   Union[str, Any]    						=timm_model.forward_features(__a	)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(__a			,					outputs.pooler_output			,					atol=1e-3	)
  else:
    lowerCamelCase__:   Optional[Any]    						=timm_model(__a	)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__a			,					outputs.logits			,					atol=1e-3	)

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


if __name__ == "__main__":
     __A             =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--vit_name",
         default="vit_base_patch16_224",
         type=str,
         help="Name of the ViT timm model you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     __A             =       parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








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

__A             =       logging.get_logger(__name__)





def   lowerCAmelCase_				( __a	)  ->	List[List[ImageInput]]:







  """simple docstring"""





  if isinstance(__a			,					(list, tuple)	) and isinstance(videos[0]			,					(list, tuple)	) and is_valid_image(videos[0][0]	):
    return videos

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

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

  raise ValueError(F"""Could not make batched video from {videos}"""	)






class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    ["pixel_values"]

 def __init__(self       :     Tuple ,				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 / 255 ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:   Optional[int]    						=size if size is not None else {"shortest_edge": 256}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   Tuple    						=crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				param_name="crop_size")

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   if "shortest_edge" in size:
     lowerCamelCase__:   Union[str, Any]    						=get_resize_output_image_size(UpperCAmelCase_ ,				size["shortest_edge"] ,				default_to_square=UpperCAmelCase_)
   elif "height" in size and "width" in size:
     lowerCamelCase__:   int    						=(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 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[int, float] ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=image.astype(np.floataa)
   if offset:
     lowerCamelCase__:   List[Any]    						=image - (scale / 2)
   return rescale(UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->np.ndarray:



   '''simple docstring'''



   return normalize(UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				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 ,				) ->np.ndarray:



   '''simple docstring'''



   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[Any]    						=self.resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_)

   if do_center_crop:
     lowerCamelCase__:   str    						=self.center_crop(UpperCAmelCase_ ,				size=UpperCAmelCase_)

   if do_rescale:
     lowerCamelCase__:   List[str]    						=self.rescale(image=UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				offset=UpperCAmelCase_)

   if do_normalize:
     lowerCamelCase__:   Dict    						=self.normalize(image=UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=to_channel_dimension_format(UpperCAmelCase_ ,				UpperCAmelCase_)
   return image







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[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[Union[str, TensorType]] = None ,				UpperCAmelCase_       :     ChannelDimension = ChannelDimension.FIRST ,				**UpperCAmelCase_       :     List[str] ,				) ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:   Optional[int]    						=resample if resample is not None else self.resample
   lowerCamelCase__:   Optional[int]    						=do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase__:   List[str]    						=do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:   Dict    						=rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:   Union[str, Any]    						=offset if offset is not None else self.offset
   lowerCamelCase__:   Dict    						=do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:   List[Any]    						=image_std if image_std is not None else self.image_std

   lowerCamelCase__:   List[Any]    						=size if size is not None else self.size
   lowerCamelCase__:   Optional[int]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   str    						=crop_size if crop_size is not None else self.crop_size
   lowerCamelCase__:   Optional[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__:   str    						=make_batched(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=[
       [
           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__:   int    						={"pixel_values": videos}
   return BatchFeature(data=UpperCAmelCase_ ,				tensor_type=UpperCAmelCase_)

