'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	Any							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False					):



 """simple docstring"""


 __a       =  []

 # fmt: off
 # stem:
 rename_keys.append(("cls_token", "vit.embeddings.cls_token")					)
 rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")					)

 rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")					)
 rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")					)

 # backbone
 rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")					)
 rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")					)
 rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")					)

 for stage_idx in range(len(config.backbone_config.depths					)					):
  for layer_idx in range(config.backbone_config.depths[stage_idx]					):
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

 # transformer encoder
 for i in range(config.num_hidden_layers					):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")					)
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")					)
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")					)
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")					)
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")					)
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")					)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")					)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")					)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")					)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")					)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
          ("pre_logits.fc.weight", "pooler.dense.weight"),
          ("pre_logits.fc.bias", "pooler.dense.bias"),
      ]					)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __a       =  [(pair[0], pair[1][4:]) if pair[1].startswith("vit"					) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("norm.weight", "vit.layernorm.weight"),
          ("norm.bias", "vit.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]					)
 # fmt: on

 return rename_keys



def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


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



def   __A		(			_A					):



 """simple docstring"""


 __a       =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(_A							,				_A					)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


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



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  BitConfig(
     global_padding="same"							,				layer_type="bottleneck"							,				depths=(3, 4, 9)							,				out_features=["stage3"]							,				embedding_dynamic_padding=_A							,				)
 __a       =  ViTHybridConfig(backbone_config=_A							,				image_size=384							,				num_labels=1000					)
 __a       =  False

 # load original model from timm
 __a       =  timm.create_model(_A							,				pretrained=_A					)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __a       =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(_A					)
 __a       =  create_rename_keys(_A							,				_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				_A					)

 __a       =  "huggingface/label-files"
 __a       =  "imagenet-1k-id2label.json"
 __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
 __a       =  {int(_A					): v for k, v in idalabel.items()}
 __a       =  idalabel
 __a       =  {v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __a       =  ViTHybridModel(_A					).eval()
 else:
  __a       =  ViTHybridForImageClassification(_A					).eval()
 model.load_state_dict(_A					)

 # create image processor
 __a       =  create_transform(**resolve_data_config({}							,				model=_A					)					)
 __a       =  transform.transforms

 __a       =  {
     "bilinear": PILImageResampling.BILINEAR,
     "bicubic": PILImageResampling.BICUBIC,
     "nearest": PILImageResampling.NEAREST,
 }

 __a       =  ViTHybridImageProcessor(
     do_resize=_A							,				size={"shortest_edge": timm_transforms[0].size}							,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,				do_center_crop=_A							,				crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}							,				do_normalize=_A							,				image_mean=timm_transforms[-1].mean.tolist()							,				image_std=timm_transforms[-1].std.tolist()							,				)

 __a       =  prepare_img()
 __a       =  transform(_A					).unsqueeze(0					)
 __a       =  processor(_A							,				return_tensors="pt"					).pixel_values

 # verify pixel values
 assert torch.allclose(_A							,				_A					)

 # verify logits
 with torch.no_grad():
  __a       =  model(_A					)
  __a       =  outputs.logits

 print("Predicted class:"							,				logits.argmax(-1					).item()					)
 if base_model:
  __a       =  timm_model.forward_features(_A					)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(_A							,				outputs.pooler_output							,				atol=1E-3					)
 else:
  __a       =  timm_model(_A					)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_A							,				outputs.logits							,				atol=1E-3					)
 print("Looks ok!"					)

 if pytorch_dump_folder_path is not None:
  Path(_A					).mkdir(exist_ok=_A					)
  print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
  model.save_pretrained(_A					)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
  processor.save_pretrained(_A					)

 if push_to_hub:
  print(f"""Pushing model and processor to the hub {vit_name}"""					)
  model.push_to_hub(f"""ybelkada/{vit_name}"""					)
  processor.push_to_hub(f"""ybelkada/{vit_name}"""					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	str							     =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_r50_s16_384""",
      type=str,
      help="""Name of the hybrid ViT timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
  )

  SCREAMING_SNAKE_CASE :	Tuple							     =		parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



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()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 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   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


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

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


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



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __a       =  torch.hub.load_state_dict_from_url(_A							,				map_location="cpu"					)["model"]

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

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

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "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(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


