






'''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      					=logging.get_logger(__name__)



def 				snake_case       (  a_				:			Optional[Any]     , a_				:			Optional[Any]=False     , a_				:			Optional[Any]=False     , a_				:			int=False						)							->					Optional[Any]:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				[]
      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 				snake_case       (  a_				:			List[str]     , a_				:			Any						)							->					int:





      """simple docstring"""

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



def 				snake_case       (  a_				:			str						)							->					str:





      """simple docstring"""

      UpperCamelCase_					: int            =				["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(a_     , a_						)



def 				snake_case       (  a_				:			Tuple     , a_				:			Optional[Any]     , a_				:			Optional[int]						)							->					Optional[Any]:





      """simple docstring"""

      UpperCamelCase_					: str            =				dct.pop(a_						)
      UpperCamelCase_					: Optional[Any]            =				val



@torch.no_grad()
def 				snake_case       (  a_				:			Union[str, Any]     , a_				:			Optional[Any]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				ViltConfig(image_size=384     , patch_size=32     , tie_word_embeddings=a_						)
      UpperCamelCase_					: List[Any]            =				False
      UpperCamelCase_					: Dict            =				False
      UpperCamelCase_					: List[str]            =				False
      UpperCamelCase_					: List[str]            =				False
      if "vqa" in checkpoint_url:
            UpperCamelCase_					: List[Any]            =				True
            UpperCamelCase_					: Union[str, Any]            =				3_129
            UpperCamelCase_					: Optional[int]            =				"""huggingface/label-files"""
            UpperCamelCase_					: Union[str, Any]            =				"""vqa2-id2label.json"""
            UpperCamelCase_					: Tuple            =				json.load(open(hf_hub_download(a_     , a_     , repo_type="""dataset"""						)     , """r"""						)						)
            UpperCamelCase_					: Any            =				{int(a_						): v for k, v in idalabel.items()}
            UpperCamelCase_					: int            =				idalabel
            UpperCamelCase_					: int            =				{v: k for k, v in idalabel.items()}
            UpperCamelCase_					: Tuple            =				ViltForQuestionAnswering(a_						)
      elif "nlvr" in checkpoint_url:
            UpperCamelCase_					: List[str]            =				True
            UpperCamelCase_					: Tuple            =				2
            UpperCamelCase_					: Union[str, Any]            =				{0: """False""", 1: """True"""}
            UpperCamelCase_					: Any            =				{v: k for k, v in config.idalabel.items()}
            UpperCamelCase_					: Optional[Any]            =				3
            UpperCamelCase_					: List[Any]            =				ViltForImagesAndTextClassification(a_						)
      elif "irtr" in checkpoint_url:
            UpperCamelCase_					: Dict            =				True
            UpperCamelCase_					: Dict            =				ViltForImageAndTextRetrieval(a_						)
      elif "mlm_itm" in checkpoint_url:
            UpperCamelCase_					: Optional[Any]            =				True
            UpperCamelCase_					: Tuple            =				ViltForMaskedLM(a_						)
      else:
            raise ValueError("""Unknown model type"""						)

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

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

      # Define processor
      UpperCamelCase_					: int            =				ViltImageProcessor(size=384						)
      UpperCamelCase_					: str            =				BertTokenizer.from_pretrained("""bert-base-uncased"""						)
      UpperCamelCase_					: int            =				ViltProcessor(a_     , a_						)

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

      # Verify outputs
      if mlm_model:
            UpperCamelCase_					: Any            =				torch.Size([1, 11, 30_522]						)
            UpperCamelCase_					: Tuple            =				torch.tensor([-12.5_061, -12.5_123, -12.5_174]						)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]     , a_     , atol=1e-4						)

            # verify masked token prediction equals "cats"
            UpperCamelCase_					: str            =				outputs.logits[0, 4, :].argmax(-1						).item()
            assert tokenizer.decode([predicted_id]						) == "cats"
      elif vqa_model:
            UpperCamelCase_					: List[str]            =				torch.Size([1, 3_129]						)
            UpperCamelCase_					: Optional[Any]            =				torch.tensor([-15.9_495, -18.1_472, -10.3_041]						)
            assert torch.allclose(outputs.logits[0, :3]     , a_     , atol=1e-4						)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]     , a_     , atol=1e-4						)

            # verify vqa prediction equals "2"
            UpperCamelCase_					: Dict            =				outputs.logits.argmax(-1						).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            UpperCamelCase_					: int            =				torch.Size([1, 2]						)
            UpperCamelCase_					: Any            =				torch.tensor([-2.8_721, 2.1_291]						)
            assert torch.allclose(outputs.logits[0, :3]     , a_     , atol=1e-4						)
            assert outputs.logits.shape == expected_shape

      Path(a_						).mkdir(exist_ok=a_						)
      print(f"Saving model and processor to {pytorch_dump_folder_path}"						)
      model.save_pretrained(a_						)
      processor.save_pretrained(a_						)


if __name__ == "__main__":
      UpperCamelCase      					=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      					=parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














'''simple docstring'''

def 				snake_case       (  a_				:			list						)							->					list:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				False
      while is_sorted is False:  # Until all the indices are traversed keep looping
            UpperCamelCase_					: Tuple            =				True
            for i in range(0     , len(a_						) - 1     , 2						):  # iterating over all even indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Any            =				False

            for i in range(1     , len(a_						) - 1     , 2						):  # iterating over all odd indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Dict            =				False
      return input_list


if __name__ == "__main__":
      print("Enter list to be sorted")
      UpperCamelCase      					=[int(x) for x in input().split()]
      # inputing elements of the list in one line
      UpperCamelCase      					=odd_even_sort(input_list)
      print("The sorted list is")
      print(sorted_list)






