






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:      str  				=				{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CLIPSegModel",
						    "CLIPSegPreTrainedModel",
						    "CLIPSegTextModel",
						    "CLIPSegVisionModel",
						    "CLIPSegForImageSegmentation",
						]

if TYPE_CHECKING:
						from .configuration_clipseg import (
						    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    CLIPSegConfig,
						    CLIPSegTextConfig,
						    CLIPSegVisionConfig,
						)
						from .processing_clipseg import CLIPSegProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_clipseg import (
												    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CLIPSegForImageSegmentation,
												    CLIPSegModel,
												    CLIPSegPreTrainedModel,
												    CLIPSegTextModel,
												    CLIPSegVisionModel,
												)

else:
						import sys

						A:      Optional[int]  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''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()
A:      List[Any]  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Dict=False						,			a		:	Union[str, Any]=False						,			a		:	Tuple=False							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   int        =			[]
						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    (  a		:	Dict						,			a		:	Tuple							)			->			Dict:





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






def 	_UpperCAmelCase    (  a		:	List[str]							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   Union[str, Any]        =			['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(a						,			a							)






def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Tuple						,			a		:	Union[str, Any]							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			dct.pop(a							)
						lowercase_      :   Dict        =			val






@torch.no_grad()
def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	List[Any]							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   str        =			ViltConfig(image_size=3_8_4						,			patch_size=3_2						,			tie_word_embeddings=a							)
						lowercase_      :   int        =			False
						lowercase_      :   Union[str, Any]        =			False
						lowercase_      :   List[str]        =			False
						lowercase_      :   str        =			False
						if "vqa" in checkpoint_url:
												lowercase_      :   str        =			True
												lowercase_      :   Optional[int]        =			3_1_2_9
												lowercase_      :   Any        =			'huggingface/label-files'
												lowercase_      :   Optional[Any]        =			'vqa2-id2label.json'
												lowercase_      :   int        =			json.load(open(hf_hub_download(a						,			a						,			repo_type='dataset'							)						,			'r'							)							)
												lowercase_      :   Optional[int]        =			{int(a							): v for k, v in idalabel.items()}
												lowercase_      :   List[Any]        =			idalabel
												lowercase_      :   str        =			{v: k for k, v in idalabel.items()}
												lowercase_      :   List[Any]        =			ViltForQuestionAnswering(a							)
						elif "nlvr" in checkpoint_url:
												lowercase_      :   Dict        =			True
												lowercase_      :   List[str]        =			2
												lowercase_      :   Tuple        =			{0: 'False', 1: 'True'}
												lowercase_      :   Optional[int]        =			{v: k for k, v in config.idalabel.items()}
												lowercase_      :   int        =			3
												lowercase_      :   Any        =			ViltForImagesAndTextClassification(a							)
						elif "irtr" in checkpoint_url:
												lowercase_      :   Union[str, Any]        =			True
												lowercase_      :   Dict        =			ViltForImageAndTextRetrieval(a							)
						elif "mlm_itm" in checkpoint_url:
												lowercase_      :   int        =			True
												lowercase_      :   Tuple        =			ViltForMaskedLM(a							)
						else:
												raise ValueError('Unknown model type'							)

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

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

						# Define processor
						lowercase_      :   Optional[int]        =			ViltImageProcessor(size=3_8_4							)
						lowercase_      :   Optional[int]        =			BertTokenizer.from_pretrained('bert-base-uncased'							)
						lowercase_      :   Any        =			ViltProcessor(a						,			a							)

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

						# Verify outputs
						if mlm_model:
												lowercase_      :   Union[str, Any]        =			torch.Size([1, 1_1, 3_0_5_2_2]							)
												lowercase_      :   Optional[Any]        =			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]						,			a						,			atol=1e-4							)

												# verify masked token prediction equals "cats"
												lowercase_      :   int        =			outputs.logits[0, 4, :].argmax(-1							).item()
												assert tokenizer.decode([predicted_id]							) == "cats"
						elif vqa_model:
												lowercase_      :   Optional[Any]        =			torch.Size([1, 3_1_2_9]							)
												lowercase_      :   Tuple        =			torch.tensor([-15.94_95, -18.14_72, -10.30_41]							)
												assert torch.allclose(outputs.logits[0, :3]						,			a						,			atol=1e-4							)
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3]						,			a						,			atol=1e-4							)

												# verify vqa prediction equals "2"
												lowercase_      :   Union[str, Any]        =			outputs.logits.argmax(-1							).item()
												assert model.config.idalabel[predicted_idx] == "2"
						elif nlvr_model:
												lowercase_      :   Optional[Any]        =			torch.Size([1, 2]							)
												lowercase_      :   Optional[Any]        =			torch.tensor([-2.87_21, 2.12_91]							)
												assert torch.allclose(outputs.logits[0, :3]						,			a						,			atol=1e-4							)
												assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
						A:      Optional[int]  				=				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."
						)

						A:      Union[str, Any]  				=				parser.parse_args()
						convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
