






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			:	int								=logging.get_logger(__name__)



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



def  a__ (__lowercase							:Union[str, Any]   )		-> Dict:
				_A	:				Optional[int]   =      ['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(__lowercase					,  __lowercase   )



def  a__ (__lowercase							:List[str]					,  __lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Union[str, Any]:
				_A	:				Union[str, Any]   =      dct.pop(__lowercase   )
				_A	:				Tuple   =      val



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

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

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

				# Define processor
				_A	:				Optional[Any]   =      ViltImageProcessor(size=384   )
				_A	:				Optional[int]   =      BertTokenizer.from_pretrained('''bert-base-uncased'''   )
				_A	:				Optional[Any]   =      ViltProcessor(__lowercase					,  __lowercase   )

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

				# Verify outputs
				if mlm_model:
								_A	:				Optional[int]   =      torch.Size([1, 11, 3_0522]   )
								_A	:				Optional[Any]   =      torch.tensor([-12.5061, -12.5123, -12.5174]   )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]					,  __lowercase					,  atol=1e-4   )

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

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

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


if __name__ == "__main__":
	_UpperCamelCase			:	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.'
	)

	_UpperCamelCase			:	Optional[int]								=parser.parse_args()
	convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



