



"""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()
lowercase__					:						Optional[Any]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a=False	,    _a=False	,    _a=False					):
    snake_case_				:  Union[str, 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 __lowercase    (							_a	,    _a					):






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




def __lowercase    (							_a					):
    snake_case_				:  List[str]       =			["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE					)




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Optional[Any]       =			dct.pop(__SCREAMING_SNAKE_CASE					)
    snake_case_				:  Optional[int]       =			val




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

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

    # load state dict into HuggingFace model
    model.eval()
    if mlm_model:
        snake_case_				:  Optional[int]       =			model.load_state_dict(__SCREAMING_SNAKE_CASE	,    strict=__SCREAMING_SNAKE_CASE					)
        assert missing_keys == ["mlm_score.decoder.bias"]
    else:
        model.load_state_dict(__SCREAMING_SNAKE_CASE					)

    # Define processor
    snake_case_				:  Tuple       =			ViltImageProcessor(size=384					)
    snake_case_				:  Tuple       =			BertTokenizer.from_pretrained('''bert-base-uncased'''					)
    snake_case_				:  List[Any]       =			ViltProcessor(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE					)

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

    # Verify outputs
    if mlm_model:
        snake_case_				:  str       =			torch.Size([1, 11, 30_522]					)
        snake_case_				:  List[str]       =			torch.tensor([-12.5061, -12.5123, -12.5174]					)
        assert outputs.logits.shape == expected_shape
        assert torch.allclose(outputs.logits[0, 0, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=1E-4					)

        # verify masked token prediction equals "cats"
        snake_case_				:  Dict       =			outputs.logits[0, 4, :].argmax(-1					).item()
        assert tokenizer.decode([predicted_id]					) == "cats"
    elif vqa_model:
        snake_case_				:  Any       =			torch.Size([1, 3_129]					)
        snake_case_				:  Optional[int]       =			torch.tensor([-15.9495, -18.1472, -10.3041]					)
        assert torch.allclose(outputs.logits[0, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=1E-4					)
        assert outputs.logits.shape == expected_shape
        assert torch.allclose(outputs.logits[0, 0, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=1E-4					)

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

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


if __name__ == "__main__":
      lowercase__					:						str												=			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.'''
      )

      lowercase__					:						List[Any]												=			parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			img.shape
    for i in range(kernel_size // 2	,    size_x - kernel_size // 2					):
        for j in range(kernel_size // 2	,    size_y - kernel_size // 2					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Tuple												=			parse_args(sys.argv)
     lowercase__					:						str												=			cva.imread(filename, 0)
     cva.imshow('''input image''', img)

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





