
"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A__ :       Any				= [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A__ :       Tuple				= [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Any   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''', '''rougeL''']    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''']    )
  assert (
      pd.DataFrame(no_aggregation['''rouge2''']    ).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['''rouge2''']    ).fmeasure.mean()
  )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  '''rougeLsum'''
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  _lowercase:      List[str]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  assert score > score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Tuple   			=  ['''rouge1''', '''rouge2''', '''rougeL''']
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  assert score_sep == score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  [
      '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
  ]
  _lowercase:      Union[str, Any]   			=  [
      '''Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
      ''' the final seconds on board Flight 9525.''',
  ]
  assert calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    ) == calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      int   			=  [
      '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
  ]
  _lowercase:      Union[str, Any]   			=  [
      ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
  ]

  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']			,					newline_sep=_UpperCamelCase    )['''rougeLsum''']
  _lowercase:      Union[str, Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']    )['''rougeLsum''']
  assert new_score > prev_score







def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  Path('''examples/seq2seq/test_data/wmt_en_ro'''    )
  _lowercase:      int   			=  calculate_rouge_path(data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge_path(
      data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )			,					bootstrap_aggregation=_UpperCamelCase    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )


"""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()
A__ :       int				= logging.get_logger(__name__)




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=False			,					_UpperCamelCase=False			,					_UpperCamelCase=False    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  []
  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 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase			,					_UpperCamelCase    )




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  dct.pop(_UpperCamelCase    )
  _lowercase:      Dict   			=  val




@torch.no_grad()
def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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

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

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

  # Define processor
  _lowercase:      int   			=  ViltImageProcessor(size=384    )
  _lowercase:      List[str]   			=  BertTokenizer.from_pretrained('''bert-base-uncased'''    )
  _lowercase:      List[Any]   			=  ViltProcessor(_UpperCamelCase			,					_UpperCamelCase    )

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

  # Verify outputs
  if mlm_model:
    _lowercase:      List[str]   			=  torch.Size([1, 11, 30_522]    )
    _lowercase:      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]			,					_UpperCamelCase			,					atol=1e-4    )

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

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

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


if __name__ == "__main__":
  A__ :       str				= 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.'
  )

  A__ :       str				= parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
