

"""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()
UpperCAmelCase													=logging.get_logger(__name__)



def   _A					(	_a	:						Optional[int]     ,   _a	:						Tuple=False				):



      """simple docstring"""






      A				  =		[]
      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"
            A				  =		[(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					(	_a	:						str     ,   _a	:						Dict     ,   _a	:						Optional[Any]=False				):



      """simple docstring"""






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



def   _A					(	_a	:						List[str]				):



      """simple docstring"""






      A				  =		["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_a     ,   _a				)



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		dct.pop(_a				)
      A				  =		val



def   _A					(	):



      """simple docstring"""






      A				  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A				  =		Image.open(requests.get(_a     ,   stream=_a				).raw				)
      return im



@torch.no_grad()
def   _A					(	_a	:						str     ,   _a	:						int				):



      """simple docstring"""






      A				  =		ViTConfig()
      A				  =		False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            A				  =		True
            A				  =		int(vit_name[-1_2:-1_0]				)
            A				  =		int(vit_name[-9:-6]				)
      else:
            A				  =		1_0_0_0
            A				  =		"""huggingface/label-files"""
            A				  =		"""imagenet-1k-id2label.json"""
            A				  =		json.load(open(hf_hub_download(_a     ,   _a     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
            A				  =		{int(_a				): v for k, v in idalabel.items()}
            A				  =		idalabel
            A				  =		{v: k for k, v in idalabel.items()}
            A				  =		int(vit_name[-6:-4]				)
            A				  =		int(vit_name[-3:]				)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("""tiny"""				):
                  A				  =		1_9_2
                  A				  =		7_6_8
                  A				  =		1_2
                  A				  =		3
            elif vit_name[9:].startswith("""small"""				):
                  A				  =		3_8_4
                  A				  =		1_5_3_6
                  A				  =		1_2
                  A				  =		6
            else:
                  pass
      else:
            if vit_name[4:].startswith("""small"""				):
                  A				  =		7_6_8
                  A				  =		2_3_0_4
                  A				  =		8
                  A				  =		8
            elif vit_name[4:].startswith("""base"""				):
                  pass
            elif vit_name[4:].startswith("""large"""				):
                  A				  =		1_0_2_4
                  A				  =		4_0_9_6
                  A				  =		2_4
                  A				  =		1_6
            elif vit_name[4:].startswith("""huge"""				):
                  A				  =		1_2_8_0
                  A				  =		5_1_2_0
                  A				  =		3_2
                  A				  =		1_6

    # load original model from timm
      A				  =		timm.create_model(_a     ,   pretrained=_a				)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      A				  =		timm_model.state_dict()
      if base_model:
            remove_classification_head_(_a				)
      A				  =		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":
            A				  =		ViTModel(_a				).eval()
      else:
            A				  =		ViTForImageClassification(_a				).eval()
      model.load_state_dict(_a				)

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

      if base_model:
            A				  =		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:
            A				  =		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__":
  UpperCAmelCase													=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."
  )

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







"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




