






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


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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    Any           =     "backbone." if is_semantic else ""

   lowercase_	:    Optional[int]           =     []
   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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




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

      lowercase_	:    Any           =     in_proj_weight[
          : config.hidden_size, :
      ]
      lowercase_	:    Tuple           =     q_bias
      lowercase_	:    Optional[Any]           =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase_	:    Optional[Any]           =     in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase_	:    int           =     v_bias

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

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     dct.pop(_UpperCamelCase							)
   lowercase_	:    Optional[Any]           =     val




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




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




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     False if "rvlcdip" in checkpoint_url else True
   lowercase_	:    Optional[int]           =     BeitConfig(use_absolute_position_embeddings=_UpperCamelCase						,		use_mask_token=_UpperCamelCase							)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      lowercase_	:    Union[str, Any]           =     1024
      lowercase_	:    Union[str, Any]           =     4096
      lowercase_	:    List[str]           =     24
      lowercase_	:    Optional[int]           =     16

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

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

   lowercase_	:    Any           =     create_rename_keys(_UpperCamelCase						,		has_lm_head=_UpperCamelCase							)
   for src, dest in rename_keys:
      rename_key(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   read_in_q_k_v(_UpperCamelCase						,		_UpperCamelCase						,		has_lm_head=_UpperCamelCase							)

   # load HuggingFace model
   lowercase_	:    List[Any]           =     BeitForMaskedImageModeling(_UpperCamelCase							) if has_lm_head else BeitForImageClassification(_UpperCamelCase							)
   model.eval()
   model.load_state_dict(_UpperCamelCase							)

   # Check outputs on an image
   lowercase_	:    str           =     BeitImageProcessor(
       size=config.image_size						,		resample=PILImageResampling.BILINEAR						,		do_center_crop=_UpperCamelCase							)
   lowercase_	:    Union[str, Any]           =     prepare_img()

   lowercase_	:    Optional[Any]           =     image_processor(images=_UpperCamelCase						,		return_tensors="pt"							)
   lowercase_	:    Any           =     encoding["pixel_values"]

   lowercase_	:    Any           =     model(_UpperCamelCase							)
   lowercase_	:    Any           =     outputs.logits

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

   Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
   print(F"""Saving model 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 push_to_hub:
      if has_lm_head:
         lowercase_	:    Any           =     "dit-base" if "base" in checkpoint_url else "dit-large"
      else:
         lowercase_	:    Tuple           =     "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(_UpperCamelCase						,		_UpperCamelCase							)						,		organization="nielsr"						,		commit_message="Add image processor"						,		use_temp_dir=_UpperCamelCase						,		)
      model.push_to_hub(
          repo_path_or_name=Path(_UpperCamelCase						,		_UpperCamelCase							)						,		organization="nielsr"						,		commit_message="Add model"						,		use_temp_dir=_UpperCamelCase						,		)


if __name__ == "__main__":
 UpperCamelCase__ =			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',
 )
 UpperCamelCase__ =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




