






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__   :      Any				=		logging.get_logger(__name__)

def   __lowercase       (			snake_case,	snake_case=False,	snake_case=False			):







				"""simple docstring"""






				__magic_name__		:Any   =   '''backbone.''' if is_semantic else ''''''

				__magic_name__		:Tuple   =   []
				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   __lowercase       (			snake_case,	snake_case,	snake_case=False,	snake_case=False			):







				"""simple docstring"""






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

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

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__magic_name__		:Optional[Any]   =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''			)
								__magic_name__		:Optional[Any]   =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''			)

								__magic_name__		:List[Any]   =   gamma_a
								__magic_name__		:Optional[int]   =   gamma_a

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   dct.pop(snake_case			)
				__magic_name__		:Tuple   =   val

def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:str   =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:List[Any]   =   Image.open(requests.get(snake_case,	stream=snake_case			).raw			)
				return im

@torch.no_grad()
def   __lowercase       (			snake_case,	snake_case,	snake_case=False			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   False if '''rvlcdip''' in checkpoint_url else True
				__magic_name__		: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:
								__magic_name__		:Tuple   =   1_0_2_4
								__magic_name__		:int   =   4_0_9_6
								__magic_name__		:List[Any]   =   2_4
								__magic_name__		:Union[str, Any]   =   1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								__magic_name__		:List[str]   =   1_6
								__magic_name__		:Dict   =   '''huggingface/label-files'''
								__magic_name__		:int   =   '''rvlcdip-id2label.json'''
								__magic_name__		:Optional[int]   =   json.load(open(hf_hub_download(snake_case,	snake_case,	repo_type='''dataset'''			),	'''r'''			)			)
								__magic_name__		:List[str]   =   {int(snake_case			): v for k, v in idalabel.items()}
								__magic_name__		:List[str]   =   idalabel
								__magic_name__		:Tuple   =   {v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				__magic_name__		:Union[str, Any]   =   torch.hub.load_state_dict_from_url(snake_case,	map_location='''cpu'''			)['''model''']

				__magic_name__		:Dict   =   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
				__magic_name__		:Union[str, Any]   =   BeitForMaskedImageModeling(snake_case			) if has_lm_head else BeitForImageClassification(snake_case			)
				model.eval()
				model.load_state_dict(snake_case			)

				# Check outputs on an image
				__magic_name__		:Dict   =   BeitImageProcessor(
				    size=config.image_size,	resample=PILImageResampling.BILINEAR,	do_center_crop=snake_case			)
				__magic_name__		:int   =   prepare_img()

				__magic_name__		:Dict   =   image_processor(images=snake_case,	return_tensors='''pt'''			)
				__magic_name__		:str   =   encoding['''pixel_values''']

				__magic_name__		:List[str]   =   model(snake_case			)
				__magic_name__		:int   =   outputs.logits

				# verify logits
				__magic_name__		:Optional[int]   =   [1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				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:
												__magic_name__		:Union[str, Any]   =   '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
								else:
												__magic_name__		:str   =   '''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__":
					SCREAMING_SNAKE_CASE__   :      Dict				=		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__   :      Optional[int]				=		parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    torch.FloatTensor






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''







 @register_to_config
 def __init__(					self		:  List[str]			,				snake_case__		:  int = 3			,				snake_case__		:  int = 3			,				snake_case__		:  Tuple[str] = ("DownEncoderBlock2D",)			,				snake_case__		:  Tuple[str] = ("UpDecoderBlock2D",)			,				snake_case__		:  Tuple[int] = (64,)			,				snake_case__		:  int = 1			,				snake_case__		:  str = "silu"			,				snake_case__		:  int = 3			,				snake_case__		:  int = 32			,				snake_case__		:  int = 2_56			,				snake_case__		:  int = 32			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  float = 0.18215			,				snake_case__		:  str = "group"			,				):



   '''simple docstring'''




   super().__init__()

   # pass init params to Encoder
   UpperCAmelCase__      :					Tuple				       =							Encoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				down_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				double_z=snake_case__			,				)

   UpperCAmelCase__      :					str				       =							vq_embed_dim if vq_embed_dim is not None else latent_channels

   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )
   UpperCAmelCase__      :					int				       =							VectorQuantizer(snake_case__			,				snake_case__			,				beta=0.25			,				remap=snake_case__			,				sane_index_shape=snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )

   # pass init params to Decoder
   UpperCAmelCase__      :					Union[str, Any]				       =							Decoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				up_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				norm_type=snake_case__			,				)







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.encoder(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							self.quant_conv(snake_case__ )

   if not return_dict:
     return (h,)

   return VQEncoderOutput(latents=snake_case__ )







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   if not force_not_quantize:
     UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.quantize(snake_case__ )
   else:
     UpperCAmelCase__      :					Tuple				       =							h
   UpperCAmelCase__      :					int				       =							self.post_quant_conv(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							self.decoder(snake_case__			,				quant if self.config.norm_type == "spatial" else None )

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							sample
   UpperCAmelCase__      :					int				       =							self.encode(snake_case__ ).latents
   UpperCAmelCase__      :					Any				       =							self.decode(snake_case__ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )



