



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()
lowercase_						=       logging.get_logger(__name__)



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			dct.pop(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			val



def __lowerCAmelCase	(       ):
   lowercase__   					=			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase__   					=			Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,					stream=SCREAMING_SNAKE_CASE_					).raw					)
   return im



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=True					):
   lowercase__   					=			ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      lowercase__   					=			8
   # set labels if required
   if not base_model:
      lowercase__   					=			1000
      lowercase__   					=			"huggingface/label-files"
      lowercase__   					=			"imagenet-1k-id2label.json"
      lowercase__   					=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					repo_type="dataset"					)  ,					"r"					)					)
      lowercase__   					=			{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
      lowercase__   					=			idalabel
      lowercase__   					=			{v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      lowercase__   					=			384
      lowercase__   					=			1536
      lowercase__   					=			12
      lowercase__   					=			6

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

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

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

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

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

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


if __name__ == "__main__":
       lowercase_						=       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)
       lowercase_						=       parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









from collections import namedtuple

import requests
from lxml import html  # type: ignore

lowercase_						=       namedtuple("""covid_data""", """cases deaths recovered""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/"					):
			lowercase__   					=			"//div[@class = \"maincounter-number\"]/span/text()"
			return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_					).content					).xpath(SCREAMING_SNAKE_CASE_					)					)


lowercase_						=       """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""




print(fmt.format(*covid_stats()))




