






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


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


def   lowerCamelCase__			(     lowercase						,     lowercase=False						,     lowercase=False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    "backbone." if is_semantic else ""

  SCREAMING_SNAKE_CASE       :      Tuple							=    []
  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"),
          (F'''{prefix}pos_embed''', "beit.embeddings.position_embeddings"),
      ]				)

  if has_lm_head:
    # mask token + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=False						,     lowercase=False				):

  """simple docstring"""






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

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

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

    SCREAMING_SNAKE_CASE       :      List[str]							=    gamma_a
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    gamma_a


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    dct.pop(lowercase				)
  SCREAMING_SNAKE_CASE       :      str							=    val


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE       :      str							=    Image.open(requests.get(lowercase						,     stream=lowercase				).raw				)
  return im


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    False if "rvlcdip" in checkpoint_url else True
  SCREAMING_SNAKE_CASE       :      List[Any]							=    BeitConfig(use_absolute_position_embeddings=lowercase						,     use_mask_token=lowercase				)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    SCREAMING_SNAKE_CASE       :      List[str]							=    1024
    SCREAMING_SNAKE_CASE       :      Tuple							=    4096
    SCREAMING_SNAKE_CASE       :      Dict							=    24
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    16

  # labels
  if "rvlcdip" in checkpoint_url:
    SCREAMING_SNAKE_CASE       :      int							=    16
    SCREAMING_SNAKE_CASE       :      int							=    "huggingface/label-files"
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "rvlcdip-id2label.json"
    SCREAMING_SNAKE_CASE       :      Dict							=    json.load(open(hf_hub_download(lowercase						,     lowercase						,     repo_type="dataset"				)						,     "r"				)				)
    SCREAMING_SNAKE_CASE       :      Dict							=    {int(lowercase				): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE       :      List[str]							=    idalabel
    SCREAMING_SNAKE_CASE       :      Any							=    {v: k for k, v in idalabel.items()}

  # load state_dict of original model, remove and rename some keys
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.hub.load_state_dict_from_url(lowercase						,     map_location="cpu"				)["model"]

  SCREAMING_SNAKE_CASE       :      List[Any]							=    create_rename_keys(lowercase						,     has_lm_head=lowercase				)
  for src, dest in rename_keys:
    rename_key(lowercase						,     lowercase						,     lowercase				)
  read_in_q_k_v(lowercase						,     lowercase						,     has_lm_head=lowercase				)

  # load HuggingFace model
  SCREAMING_SNAKE_CASE       :      Dict							=    BeitForMaskedImageModeling(lowercase				) if has_lm_head else BeitForImageClassification(lowercase				)
  model.eval()
  model.load_state_dict(lowercase				)

  # Check outputs on an image
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    BeitImageProcessor(
      size=config.image_size						,     resample=PILImageResampling.BILINEAR						,     do_center_crop=lowercase				)
  SCREAMING_SNAKE_CASE       :      int							=    prepare_img()

  SCREAMING_SNAKE_CASE       :      List[str]							=    image_processor(images=lowercase						,     return_tensors="pt"				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    encoding["pixel_values"]

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(lowercase				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    outputs.logits

  # verify logits
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
  assert logits.shape == torch.Size(lowercase				), "Shape of logits 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 push_to_hub:
    if has_lm_head:
      SCREAMING_SNAKE_CASE       :      List[str]							=    "dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      SCREAMING_SNAKE_CASE       :      Dict							=    "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
    image_processor.push_to_hub(
        repo_path_or_name=Path(lowercase						,     lowercase				)						,     organization="nielsr"						,     commit_message="Add image processor"						,     use_temp_dir=lowercase						,     )
    model.push_to_hub(
        repo_path_or_name=Path(lowercase						,     lowercase				)						,     organization="nielsr"						,     commit_message="Add model"						,     use_temp_dir=lowercase						,     )


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

   parser.add_argument(
       """--checkpoint_url""",
       default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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."""
   )
   parser.add_argument(
       """--push_to_hub""",
       action="""store_true""",
   )
   snake_case    				=		parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

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






