


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()
lowerCamelCase											=       logging.get_logger(__name__)




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=False		,			SCREAMING_SNAKE_CASE__					:      Any=False		,			SCREAMING_SNAKE_CASE__					:      List[Any]=False     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[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__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=dct.pop(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[int]		=val




@torch.no_grad()
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      List[str]     ):
       '''simple docstring'''





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

       # load state_dict of original model, remove and rename some keys
       _lowerCamelCase       :		int		=torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__		,			map_location='cpu'     )['state_dict']
       _lowerCamelCase       :		str		=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:
              _lowerCamelCase       :		Dict		=['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:
              _lowerCamelCase	,     _lowerCamelCase       :		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
       _lowerCamelCase       :		List[Any]		=ViltImageProcessor(size=384     )
       _lowerCamelCase       :		Dict		=BertTokenizer.from_pretrained('bert-base-uncased'     )
       _lowerCamelCase       :		Tuple		=ViltProcessor(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

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

       # Verify outputs
       if mlm_model:
              _lowerCamelCase       :		Union[str, Any]		=torch.Size([1, 11, 30_522]     )
              _lowerCamelCase       :		List[Any]		=torch.tensor([-12.50_61, -12.51_23, -12.51_74]     )
              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"
              _lowerCamelCase       :		List[Any]		=outputs.logits[0, 4, :].argmax(-1     ).item()
              assert tokenizer.decode([predicted_id]     ) == "cats"
       elif vqa_model:
              _lowerCamelCase       :		List[Any]		=torch.Size([1, 3_129]     )
              _lowerCamelCase       :		Dict		=torch.tensor([-15.94_95, -18.14_72, -10.30_41]     )
              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"
              _lowerCamelCase       :		List[Any]		=outputs.logits.argmax(-1     ).item()
              assert model.config.idalabel[predicted_idx] == "2"
       elif nlvr_model:
              _lowerCamelCase       :		Optional[int]		=torch.Size([1, 2]     )
              _lowerCamelCase       :		Dict		=torch.tensor([-2.87_21, 2.12_91]     )
              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__":
 lowerCamelCase											=       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.'
 )

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






# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


