






"""simple docstring"""
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 			__UpperCamelCase	(		snake_case__   ,   snake_case__=False   ,   snake_case__=False   ,   snake_case__=False  ):
	A_		:						str	    =   []
	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 			__UpperCamelCase	(		snake_case__   ,   snake_case__  ):
	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_		:						Tuple	    =   state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight"""  )
		A_		:						List[str]	    =   state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.bias"""  )
		# next, add query, keys and values (in that order) to the state dict
		A_		:						Dict	    =   in_proj_weight[
		    : config.hidden_size, :
		]
		A_		:						Tuple	    =   in_proj_bias[: config.hidden_size]
		A_		:						List[str]	    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A_		:						List[str]	    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A_		:						Tuple	    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		A_		:						Optional[Any]	    =   in_proj_bias[-config.hidden_size :]






def 			__UpperCamelCase	(		snake_case__  ):
	A_		:						Any	    =   ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__  )






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__  ):
	A_		:						List[Any]	    =   dct.pop(snake_case__  )
	A_		:						Optional[int]	    =   val






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__  ):
	A_		:						List[Any]	    =   ViltConfig(image_size=384   ,   patch_size=32   ,   tie_word_embeddings=snake_case__  )
	A_		:						Union[str, Any]	    =   False
	A_		:						str	    =   False
	A_		:						Optional[int]	    =   False
	A_		:						Dict	    =   False
	if "vqa" in checkpoint_url:
		A_		:						Optional[int]	    =   True
		A_		:						Optional[int]	    =   3_129
		A_		:						Optional[int]	    =   """huggingface/label-files"""
		A_		:						str	    =   """vqa2-id2label.json"""
		A_		:						List[Any]	    =   json.load(open(hf_hub_download(snake_case__   ,   snake_case__   ,   repo_type="""dataset"""  )   ,   """r"""  )  )
		A_		:						Dict	    =   {int(snake_case__  ): v for k, v in idalabel.items()}
		A_		:						int	    =   idalabel
		A_		:						List[str]	    =   {v: k for k, v in idalabel.items()}
		A_		:						Tuple	    =   ViltForQuestionAnswering(snake_case__  )
	elif "nlvr" in checkpoint_url:
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   2
		A_		:						Optional[int]	    =   {0: """False""", 1: """True"""}
		A_		:						Union[str, Any]	    =   {v: k for k, v in config.idalabel.items()}
		A_		:						Optional[Any]	    =   3
		A_		:						int	    =   ViltForImagesAndTextClassification(snake_case__  )
	elif "irtr" in checkpoint_url:
		A_		:						Dict	    =   True
		A_		:						List[Any]	    =   ViltForImageAndTextRetrieval(snake_case__  )
	elif "mlm_itm" in checkpoint_url:
		A_		:						Any	    =   True
		A_		:						Union[str, Any]	    =   ViltForMaskedLM(snake_case__  )
	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(snake_case__   ,   map_location="""cpu"""  )["""state_dict"""]
	A_		:						List[str]	    =   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:
		A_		:						int	    =   ["""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:
		A_		:						Dict	    =   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
	A_		:						str	    =   ViltImageProcessor(size=384  )
	A_		:						Tuple	    =   BertTokenizer.from_pretrained("""bert-base-uncased"""  )
	A_		:						Optional[int]	    =   ViltProcessor(snake_case__   ,   snake_case__  )

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

	# Verify outputs
	if mlm_model:
		A_		:						List[Any]	    =   torch.Size([1, 11, 30_522]  )
		A_		:						Any	    =   torch.tensor([-12.5_061, -12.5_123, -12.5_174]  )
		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"
		A_		:						Any	    =   outputs.logits[0, 4, :].argmax(-1  ).item()
		assert tokenizer.decode([predicted_id]  ) == "cats"
	elif vqa_model:
		A_		:						int	    =   torch.Size([1, 3_129]  )
		A_		:						List[str]	    =   torch.tensor([-15.9_495, -18.1_472, -10.3_041]  )
		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"
		A_		:						Union[str, Any]	    =   outputs.logits.argmax(-1  ).item()
		assert model.config.idalabel[predicted_idx] == "2"
	elif nlvr_model:
		A_		:						List[Any]	    =   torch.Size([1, 2]  )
		A_		:						List[str]	    =   torch.tensor([-2.8_721, 2.1_291]  )
		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__":
							_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)









"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		if two_stage is True and with_box_refine is False:
			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
		# Hungarian matcher
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

