

'''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									=     logging.get_logger(__name__)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__=False,							UpperCAmelCase__=False					)      ->      Tuple:
       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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False,							UpperCAmelCase__=False					)      ->      Any:
       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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      str:
       A_						       =   dct.pop(UpperCAmelCase__					)
       A_						       =   val
def    UpperCAmelCase__     (   )      ->      int:
       A_						       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_						       =   Image.open(requests.get(UpperCAmelCase__,							stream=UpperCAmelCase__					).raw					)
       return im
@torch.no_grad()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Union[str, Any]:
       A_						       =   False if """rvlcdip""" in checkpoint_url else True
       A_						       =   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:
              A_						       =   10_24
              A_						       =   40_96
              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(UpperCAmelCase__,							UpperCAmelCase__,							repo_type="""dataset"""					),							"""r"""					)					)
              A_						       =   {int(UpperCAmelCase__					): 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(UpperCAmelCase__,							map_location="""cpu"""					)["""model"""]

       A_						       =   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
       A_						       =   BeitForMaskedImageModeling(UpperCAmelCase__					) if has_lm_head else BeitForImageClassification(UpperCAmelCase__					)
       model.eval()
       model.load_state_dict(UpperCAmelCase__					)

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

       A_						       =   image_processor(images=UpperCAmelCase__,							return_tensors="""pt"""					)
       A_						       =   encoding["""pixel_values"""]

       A_						       =   model(UpperCAmelCase__					)
       A_						       =   outputs.logits

       # verify logits
       A_						       =   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
       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:
                     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(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__":
      __lowerCamelCase									=     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									=     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


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
