


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				:    List[Any]       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[int]=False ,  UpperCAmelCase_						:		Optional[Any]=False ,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





      a						:Any 							=       []
      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_						:		List[Any] ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      for i in range(config.num_hidden_layers						):
            a						:str 							=       '''vilt.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            a						:List[Any] 							=       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						:Union[str, Any] 							=       in_proj_weight[
                : config.hidden_size, :
            ]
            a						:Dict 							=       in_proj_bias[: config.hidden_size]
            a						:Any 							=       in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            a						:Optional[int] 							=       in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            a						:str 							=       in_proj_weight[
                -config.hidden_size :, :
            ]
            a						:Union[str, Any] 							=       in_proj_bias[-config.hidden_size :]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						:List[str] 							=       ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase_ ,  UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:int 							=       dct.pop(UpperCAmelCase_						)
      a						:Tuple 							=       val
@torch.no_grad()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





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

      # load state_dict of original model, remove and rename some keys
      a						:Dict 							=       torch.hub.load_state_dict_from_url(UpperCAmelCase_ ,  map_location='''cpu'''						)['''state_dict''']
      a						: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:
            a						:str 							=       ['''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:
            a							,				a						:List[Any] 							=       model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
            assert missing_keys == ["mlm_score.decoder.bias"]
      else:
            model.load_state_dict(UpperCAmelCase_						)

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

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

      # Verify outputs
      if mlm_model:
            a						:Any 							=       torch.Size([1, 11, 3_0522]						)
            a						:List[str] 							=       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"
            a						:Union[str, Any] 							=       outputs.logits[0, 4, :].argmax(-1						).item()
            assert tokenizer.decode([predicted_id]						) == "cats"
      elif vqa_model:
            a						:Tuple 							=       torch.Size([1, 3129]						)
            a						:List[str] 							=       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"
            a						:int 							=       outputs.logits.argmax(-1						).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            a						:Tuple 							=       torch.Size([1, 2]						)
            a						:Optional[int] 							=       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__":
     snake_case				:    Union[str, Any]       				=     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				:    List[str]       				=     parser.parse_args()
     convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=__magic_name__	)
class      A__							(     __magic_name__	):
				lowercase			    =  field(default='audio-classification'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'labels': ClassLabel}	)
				lowercase			    =  "audio"
				lowercase			    =  "labels"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Tuple   ):






							'''simple docstring'''







							if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
							if not isinstance(features[self.label_column]    ,     a   ):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
							lowerCAmelCase__      :      Tuple         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      List[Any]         =		self.label_schema.copy()
							lowerCAmelCase__      :      List[Any]         =		features[self.label_column]
							lowerCAmelCase__      :      Optional[int]         =		label_schema
							return task_template






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return {
							    self.audio_column: "audio",
							    self.label_column: "labels",
							}