




'''simple docstring'''





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()
_SCREAMING_SNAKE_CASE							:   int      		= logging.get_logger(__name__)


def      UpperCamelCase_(	snake_case							:       Union[str, Any]							,				snake_case							:       Optional[Any]=False     ):

							'''simple docstring'''






							snake_case_						=      []
							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"
														snake_case_						=      [(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      UpperCamelCase_(	snake_case							:       Any							,				snake_case							:       Tuple							,				snake_case							:       str=False     ):

							'''simple docstring'''






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


def      UpperCamelCase_(	snake_case							:       Tuple     ):

							'''simple docstring'''






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


def      UpperCamelCase_(	snake_case							:       List[str]							,				snake_case							:       Union[str, Any]							,				snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      dct.pop(snake_case     )
							snake_case_						=      val


def      UpperCamelCase_(	):

							'''simple docstring'''






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


@torch.no_grad()
def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Any							,				snake_case							:       str=True     ):

							'''simple docstring'''






							snake_case_						=      ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														snake_case_						=      8
							# set labels if required
							if not base_model:
														snake_case_						=      1_0_0_0
														snake_case_						=      "huggingface/label-files"
														snake_case_						=      "imagenet-1k-id2label.json"
														snake_case_						=      json.load(open(hf_hub_download(snake_case							,				snake_case							,				repo_type="dataset"     )							,				"r"     )     )
														snake_case_						=      {int(snake_case     ): v for k, v in idalabel.items()}
														snake_case_						=      idalabel
														snake_case_						=      {v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														snake_case_						=      3_8_4
														snake_case_						=      1_5_3_6
														snake_case_						=      1_2
														snake_case_						=      6

							# load original model from torch hub
							snake_case_						=      torch.hub.load("facebookresearch/dino:main"							,				snake_case     )
							original_model.eval()

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

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

							# Check outputs on an image, prepared by ViTImageProcessor
							snake_case_						=      ViTImageProcessor()
							snake_case_						=      image_processor(images=prepare_img()							,				return_tensors="pt"     )
							snake_case_						=      encoding["pixel_values"]
							snake_case_						=      model(snake_case     )

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

							Path(snake_case     ).mkdir(exist_ok=snake_case     )
							print(f'Saving model {model_name} to {pytorch_dump_folder_path}'     )
							model.save_pretrained(snake_case     )
							print(f'Saving image processor to {pytorch_dump_folder_path}'     )
							image_processor.save_pretrained(snake_case     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   List[Any]      		= 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)
		_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



