


"""simple docstring"""





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     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Union[str, Any]=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Union[str, Any]:
  lowerCAmelCase__    :    str								=					"backbone." if is_semantic else ""

  lowerCAmelCase__    :    Optional[Any]								=					[]
  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     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Dict=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Tuple:
  for i in range(config.num_hidden_layers				):
    lowerCAmelCase__    :    Dict								=					"backbone." if is_semantic else ""
    # queries, keys and values
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"				)
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"				)
    lowerCAmelCase__    :    Optional[Any]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"				)

    lowerCAmelCase__    :    Union[str, Any]								=					in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase__    :    Dict								=					q_bias
    lowerCAmelCase__    :    str								=					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__    :    Optional[int]								=					in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase__    :    Optional[Any]								=					v_bias

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

    lowerCAmelCase__    :    Tuple								=					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__    :    int								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"				)
      lowerCAmelCase__    :    Optional[int]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"				)

      lowerCAmelCase__    :    Any								=					table
      lowerCAmelCase__    :    int								=					index





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Any				)					->     Dict:
  lowerCAmelCase__    :    str								=					dct.pop(lowerCamelCase				)
  lowerCAmelCase__    :    Union[str, Any]								=					val





def     lowercase__	(  )					->     Optional[int]:
  lowerCAmelCase__    :    Union[str, Any]								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__    :    List[str]								=					Image.open(requests.get(lowerCamelCase				,	stream=lowerCamelCase				).raw				)
  return im





@torch.no_grad()
def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Optional[int]				)					->     int:
  lowerCAmelCase__    :    str								=					BeitConfig()
  lowerCAmelCase__    :    Any								=					False
  lowerCAmelCase__    :    Optional[Any]								=					False
  lowerCAmelCase__    :    Optional[Any]								=					"huggingface/label-files"
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    List[str]								=					True
    lowerCAmelCase__    :    Union[str, Any]								=					True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    lowerCAmelCase__    :    List[Any]								=					True
    lowerCAmelCase__    :    Dict								=					2_1_8_4_1
    lowerCAmelCase__    :    str								=					"imagenet-22k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Tuple								=					{int(lowerCamelCase				): 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[9_2_0_5]
    del idalabel[1_5_0_2_7]
    lowerCAmelCase__    :    str								=					idalabel
    lowerCAmelCase__    :    str								=					{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Optional[int]								=					1_0_0_0
    lowerCAmelCase__    :    List[Any]								=					"imagenet-1k-id2label.json"
    lowerCAmelCase__    :    List[str]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Optional[Any]								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Union[str, Any]								=					{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      lowerCAmelCase__    :    str								=					3_8_4
    if "512" in checkpoint_url:
      lowerCAmelCase__    :    Union[str, Any]								=					5_1_2
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Tuple								=					1_5_0
    lowerCAmelCase__    :    Dict								=					"ade20k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    str								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Any								=					{v: k for k, v in idalabel.items()}
    lowerCAmelCase__    :    Any								=					6_4_0
    lowerCAmelCase__    :    Dict								=					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__    :    Optional[int]								=					1_0_2_4
    lowerCAmelCase__    :    int								=					4_0_9_6
    lowerCAmelCase__    :    Optional[Any]								=					2_4
    lowerCAmelCase__    :    Optional[int]								=					1_6
    if "ade20k" in checkpoint_url:
      lowerCAmelCase__    :    Optional[int]								=					6_4_0
      lowerCAmelCase__    :    Tuple								=					[7, 1_1, 1_5, 2_3]
  else:
    raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

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

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

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    lowerCAmelCase__    :    Union[str, Any]								=					BeitForMaskedImageModeling(lowerCamelCase				)
  elif "ade20k" in checkpoint_url:
    lowerCAmelCase__    :    List[str]								=					BeitForSemanticSegmentation(lowerCamelCase				)
  else:
    lowerCAmelCase__    :    int								=					BeitForImageClassification(lowerCamelCase				)
  model.eval()
  model.load_state_dict(lowerCamelCase				)

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

  lowerCAmelCase__    :    Optional[int]								=					image_processor(images=lowerCamelCase				,	return_tensors="pt"				)
  lowerCAmelCase__    :    Union[str, Any]								=					encoding["pixel_values"]

  lowerCAmelCase__    :    Optional[Any]								=					model(lowerCamelCase				)
  lowerCAmelCase__    :    Optional[int]								=					outputs.logits

  # verify logits
  lowerCAmelCase__    :    Union[str, Any]								=					torch.Size([1, 1_0_0_0]				)
  if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Dict								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    Optional[int]								=					torch.tensor([2.22_88, 2.46_71, 0.73_95]				)
    lowerCAmelCase__    :    Any								=					2_3_9_7
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Tuple								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    List[str]								=					torch.tensor([1.68_81, -0.27_87, 0.59_01]				)
    lowerCAmelCase__    :    int								=					2_3_9_6
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Tuple								=					torch.tensor([0.12_41, 0.07_98, -0.65_69]				)
    lowerCAmelCase__    :    Tuple								=					2_8_5
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([-1.23_85, -1.09_87, -1.01_08]				)
    lowerCAmelCase__    :    Union[str, Any]								=					2_8_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Optional[Any]								=					torch.tensor([-1.53_03, -0.94_84, -0.31_47]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([0.46_10, -0.09_28, 0.20_86]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    str								=					torch.tensor([-0.48_04, 0.62_57, -0.18_37]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    int								=					torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]				)
    lowerCAmelCase__    :    Optional[int]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([-0.30_62, 0.72_61, 0.48_52]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor(
        [
            [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
            [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
            [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
        ]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Dict								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    List[Any]								=					torch.tensor(
        [
            [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
            [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
            [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
        ]				)
  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]				,	lowerCamelCase				,	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]				,	lowerCamelCase				,	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(lowerCamelCase				).mkdir(exist_ok=lowerCamelCase				)
  print(F"Saving model to {pytorch_dump_folder_path}"				)
  model.save_pretrained(lowerCamelCase				)
  print(F"Saving image processor to {pytorch_dump_folder_path}"				)
  image_processor.save_pretrained(lowerCamelCase				)


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)




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      ["LayoutLMTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
