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







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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  a__:					List[str]      							=     ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					List[str]      							=     dct.pop(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     val







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

  # load state_dict of original model, remove and rename some keys
  a__:					List[str]      							=     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )['state_dict']
  a__:					Optional[int]      							=     create_rename_keys(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_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       )
  if mlm_model or irtr_model:
    a__:					Any      							=     ['itm_score.fc.weight', 'itm_score.fc.bias']
    for k in ignore_keys:
      state_dict.pop(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

  # Define processor
  a__:					Optional[Any]      							=     ViltImageProcessor(size=384       )
  a__:					Union[str, Any]      							=     BertTokenizer.from_pretrained('bert-base-uncased'       )
  a__:					int      							=     ViltProcessor(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

  # Verify outputs
  if mlm_model:
    a__:					Dict      							=     torch.Size([1, 11, 30522]       )
    a__:					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]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )

    # verify masked token prediction equals "cats"
    a__:					Dict      							=     outputs.logits[0, 4, :].argmax(-1       ).item()
    assert tokenizer.decode([predicted_id]       ) == "cats"
  elif vqa_model:
    a__:					str      							=     torch.Size([1, 3129]       )
    a__:					Optional[int]      							=     torch.tensor([-15.9_495, -18.1_472, -10.3_041]       )
    assert torch.allclose(outputs.logits[0, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )

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

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


if __name__ == "__main__":
 lowercase__ 	=     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.'
 )

 lowercase__ 	=     parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


