
'''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 (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							: Union[str, Any] =							logging.get_logger(__name__)







def 		A_							(						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     int=False					,						_lowerCAmelCase :     Any=False					,						_lowerCAmelCase :     Dict=False				):



      """simple docstring"""




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

      # embeddings
      rename_keys.extend(
          [
              # text embeddings
              ("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
              (
                  "text_embeddings.position_embeddings.weight",
                  "vilt.embeddings.text_embeddings.position_embeddings.weight",
              ),
              ("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
              (
                  "text_embeddings.token_type_embeddings.weight",
                  "vilt.embeddings.text_embeddings.token_type_embeddings.weight",
              ),
              ("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
              ("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
              # patch embeddings
              ("transformer.cls_token", "vilt.embeddings.cls_token"),
              ("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
              ("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
              ("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
              # token type embeddings
              ("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
          ]				)

      # final layernorm + pooler
      rename_keys.extend(
          [
              ("transformer.norm.weight", "vilt.layernorm.weight"),
              ("transformer.norm.bias", "vilt.layernorm.bias"),
              ("pooler.dense.weight", "vilt.pooler.dense.weight"),
              ("pooler.dense.bias", "vilt.pooler.dense.bias"),
          ]				)

      # classifier head(s)
      if vqa_model:
            # classification head
            rename_keys.extend(
                [
                    ("vqa_classifier.0.weight", "classifier.0.weight"),
                    ("vqa_classifier.0.bias", "classifier.0.bias"),
                    ("vqa_classifier.1.weight", "classifier.1.weight"),
                    ("vqa_classifier.1.bias", "classifier.1.bias"),
                    ("vqa_classifier.3.weight", "classifier.3.weight"),
                    ("vqa_classifier.3.bias", "classifier.3.bias"),
                ]				)
      elif nlvr_model:
            # classification head
            rename_keys.extend(
                [
                    ("nlvr2_classifier.0.weight", "classifier.0.weight"),
                    ("nlvr2_classifier.0.bias", "classifier.0.bias"),
                    ("nlvr2_classifier.1.weight", "classifier.1.weight"),
                    ("nlvr2_classifier.1.bias", "classifier.1.bias"),
                    ("nlvr2_classifier.3.weight", "classifier.3.weight"),
                    ("nlvr2_classifier.3.bias", "classifier.3.bias"),
                ]				)
      else:
            pass

      return rename_keys







def 		A_							(						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




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







def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = ["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(_lowerCAmelCase					,						_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = dct.pop(_lowerCAmelCase				)
      _lowerCamelCase					: str		    = val







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = ViltConfig(image_size=384					,						patch_size=32					,						tie_word_embeddings=_lowerCAmelCase				)
      _lowerCamelCase					: List[str]		    = False
      _lowerCamelCase					: Dict		    = False
      _lowerCamelCase					: Optional[Any]		    = False
      _lowerCamelCase					: List[str]		    = False
      if "vqa" in checkpoint_url:
            _lowerCamelCase					: Optional[int]		    = True
            _lowerCamelCase					: Tuple		    = 3129
            _lowerCamelCase					: Tuple		    = "huggingface/label-files"
            _lowerCamelCase					: List[str]		    = "vqa2-id2label.json"
            _lowerCamelCase					: int		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
            _lowerCamelCase					: str		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
            _lowerCamelCase					: List[Any]		    = idalabel
            _lowerCamelCase					: List[Any]		    = {v: k for k, v in idalabel.items()}
            _lowerCamelCase					: Optional[int]		    = ViltForQuestionAnswering(_lowerCAmelCase				)
      elif "nlvr" in checkpoint_url:
            _lowerCamelCase					: int		    = True
            _lowerCamelCase					: Dict		    = 2
            _lowerCamelCase					: Tuple		    = {0: "False", 1: "True"}
            _lowerCamelCase					: Any		    = {v: k for k, v in config.idalabel.items()}
            _lowerCamelCase					: Dict		    = 3
            _lowerCamelCase					: Dict		    = ViltForImagesAndTextClassification(_lowerCAmelCase				)
      elif "irtr" in checkpoint_url:
            _lowerCamelCase					: Optional[Any]		    = True
            _lowerCamelCase					: Tuple		    = ViltForImageAndTextRetrieval(_lowerCAmelCase				)
      elif "mlm_itm" in checkpoint_url:
            _lowerCamelCase					: List[str]		    = True
            _lowerCamelCase					: Dict		    = ViltForMaskedLM(_lowerCAmelCase				)
      else:
            raise ValueError("Unknown model type"				)

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase					: Tuple		    = torch.hub.load_state_dict_from_url(_lowerCAmelCase					,						map_location="cpu"				)["state_dict"]
      _lowerCamelCase					: Optional[int]		    = create_rename_keys(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
      read_in_q_k_v(_lowerCAmelCase					,						_lowerCAmelCase				)
      if mlm_model or irtr_model:
            _lowerCamelCase					: int		    = ["itm_score.fc.weight", "itm_score.fc.bias"]
            for k in ignore_keys:
                  state_dict.pop(_lowerCAmelCase					,						_lowerCAmelCase				)

    # load state dict into HuggingFace model
      model.eval()
      if mlm_model:
            _lowerCamelCase    ,		_lowerCamelCase					: Any		    = model.load_state_dict(_lowerCAmelCase					,						strict=_lowerCAmelCase				)
            assert missing_keys == ["mlm_score.decoder.bias"]
      else:
            model.load_state_dict(_lowerCAmelCase				)

      # Define processor
      _lowerCamelCase					: int		    = ViltImageProcessor(size=384				)
      _lowerCamelCase					: List[str]		    = BertTokenizer.from_pretrained("bert-base-uncased"				)
      _lowerCamelCase					: Optional[int]		    = ViltProcessor(_lowerCAmelCase					,						_lowerCAmelCase				)

      # Forward pass on example inputs (image + text)
      if nlvr_model:
            _lowerCamelCase					: Optional[Any]		    = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"					,						stream=_lowerCAmelCase				).raw				)
            _lowerCamelCase					: int		    = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"					,						stream=_lowerCAmelCase				).raw				)
            _lowerCamelCase					: Union[str, Any]		    = (
                "The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
                " standing."
            )
            _lowerCamelCase					: Dict		    = processor(_lowerCAmelCase					,						_lowerCAmelCase					,						return_tensors="pt"				)
            _lowerCamelCase					: Optional[int]		    = processor(_lowerCAmelCase					,						_lowerCAmelCase					,						return_tensors="pt"				)
            _lowerCamelCase					: Tuple		    = model(
                input_ids=encoding_a.input_ids					,						pixel_values=encoding_a.pixel_values					,						pixel_values_a=encoding_a.pixel_values					,						)
      else:
            _lowerCamelCase					: Any		    = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg"					,						stream=_lowerCAmelCase				).raw				)
            if mlm_model:
                  _lowerCamelCase					: str		    = "a bunch of [MASK] laying on a [MASK]."
            else:
                  _lowerCamelCase					: List[Any]		    = "How many cats are there?"
            _lowerCamelCase					: Optional[int]		    = processor(_lowerCAmelCase					,						_lowerCAmelCase					,						return_tensors="pt"				)
            _lowerCamelCase					: Optional[Any]		    = model(**_lowerCAmelCase				)

      # Verify outputs
      if mlm_model:
            _lowerCamelCase					: Tuple		    = torch.Size([1, 11, 30522]				)
            _lowerCamelCase					: List[str]		    = torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]				)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]					,						_lowerCAmelCase					,						atol=1E-4				)

            # verify masked token prediction equals "cats"
            _lowerCamelCase					: Optional[Any]		    = outputs.logits[0, 4, :].argmax(-1				).item()
            assert tokenizer.decode([predicted_id]				) == "cats"
      elif vqa_model:
            _lowerCamelCase					: Optional[int]		    = torch.Size([1, 3129]				)
            _lowerCamelCase					: List[str]		    = torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]				)
            assert torch.allclose(outputs.logits[0, :3]					,						_lowerCAmelCase					,						atol=1E-4				)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]					,						_lowerCAmelCase					,						atol=1E-4				)

            # verify vqa prediction equals "2"
            _lowerCamelCase					: str		    = outputs.logits.argmax(-1				).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            _lowerCamelCase					: Tuple		    = torch.Size([1, 2]				)
            _lowerCamelCase					: Optional[Any]		    = torch.tensor([-2.8_7_2_1, 2.1_2_9_1]				)
            assert torch.allclose(outputs.logits[0, :3]					,						_lowerCAmelCase					,						atol=1E-4				)
            assert outputs.logits.shape == expected_shape

      Path(_lowerCAmelCase				).mkdir(exist_ok=_lowerCAmelCase				)
      print(F'Saving model and processor to {pytorch_dump_folder_path}'				)
      model.save_pretrained(_lowerCAmelCase				)
      processor.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: List[Any] =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
     type=str,
     help='URL of the checkpoint 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_							: str =							parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""



from collections import namedtuple

import requests
from lxml import html  # type: ignore

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







def 						__lowercase	(  snake_case_    :    str = "https://www.worldometers.info/coronavirus/"		)  ->covid_data:



    '''simple docstring'''







    __A       :		List[str]							=	'''//div[@class = "maincounter-number"]/span/text()'''
    return covid_data(*html.fromstring(requests.get(snake_case_		).content		).xpath(snake_case_		)		)


a_					=     """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()))




