"""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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A :     List[str]				       =				logging.get_logger(__name__)



def 		lowercase (       UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     Tuple=False		):





      """simple docstring"""
      A__	:      Tuple      =[]
      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''')		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "vit.embeddings.cls_token"),
              ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "vit.embeddings.position_embeddings"),
          ]		)

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

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            A__	:      Tuple      =[(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"),
                ]		)

      return rename_keys



def 		lowercase (       UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     int       ,  UpperCamelCase     :     Tuple=False		):





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



def 		lowercase (       UpperCamelCase     :     List[Any]		):





      """simple docstring"""
      A__	:      int      =["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase       ,  UpperCamelCase		)



def 		lowercase (       UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     str       ,  UpperCamelCase     :     List[Any]		):





      """simple docstring"""
      A__	:      Optional[Any]      =dct.pop(UpperCamelCase		)
      A__	:      Union[str, Any]      =val



def 		lowercase (       ):





      """simple docstring"""
      A__	:      Any      ="http://images.cocodataset.org/val2017/000000039769.jpg"
      A__	:      Any      =Image.open(requests.get(UpperCamelCase       ,  stream=UpperCamelCase		).raw		)
      return im



@torch.no_grad()
def 		lowercase (       UpperCamelCase     :     Tuple       ,  UpperCamelCase     :     str       ,  UpperCamelCase     :     Optional[int]=True		):





      """simple docstring"""
      A__	:      Dict      =ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            A__	:      List[Any]      =8
      # set labels if required
      if not base_model:
            A__	:      List[str]      =1000
            A__	:      Union[str, Any]      ="huggingface/label-files"
            A__	:      int      ="imagenet-1k-id2label.json"
            A__	:      Dict      =json.load(open(hf_hub_download(UpperCamelCase       ,  UpperCamelCase       ,  repo_type="dataset"		)       ,  "r"		)		)
            A__	:      Dict      ={int(UpperCamelCase		): v for k, v in idalabel.items()}
            A__	:      List[str]      =idalabel
            A__	:      str      ={v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            A__	:      List[str]      =384
            A__	:      Tuple      =1536
            A__	:      int      =12
            A__	:      Tuple      =6

      # load original model from torch hub
      A__	:      Any      =torch.hub.load("facebookresearch/dino:main"       ,  UpperCamelCase		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      A__	:      int      =original_model.state_dict()
      if base_model:
            remove_classification_head_(UpperCamelCase		)
      A__	:      Any      =create_rename_keys(UpperCamelCase       ,  base_model=UpperCamelCase		)
      for src, dest in rename_keys:
            rename_key(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase		)
      read_in_q_k_v(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase		)

      # load HuggingFace model
      if base_model:
            A__	:      str      =ViTModel(UpperCamelCase       ,  add_pooling_layer=UpperCamelCase		).eval()
      else:
            A__	:      Union[str, Any]      =ViTForImageClassification(UpperCamelCase		).eval()
      model.load_state_dict(UpperCamelCase		)

      # Check outputs on an image, prepared by ViTImageProcessor
      A__	:      int      =ViTImageProcessor()
      A__	:      Dict      =image_processor(images=prepare_img()       ,  return_tensors="pt"		)
      A__	:      List[str]      =encoding["pixel_values"]
      A__	:      List[str]      =model(UpperCamelCase		)

      if base_model:
            A__	:      List[Any]      =original_model(UpperCamelCase		)
            assert torch.allclose(UpperCamelCase       ,  outputs.last_hidden_state[:, 0, :]       ,  atol=1E-1		)
      else:
            A__	:      Optional[Any]      =original_model(UpperCamelCase		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(UpperCamelCase       ,  outputs.logits       ,  atol=1E-3		)

      Path(UpperCamelCase		).mkdir(exist_ok=UpperCamelCase		)
      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
      model.save_pretrained(UpperCamelCase		)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
      image_processor.save_pretrained(UpperCamelCase		)


if __name__ == "__main__":
  __A :     Tuple				       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="dino_vitb16",
      type=str,
      help="Name of the model trained with DINO 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(
      "--base_model",
      action="store_true",
      help="Whether to only convert the base model (no projection head weights).",
  )

  parser.set_defaults(base_model=True)
  __A :     List[Any]				       =				parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__							  =						{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizer"]

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

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

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


