






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

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(snake_case__      ,snake_case__		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Any     	=   dct.pop(snake_case__		)
			UpperCAmelCase_	:					Dict     	=   val

@torch.no_grad()
def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[Any]     	=   ViltConfig(image_size=3_84      ,patch_size=32      ,tie_word_embeddings=snake_case__		)
			UpperCAmelCase_	:					Dict     	=   False
			UpperCAmelCase_	:					Tuple     	=   False
			UpperCAmelCase_	:					Any     	=   False
			UpperCAmelCase_	:					int     	=   False
			if "vqa" in checkpoint_url:
						UpperCAmelCase_	:					List[str]     	=   True
						UpperCAmelCase_	:					Optional[Any]     	=   31_29
						UpperCAmelCase_	:					int     	=   "huggingface/label-files"
						UpperCAmelCase_	:					Tuple     	=   "vqa2-id2label.json"
						UpperCAmelCase_	:					List[str]     	=   json.load(open(hf_hub_download(snake_case__      ,snake_case__      ,repo_type="dataset"		)      ,"r"		)		)
						UpperCAmelCase_	:					List[Any]     	=   {int(snake_case__		): v for k, v in idalabel.items()}
						UpperCAmelCase_	:					List[Any]     	=   idalabel
						UpperCAmelCase_	:					Optional[int]     	=   {v: k for k, v in idalabel.items()}
						UpperCAmelCase_	:					Union[str, Any]     	=   ViltForQuestionAnswering(snake_case__		)
			elif "nlvr" in checkpoint_url:
						UpperCAmelCase_	:					Union[str, Any]     	=   True
						UpperCAmelCase_	:					List[str]     	=   2
						UpperCAmelCase_	:					List[Any]     	=   {0: "False", 1: "True"}
						UpperCAmelCase_	:					str     	=   {v: k for k, v in config.idalabel.items()}
						UpperCAmelCase_	:					Optional[Any]     	=   3
						UpperCAmelCase_	:					Any     	=   ViltForImagesAndTextClassification(snake_case__		)
			elif "irtr" in checkpoint_url:
						UpperCAmelCase_	:					Optional[int]     	=   True
						UpperCAmelCase_	:					Dict     	=   ViltForImageAndTextRetrieval(snake_case__		)
			elif "mlm_itm" in checkpoint_url:
						UpperCAmelCase_	:					Dict     	=   True
						UpperCAmelCase_	:					Dict     	=   ViltForMaskedLM(snake_case__		)
			else:
						raise ValueError("Unknown model type"		)

			# load state_dict of original model, remove and rename some keys
			UpperCAmelCase_	:					str     	=   torch.hub.load_state_dict_from_url(snake_case__      ,map_location="cpu"		)["state_dict"]
			UpperCAmelCase_	:					Any     	=   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:
						UpperCAmelCase_	:					Optional[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:
						UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   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
			UpperCAmelCase_	:					Dict     	=   ViltImageProcessor(size=3_84		)
			UpperCAmelCase_	:					Optional[Any]     	=   BertTokenizer.from_pretrained("bert-base-uncased"		)
			UpperCAmelCase_	:					str     	=   ViltProcessor(snake_case__      ,snake_case__		)

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

			# Verify outputs
			if mlm_model:
						UpperCAmelCase_	:					Any     	=   torch.Size([1, 11, 3_05_22]		)
						UpperCAmelCase_	:					Optional[int]     	=   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]      ,snake_case__      ,atol=1e-4		)

						# verify masked token prediction equals "cats"
						UpperCAmelCase_	:					Tuple     	=   outputs.logits[0, 4, :].argmax(-1		).item()
						assert tokenizer.decode([predicted_id]		) == "cats"
			elif vqa_model:
						UpperCAmelCase_	:					Any     	=   torch.Size([1, 31_29]		)
						UpperCAmelCase_	:					str     	=   torch.tensor([-15.94_95, -18.14_72, -10.30_41]		)
						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"
						UpperCAmelCase_	:					List[str]     	=   outputs.logits.argmax(-1		).item()
						assert model.config.idalabel[predicted_idx] == "2"
			elif nlvr_model:
						UpperCAmelCase_	:					Dict     	=   torch.Size([1, 2]		)
						UpperCAmelCase_	:					int     	=   torch.tensor([-2.8721, 2.1291]		)
						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)





import copy

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


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




