




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 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Optional[int]=False				,       UpperCamelCase__							:			Union[str, Any]=False				,       UpperCamelCase__							:			Optional[int]=False				)    -> List[str]:
       '''simple docstring'''


       __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 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				)    -> Dict:
       '''simple docstring'''


       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 		_A(       UpperCamelCase__							:			List[Any]				)    -> int:
       '''simple docstring'''


       __lowercase      =    ['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(UpperCamelCase__				,       UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Optional[Any]				)    -> int:
       '''simple docstring'''


       __lowercase      =    dct.pop(UpperCamelCase__				)
       __lowercase      =    val







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			int				)    -> Any:
       '''simple docstring'''


       __lowercase      =    ViltConfig(image_size=384				,       patch_size=32				,       tie_word_embeddings=UpperCamelCase__				)
       __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(UpperCamelCase__				,       UpperCamelCase__				,       repo_type='''dataset'''				)				,       '''r'''				)				)
              __lowercase      =    {int(UpperCamelCase__				): v for k, v in idalabel.items()}
              __lowercase      =    idalabel
              __lowercase      =    {v: k for k, v in idalabel.items()}
              __lowercase      =    ViltForQuestionAnswering(UpperCamelCase__				)
       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(UpperCamelCase__				)
       elif "irtr" in checkpoint_url:
              __lowercase      =    True
              __lowercase      =    ViltForImageAndTextRetrieval(UpperCamelCase__				)
       elif "mlm_itm" in checkpoint_url:
              __lowercase      =    True
              __lowercase      =    ViltForMaskedLM(UpperCamelCase__				)
       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(UpperCamelCase__				,       map_location='''cpu'''				)['''state_dict''']
       __lowercase      =    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      =    ['''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      =    model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__				)
              assert missing_keys == ["mlm_score.decoder.bias"]
       else:
              model.load_state_dict(UpperCamelCase__				)

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

       # 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=UpperCamelCase__				).raw				)
              __lowercase      =    Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''				,       stream=UpperCamelCase__				).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(UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''pt'''				)
              __lowercase      =    processor(UpperCamelCase__				,       UpperCamelCase__				,       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=UpperCamelCase__				).raw				)
              if mlm_model:
                     __lowercase      =    '''a bunch of [MASK] laying on a [MASK].'''
              else:
                     __lowercase      =    '''How many cats are there?'''
              __lowercase      =    processor(UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''pt'''				)
              __lowercase      =    model(**UpperCamelCase__				)

       # 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]				,       UpperCamelCase__				,       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]				,       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      =    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]				,       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__":
   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)












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

							__lowercase      =    [[int(UpperCamelCase__				) for cell in row.split(''','''				)] for row in data.strip().splitlines()]
							__lowercase      =    [[0 for cell in row] for row in grid]
							__lowercase      =    len(grid[0]				)

							__lowercase      =    [[0 for i in range(UpperCamelCase__				)] for j in range(UpperCamelCase__				)]
							__lowercase      =    grid[0][0]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[0][i] + dp[0][i - 1]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[i][0] + dp[i - 1][0]

							for i in range(1				,       UpperCamelCase__				):
														for j in range(1				,       UpperCamelCase__				):
																					__lowercase      =    grid[i][j] + min(dp[i - 1][j]				,       dp[i][j - 1]				)

							return dp[-1][-1]


if __name__ == "__main__":
			print(F"""{solution() = }""")






