





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()
SCREAMING_SNAKE_CASE_   :      List[str]			=   logging.get_logger(__name__)



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



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     ['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[int]:
		__lowercase											=     dct.pop(snake_case							)
		__lowercase											=     val



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		__lowercase											=     ViltConfig(image_size=384						,   patch_size=32						,   tie_word_embeddings=snake_case							)
		__lowercase											=     False
		__lowercase											=     False
		__lowercase											=     False
		__lowercase											=     False
		if "vqa" in checkpoint_url:
				__lowercase											=     True
				__lowercase											=     3_129
				__lowercase											=     'huggingface/label-files'
				__lowercase											=     'vqa2-id2label.json'
				__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
				__lowercase											=     {int(snake_case							): v for k, v in idalabel.items()}
				__lowercase											=     idalabel
				__lowercase											=     {v: k for k, v in idalabel.items()}
				__lowercase											=     ViltForQuestionAnswering(snake_case							)
		elif "nlvr" in checkpoint_url:
				__lowercase											=     True
				__lowercase											=     2
				__lowercase											=     {0: 'False', 1: 'True'}
				__lowercase											=     {v: k for k, v in config.idalabel.items()}
				__lowercase											=     3
				__lowercase											=     ViltForImagesAndTextClassification(snake_case							)
		elif "irtr" in checkpoint_url:
				__lowercase											=     True
				__lowercase											=     ViltForImageAndTextRetrieval(snake_case							)
		elif "mlm_itm" in checkpoint_url:
				__lowercase											=     True
				__lowercase											=     ViltForMaskedLM(snake_case							)
		else:
				raise ValueError('Unknown model type'							)

		# load state_dict of original model, remove and rename some keys
		__lowercase											=     torch.hub.load_state_dict_from_url(snake_case						,   map_location='cpu'							)['state_dict']
		__lowercase											=     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:
				__lowercase											=     ['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:
				__lowercase						,      __lowercase											=     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
		__lowercase											=     ViltImageProcessor(size=384							)
		__lowercase											=     BertTokenizer.from_pretrained('bert-base-uncased'							)
		__lowercase											=     ViltProcessor(snake_case						,   snake_case							)

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

		# Verify outputs
		if mlm_model:
				__lowercase											=     torch.Size([1, 11, 30_522]							)
				__lowercase											=     torch.tensor([-12.5061, -12.5123, -12.5174]							)
				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"
				__lowercase											=     outputs.logits[0, 4, :].argmax(-1							).item()
				assert tokenizer.decode([predicted_id]							) == "cats"
		elif vqa_model:
				__lowercase											=     torch.Size([1, 3_129]							)
				__lowercase											=     torch.tensor([-15.9495, -18.1472, -10.3041]							)
				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"
				__lowercase											=     outputs.logits.argmax(-1							).item()
				assert model.config.idalabel[predicted_idx] == "2"
		elif nlvr_model:
				__lowercase											=     torch.Size([1, 2]							)
				__lowercase											=     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__":
		SCREAMING_SNAKE_CASE_   :      Tuple			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




