






"""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()
_lowerCAmelCase	:	Optional[Any]				=      logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

     UpperCAmelCase__						:    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"),
             (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__       (  snake_case       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



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

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

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

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



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


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



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

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

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

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

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=snake_case				)
     for src, dest in rename_keys:
          rename_key(snake_case			,					snake_case			,					snake_case				)
     read_in_q_k_v(snake_case			,					snake_case			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

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

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


if __name__ == "__main__":
 _lowerCAmelCase	:	Tuple				=      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""",
 )
 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Union[str, Any]				=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''megatron-bert'''
   def __init__(		self      :	Tuple							,			snake_case__      :	List[Any]=2_9_0_5_6							,			snake_case__      :	Any=1_0_2_4							,			snake_case__      :	Tuple=2_4							,			snake_case__      :	Optional[Any]=1_6							,			snake_case__      :	List[str]=4_0_9_6							,			snake_case__      :	Union[str, Any]="gelu"							,			snake_case__      :	int=0.1							,			snake_case__      :	Dict=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Any=0.02							,			snake_case__      :	Optional[Any]=1e-12							,			snake_case__      :	str=0							,			snake_case__      :	Optional[Any]="absolute"							,			snake_case__      :	Optional[Any]=True							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    Optional[Any]							=							num_hidden_layers
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    List[Any]							=							hidden_act
        UpperCAmelCase__						:    Optional[int]							=							intermediate_size
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    str							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    List[str]							=							type_vocab_size
        UpperCAmelCase__						:    Tuple							=							initializer_range
        UpperCAmelCase__						:    str							=							layer_norm_eps
        UpperCAmelCase__						:    List[Any]							=							position_embedding_type
        UpperCAmelCase__						:    Dict							=							use_cache


