





import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase						=      logging.get_logger(__name__)


def 			__UpperCamelCase				(			lowercase__	:       List[str]		,    lowercase__	:       Dict=False		,    lowercase__	:       int=False		)	->      List[Any]:
    '''simple docstring'''





    lowerCAmelCase_					:	Optional[int]						=   """backbone.""" if is_semantic else """"""

    lowerCAmelCase_					:	List[str]						=   []
    for i in range(config.num_hidden_layers		):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')		)
        rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')		)
        rename_keys.append(
            (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')		)
        rename_keys.append(
            (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')		)
        rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')		)
        rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')		)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')		)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')		)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')		)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')		)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f'{prefix}cls_token', """beit.embeddings.cls_token"""),
            (f'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
            (f'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
        ]		)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", """beit.embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    """beit.encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    """beit.encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]		)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]		)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
                ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]		)

    return rename_keys


def 			__UpperCamelCase				(			lowercase__	:       Dict		,    lowercase__	:       List[str]		,    lowercase__	:       str=False		,    lowercase__	:       Union[str, Any]=False		)	->      str:
    '''simple docstring'''





    for i in range(config.num_hidden_layers		):
        lowerCAmelCase_					:	Any						=   """backbone.""" if is_semantic else """"""
        # queries, keys and values
        lowerCAmelCase_					:	List[str]						=   state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'		)
        lowerCAmelCase_					:	List[Any]						=   state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'		)
        lowerCAmelCase_					:	List[Any]						=   state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'		)

        lowerCAmelCase_					:	Union[str, Any]						=   in_proj_weight[
            : config.hidden_size, :
        ]
        lowerCAmelCase_					:	str						=   q_bias
        lowerCAmelCase_					:	List[str]						=   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        lowerCAmelCase_					:	Dict						=   in_proj_weight[
            -config.hidden_size :, :
        ]
        lowerCAmelCase_					:	List[Any]						=   v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        lowerCAmelCase_					:	Optional[int]						=   state_dict.pop(f'{prefix}blocks.{i}.gamma_1'		)
        lowerCAmelCase_					:	str						=   state_dict.pop(f'{prefix}blocks.{i}.gamma_2'		)

        lowerCAmelCase_					:	List[str]						=   gamma_a
        lowerCAmelCase_					:	Any						=   gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            lowerCAmelCase_					:	List[Any]						=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'		)
            lowerCAmelCase_					:	List[Any]						=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'		)

            lowerCAmelCase_					:	Any						=   table
            lowerCAmelCase_					:	str						=   index


def 			__UpperCamelCase				(			lowercase__	:       Tuple		,    lowercase__	:       Optional[int]		,    lowercase__	:       int		)	->      Dict:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   dct.pop(lowercase__		)
    lowerCAmelCase_					:	Dict						=   val


def 			__UpperCamelCase				(			)	->      Union[str, Any]:
    '''simple docstring'''





    lowerCAmelCase_					:	List[str]						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase_					:	Optional[int]						=   Image.open(requests.get(lowercase__		,    stream=lowercase__		).raw		)
    return im


@torch.no_grad()
def 			__UpperCamelCase				(			lowercase__	:       Optional[Any]		,    lowercase__	:       Optional[int]		)	->      Tuple:
    '''simple docstring'''





    lowerCAmelCase_					:	List[str]						=   BeitConfig()
    lowerCAmelCase_					:	Dict						=   False
    lowerCAmelCase_					:	Optional[Any]						=   False
    lowerCAmelCase_					:	Tuple						=   """huggingface/label-files"""
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        lowerCAmelCase_					:	Optional[int]						=   True
        lowerCAmelCase_					:	Any						=   True
        lowerCAmelCase_					:	int						=   True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        lowerCAmelCase_					:	Optional[int]						=   True
        lowerCAmelCase_					:	int						=   21841
        lowerCAmelCase_					:	Any						=   """imagenet-22k-id2label.json"""
        lowerCAmelCase_					:	Tuple						=   json.load(open(hf_hub_download(lowercase__		,    lowercase__		,    repo_type="""dataset"""		)		,    """r"""		)		)
        lowerCAmelCase_					:	str						=   {int(lowercase__		): v for k, v in idalabel.items()}
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del idalabel[9205]
        del idalabel[15027]
        lowerCAmelCase_					:	Optional[int]						=   idalabel
        lowerCAmelCase_					:	Any						=   {v: k for k, v in idalabel.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        lowerCAmelCase_					:	Any						=   True
        lowerCAmelCase_					:	List[str]						=   1000
        lowerCAmelCase_					:	Union[str, Any]						=   """imagenet-1k-id2label.json"""
        lowerCAmelCase_					:	int						=   json.load(open(hf_hub_download(lowercase__		,    lowercase__		,    repo_type="""dataset"""		)		,    """r"""		)		)
        lowerCAmelCase_					:	Any						=   {int(lowercase__		): v for k, v in idalabel.items()}
        lowerCAmelCase_					:	Any						=   idalabel
        lowerCAmelCase_					:	str						=   {v: k for k, v in idalabel.items()}
        if "384" in checkpoint_url:
            lowerCAmelCase_					:	Optional[Any]						=   384
        if "512" in checkpoint_url:
            lowerCAmelCase_					:	Optional[int]						=   512
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        lowerCAmelCase_					:	str						=   True
        lowerCAmelCase_					:	Dict						=   150
        lowerCAmelCase_					:	Optional[int]						=   """ade20k-id2label.json"""
        lowerCAmelCase_					:	Union[str, Any]						=   json.load(open(hf_hub_download(lowercase__		,    lowercase__		,    repo_type="""dataset"""		)		,    """r"""		)		)
        lowerCAmelCase_					:	Union[str, Any]						=   {int(lowercase__		): v for k, v in idalabel.items()}
        lowerCAmelCase_					:	Optional[Any]						=   idalabel
        lowerCAmelCase_					:	str						=   {v: k for k, v in idalabel.items()}
        lowerCAmelCase_					:	int						=   640
        lowerCAmelCase_					:	List[str]						=   True
    else:
        raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""		)

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        lowerCAmelCase_					:	List[str]						=   1024
        lowerCAmelCase_					:	str						=   4096
        lowerCAmelCase_					:	Dict						=   24
        lowerCAmelCase_					:	Dict						=   16
        if "ade20k" in checkpoint_url:
            lowerCAmelCase_					:	Dict						=   640
            lowerCAmelCase_					:	int						=   [7, 11, 15, 23]
    else:
        raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""		)

    # load state_dict of original model, remove and rename some keys
    lowerCAmelCase_					:	Union[str, Any]						=   torch.hub.load_state_dict_from_url(lowercase__		,    map_location="""cpu"""		,    check_hash=lowercase__		)
    lowerCAmelCase_					:	Tuple						=   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

    lowerCAmelCase_					:	Optional[int]						=   create_rename_keys(lowercase__		,    has_lm_head=lowercase__		,    is_semantic=lowercase__		)
    for src, dest in rename_keys:
        rename_key(lowercase__		,    lowercase__		,    lowercase__		)
    read_in_q_k_v(lowercase__		,    lowercase__		,    has_lm_head=lowercase__		,    is_semantic=lowercase__		)
    if is_semantic:
        # add prefix to decoder keys
        for key, val in state_dict.copy().items():
            lowerCAmelCase_					:	str						=   state_dict.pop(lowercase__		)
            if key.startswith("""backbone.fpn"""		):
                lowerCAmelCase_					:	Optional[Any]						=   key.replace("""backbone.fpn"""		,    """fpn"""		)
            lowerCAmelCase_					:	Union[str, Any]						=   val

    # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        lowerCAmelCase_					:	Optional[int]						=   BeitForMaskedImageModeling(lowercase__		)
    elif "ade20k" in checkpoint_url:
        lowerCAmelCase_					:	List[Any]						=   BeitForSemanticSegmentation(lowercase__		)
    else:
        lowerCAmelCase_					:	List[Any]						=   BeitForImageClassification(lowercase__		)
    model.eval()
    model.load_state_dict(lowercase__		)

    # Check outputs on an image
    if is_semantic:
        lowerCAmelCase_					:	str						=   BeitImageProcessor(size=config.image_size		,    do_center_crop=lowercase__		)
        lowerCAmelCase_					:	List[str]						=   load_dataset("""hf-internal-testing/fixtures_ade20k"""		,    split="""test"""		)
        lowerCAmelCase_					:	List[str]						=   Image.open(ds[0]["""file"""]		)
    else:
        lowerCAmelCase_					:	Optional[int]						=   BeitImageProcessor(
            size=config.image_size		,    resample=PILImageResampling.BILINEAR		,    do_center_crop=lowercase__		)
        lowerCAmelCase_					:	Optional[Any]						=   prepare_img()

    lowerCAmelCase_					:	Optional[int]						=   image_processor(images=lowercase__		,    return_tensors="""pt"""		)
    lowerCAmelCase_					:	Dict						=   encoding["""pixel_values"""]

    lowerCAmelCase_					:	Union[str, Any]						=   model(lowercase__		)
    lowerCAmelCase_					:	Optional[Any]						=   outputs.logits

    # verify logits
    lowerCAmelCase_					:	str						=   torch.Size([1, 1000]		)
    if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""		):
        lowerCAmelCase_					:	List[Any]						=   torch.Size([1, 196, 8192]		)
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""		):
        lowerCAmelCase_					:	Union[str, Any]						=   torch.Size([1, 196, 8192]		)
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""		):
        lowerCAmelCase_					:	Any						=   torch.Size([1, 21841]		)
        lowerCAmelCase_					:	str						=   torch.tensor([2.2288, 2.4671, 0.7395]		)
        lowerCAmelCase_					:	Tuple						=   2397
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""		):
        lowerCAmelCase_					:	str						=   torch.Size([1, 21841]		)
        lowerCAmelCase_					:	Optional[int]						=   torch.tensor([1.6881, -0.2787, 0.5901]		)
        lowerCAmelCase_					:	List[str]						=   2396
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""		):
        lowerCAmelCase_					:	str						=   torch.tensor([0.1241, 0.0798, -0.6569]		)
        lowerCAmelCase_					:	Any						=   285
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""		):
        lowerCAmelCase_					:	List[str]						=   torch.tensor([-1.2385, -1.0987, -1.0108]		)
        lowerCAmelCase_					:	Optional[int]						=   281
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""		):
        lowerCAmelCase_					:	Any						=   torch.tensor([-1.5303, -0.9484, -0.3147]		)
        lowerCAmelCase_					:	str						=   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""		):
        lowerCAmelCase_					:	Optional[int]						=   torch.tensor([0.4610, -0.0928, 0.2086]		)
        lowerCAmelCase_					:	Union[str, Any]						=   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""		):
        lowerCAmelCase_					:	Union[str, Any]						=   torch.tensor([-0.4804, 0.6257, -0.1837]		)
        lowerCAmelCase_					:	List[str]						=   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""		):
        lowerCAmelCase_					:	Dict						=   torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
        lowerCAmelCase_					:	int						=   761
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""		):
        lowerCAmelCase_					:	Dict						=   torch.tensor([-0.3062, 0.7261, 0.4852]		)
        lowerCAmelCase_					:	Any						=   761
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""		):
        lowerCAmelCase_					:	Union[str, Any]						=   (1, 150, 160, 160)
        lowerCAmelCase_					:	List[str]						=   torch.tensor(
            [
                [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
            ]		)
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""		):
        lowerCAmelCase_					:	Dict						=   (1, 150, 160, 160)
        lowerCAmelCase_					:	int						=   torch.tensor(
            [
                [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
            ]		)
    else:
        raise ValueError("""Can't verify logits as model is not supported"""		)

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    if not has_lm_head:
        if is_semantic:
            assert torch.allclose(
                logits[0, :3, :3, :3]		,    lowercase__		,    atol=1E-3		), "First elements of logits not as expected"
        else:
            print("""Predicted class idx:"""		,    logits.argmax(-1		).item()		)
            assert torch.allclose(
                logits[0, :3]		,    lowercase__		,    atol=1E-3		), "First elements of logits not as expected"
            assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

    Path(lowercase__		).mkdir(exist_ok=lowercase__		)
    print(f'Saving model to {pytorch_dump_folder_path}'		)
    model.save_pretrained(lowercase__		)
    print(f'Saving image processor to {pytorch_dump_folder_path}'		)
    image_processor.save_pretrained(lowercase__		)


if __name__ == "__main__":
  __UpperCAmelCase						=      argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
      type=str,
      help='URL to the original PyTorch checkpoint (.pth file).',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  __UpperCAmelCase						=      parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









def 			__UpperCamelCase				(			lowercase__	:       set		,    lowercase__	:       set		)	->      set:
    '''simple docstring'''





    assert isinstance(lowercase__		,    lowercase__		), f'The input value of [set_a={set_a}] is not a set'
    assert isinstance(lowercase__		,    lowercase__		), f'The input value of [set_b={set_b}] is not a set'

    return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()


