





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()
snake_case 				=       logging.get_logger(__name__)

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :Optional[Any]=False			,							snake_case__  :Tuple=False			,							snake_case__  :List[Any]=False     )      ->						List[Any]:
  _lowercase							= []
  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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :List[Any]     )      ->						Union[str, Any]:
  for i in range(config.num_hidden_layers     ):
    _lowercase							= 'vilt.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _lowercase							= state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight"""     )
    _lowercase							= state_dict.pop(F"""transformer.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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Union[str, Any]:
  _lowercase							= ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(snake_case__			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :int			,							snake_case__  :int     )      ->						str:
  _lowercase							= dct.pop(snake_case__     )
  _lowercase							= val

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

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

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

  # Define processor
  _lowercase							= ViltImageProcessor(size=384     )
  _lowercase							= BertTokenizer.from_pretrained('bert-base-uncased'     )
  _lowercase							= ViltProcessor(snake_case__			,							snake_case__     )

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

  # Verify outputs
  if mlm_model:
    _lowercase							= torch.Size([1, 11, 3_0522]     )
    _lowercase							= torch.tensor([-12.5061, -12.5123, -12.5174]     )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,							snake_case__			,							atol=1E-4     )

    # verify masked token prediction equals "cats"
    _lowercase							= outputs.logits[0, 4, :].argmax(-1     ).item()
    assert tokenizer.decode([predicted_id]     ) == "cats"
  elif vqa_model:
    _lowercase							= torch.Size([1, 3129]     )
    _lowercase							= torch.tensor([-15.9495, -18.1472, -10.3041]     )
    assert torch.allclose(outputs.logits[0, :3]			,							snake_case__			,							atol=1E-4     )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,							snake_case__			,							atol=1E-4     )

    # verify vqa prediction equals "2"
    _lowercase							= outputs.logits.argmax(-1     ).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _lowercase							= torch.Size([1, 2]     )
    _lowercase							= torch.tensor([-2.8721, 2.1291]     )
    assert torch.allclose(outputs.logits[0, :3]			,							snake_case__			,							atol=1E-4     )
    assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
      snake_case 				=       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."""
      )

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
      import torch

      from transformers.generation import DisjunctiveConstraint





@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         # For consistency across different places the DisjunctiveConstraint is called,
         # dc.token_ids is a list of integers. It is also initialized only by integers.

         _lowercase							= [[1, 2, 4], [1, 2, 3, 4]]
         _lowercase							= DisjunctiveConstraint(__A  )
         self.assertTrue(isinstance(dc.token_ids						,__A  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         # We can't have constraints that are complete subsets of another. This leads to a preverse
         # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
         # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
         # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
         # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
         _lowercase							= [[1, 2], [1, 2, 3, 4]]

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(__A  )  # fails here







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= [[1, 2, 3], [1, 2, 4]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(3  )
         _lowercase							= stepped is True and completed is True and reset is False
         self.assertTrue(__A  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 3]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(4  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2, 4]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

         dc.reset()

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 3  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 2  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.remaining() == 0  )
         self.assertTrue(dc.current_seq == [1, 2, 5]  )