






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase : Tuple						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCamelCase : Optional[Any]						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCamelCase : Tuple						   =       []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
	# loop over downblocks/upblocks

	for j in range(2):
		# loop over resnets/attentions for downblocks
		lowerCamelCase : str						   =       f'''down_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Optional[Any]						   =       f'''input_blocks.{3*i + j + 1}.0.'''
		unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

		if i < 3:
			# no attention layers in down_blocks.3
			lowerCamelCase : Optional[Any]						   =       f'''down_blocks.{i}.attentions.{j}.'''
			lowerCamelCase : str						   =       f'''input_blocks.{3*i + j + 1}.1.'''
			unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

	for j in range(3):
		# loop over resnets/attentions for upblocks
		lowerCamelCase : Union[str, Any]						   =       f'''up_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Optional[Any]						   =       f'''output_blocks.{3*i + j}.0.'''
		unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

		if i > 0:
			# no attention layers in up_blocks.0
			lowerCamelCase : List[Any]						   =       f'''up_blocks.{i}.attentions.{j}.'''
			lowerCamelCase : Optional[int]						   =       f'''output_blocks.{3*i + j}.1.'''
			unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

	if i < 3:
		# no downsample in down_blocks.3
		lowerCamelCase : List[str]						   =       f'''down_blocks.{i}.downsamplers.0.conv.'''
		lowerCamelCase : List[str]						   =       f'''input_blocks.{3*(i+1)}.0.op.'''
		unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

		# no upsample in up_blocks.3
		lowerCamelCase : Optional[int]						   =       f'''up_blocks.{i}.upsamplers.0.'''
		lowerCamelCase : Optional[int]						   =       f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase : Optional[Any]						   =       '''mid_block.attentions.0.'''
lowerCamelCase : Tuple						   =       '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	lowerCamelCase : Optional[Any]						   =       f'''mid_block.resnets.{j}.'''
	lowerCamelCase : int						   =       f'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def       snake_case_		(     lowerCAmelCase_       :						Optional[int]      ):
							# buyer beware: this is a *brittle* function,
							# and correct output requires that all of these pieces interact in
							# the exact order in which I have arranged them.
							__lowercase      :							List[str]				=     {k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														__lowercase      :							Union[str, Any]				=     sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												__lowercase      :							List[str]				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
																					__lowercase      :							str				=     v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					__lowercase      :							int				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
														__lowercase      :							List[Any]				=     v
							__lowercase      :							List[str]				=     {v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase : List[str]						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
	# down_blocks have two resnets
	for j in range(2):
		lowerCamelCase : str						   =       f'''encoder.down_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Dict						   =       f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		lowerCamelCase : List[str]						   =       f'''down_blocks.{i}.downsamplers.0.'''
		lowerCamelCase : Tuple						   =       f'''down.{i}.downsample.'''
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		lowerCamelCase : Union[str, Any]						   =       f'''up_blocks.{i}.upsamplers.0.'''
		lowerCamelCase : List[str]						   =       f'''up.{3-i}.upsample.'''
		vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

	# up_blocks have three resnets
	# also, up blocks in hf are numbered in reverse from sd
	for j in range(3):
		lowerCamelCase : Dict						   =       f'''decoder.up_blocks.{i}.resnets.{j}.'''
		lowerCamelCase : Any						   =       f'''decoder.up.{3-i}.block.{j}.'''
		vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
	lowerCamelCase : Optional[Any]						   =       f'''mid_block.resnets.{i}.'''
	lowerCamelCase : Dict						   =       f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase : str						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							# convert HF linear weights to SD conv2d weights
							return w.reshape(*w.shape       ,       1       ,       1      )
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]      ):
							__lowercase      :							str				=     {k: k for k in vae_state_dict.keys()}
							for k, v in mapping.items():
														for sd_part, hf_part in vae_conversion_map:
																					__lowercase      :							Optional[int]				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
														__lowercase      :							Optional[int]				=     v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												__lowercase      :							Any				=     v.replace(lowerCAmelCase_       ,       lowerCAmelCase_      )
																					__lowercase      :							Optional[Any]				=     v
							__lowercase      :							Any				=     {v: vae_state_dict[k] for k, v in mapping.items()}
							__lowercase      :							str				=     ["""q""", """k""", """v""", """proj_out"""]
							for k, v in new_state_dict.items():
														for weight_name in weights_to_convert:
																					if F"mid.attn_1.{weight_name}.weight" in k:
																												print(F"Reshaping {k} for SD format"      )
																												__lowercase      :							Dict				=     reshape_weight_for_sd(lowerCAmelCase_      )
							return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase : Tuple						   =       [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCamelCase : Union[str, Any]						   =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase : Any						   =       re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase : Dict						   =       {'''q''': 0, '''k''': 1, '''v''': 2}
def       snake_case_		(     lowerCAmelCase_       :						str      ):
							__lowercase      :							List[Any]				=     {}
							__lowercase      :							Tuple				=     {}
							__lowercase      :							Dict				=     {}
							for k, v in text_enc_dict.items():
														if (
														    k.endswith(""".self_attn.q_proj.weight"""      )
														    or k.endswith(""".self_attn.k_proj.weight"""      )
														    or k.endswith(""".self_attn.v_proj.weight"""      )
														):
																					__lowercase      :							List[str]				=     k[: -len(""".q_proj.weight"""      )]
																					__lowercase      :							Any				=     k[-len("""q_proj.weight"""      )]
																					if k_pre not in capture_qkv_weight:
																												__lowercase      :							Any				=     [None, None, None]
																					__lowercase      :							List[Any]				=     v
																					continue

														if (
														    k.endswith(""".self_attn.q_proj.bias"""      )
														    or k.endswith(""".self_attn.k_proj.bias"""      )
														    or k.endswith(""".self_attn.v_proj.bias"""      )
														):
																					__lowercase      :							Optional[Any]				=     k[: -len(""".q_proj.bias"""      )]
																					__lowercase      :							Optional[Any]				=     k[-len("""q_proj.bias"""      )]
																					if k_pre not in capture_qkv_bias:
																												__lowercase      :							Any				=     [None, None, None]
																					__lowercase      :							List[str]				=     v
																					continue

														__lowercase      :							List[Any]				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							Union[str, Any]				=     v

							for k_pre, tensors in capture_qkv_weight.items():
														if None in tensors:
																					raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
														__lowercase      :							Any				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							List[Any]				=     torch.cat(lowerCAmelCase_      )

							for k_pre, tensors in capture_qkv_bias.items():
														if None in tensors:
																					raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
														__lowercase      :							Union[str, Any]				=     textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,       lowerCAmelCase_      )
														__lowercase      :							str				=     torch.cat(lowerCAmelCase_      )

							return new_state_dict
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]      ):
							return text_enc_dict


if __name__ == "__main__":
	lowerCamelCase : Union[str, Any]						   =       argparse.ArgumentParser()

	parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
	parser.add_argument(
	    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
	)

	lowerCamelCase : List[Any]						   =       parser.parse_args()

	assert args.model_path is not None, "Must provide a model path!"

	assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

	# Path for safetensors
	lowerCamelCase : Dict						   =       osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase : Tuple						   =       osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
	lowerCamelCase : List[str]						   =       osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

	# Load models from safetensors if it exists, if it doesn't pytorch
	if osp.exists(unet_path):
		lowerCamelCase : Any						   =       load_file(unet_path, device='''cpu''')
	else:
		lowerCamelCase : Optional[Any]						   =       osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
		lowerCamelCase : List[Any]						   =       torch.load(unet_path, map_location='''cpu''')

	if osp.exists(vae_path):
		lowerCamelCase : List[str]						   =       load_file(vae_path, device='''cpu''')
	else:
		lowerCamelCase : Tuple						   =       osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
		lowerCamelCase : str						   =       torch.load(vae_path, map_location='''cpu''')

	if osp.exists(text_enc_path):
		lowerCamelCase : Optional[Any]						   =       load_file(text_enc_path, device='''cpu''')
	else:
		lowerCamelCase : Any						   =       osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
		lowerCamelCase : Dict						   =       torch.load(text_enc_path, map_location='''cpu''')

	# Convert the UNet model
	lowerCamelCase : Dict						   =       convert_unet_state_dict(unet_state_dict)
	lowerCamelCase : Optional[int]						   =       {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	lowerCamelCase : Optional[int]						   =       convert_vae_state_dict(vae_state_dict)
	lowerCamelCase : Optional[Any]						   =       {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

	# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
	lowerCamelCase : Union[str, Any]						   =       '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

	if is_vaa_model:
		# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
		lowerCamelCase : int						   =       {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
		lowerCamelCase : Tuple						   =       convert_text_enc_state_dict_vaa(text_enc_dict)
		lowerCamelCase : str						   =       {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
	else:
		lowerCamelCase : Union[str, Any]						   =       convert_text_enc_state_dict(text_enc_dict)
		lowerCamelCase : Optional[int]						   =       {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

	# Put together new checkpoint
	lowerCamelCase : List[Any]						   =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
	if args.half:
		lowerCamelCase : Union[str, Any]						   =       {k: v.half() for k, v in state_dict.items()}

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		lowerCamelCase : List[str]						   =       {'''state_dict''': state_dict}
		torch.save(state_dict, args.checkpoint_path)







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 : List[Any]						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str=False       ,       lowerCAmelCase_       :						Any=False      ):
							__lowercase      :							Any				=     """backbone.""" if is_semantic else """"""

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

														__lowercase      :							List[str]				=     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowercase      :							Union[str, Any]				=     q_bias
														__lowercase      :							Any				=     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowercase      :							Union[str, Any]				=     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowercase      :							str				=     v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__lowercase      :							int				=     state_dict.pop(F"{prefix}blocks.{i}.gamma_1"      )
														__lowercase      :							str				=     state_dict.pop(F"{prefix}blocks.{i}.gamma_2"      )

														__lowercase      :							List[str]				=     gamma_a
														__lowercase      :							Optional[int]				=     gamma_a
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Tuple				=     dct.pop(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     val
def       snake_case_		(     ):
							__lowercase      :							Optional[int]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase      :							Any				=     Image.open(requests.get(lowerCAmelCase_       ,       stream=lowerCAmelCase_      ).raw      )
							return im
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[int]=False      ):
							__lowercase      :							Dict				=     False if """rvlcdip""" in checkpoint_url else True
							__lowercase      :							Tuple				=     BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_       ,       use_mask_token=lowerCAmelCase_      )

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

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

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

							__lowercase      :							Union[str, Any]				=     create_rename_keys(lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )
							read_in_q_k_v(lowerCAmelCase_       ,       lowerCAmelCase_       ,       has_lm_head=lowerCAmelCase_      )

							# load HuggingFace model
							__lowercase      :							Dict				=     BeitForMaskedImageModeling(lowerCAmelCase_      ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_      )
							model.eval()
							model.load_state_dict(lowerCAmelCase_      )

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

							__lowercase      :							Optional[Any]				=     image_processor(images=lowerCAmelCase_       ,       return_tensors="""pt"""      )
							__lowercase      :							Optional[int]				=     encoding["""pixel_values"""]

							__lowercase      :							str				=     model(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     outputs.logits

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

							Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
							print(F"Saving model to {pytorch_dump_folder_path}"      )
							model.save_pretrained(lowerCAmelCase_      )
							print(F"Saving image processor to {pytorch_dump_folder_path}"      )
							image_processor.save_pretrained(lowerCAmelCase_      )

							if push_to_hub:
														if has_lm_head:
																					__lowercase      :							Optional[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(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add image processor"""       ,       use_temp_dir=lowerCAmelCase_       ,       )
														model.push_to_hub(
														    repo_path_or_name=Path(lowerCAmelCase_       ,       lowerCAmelCase_      )       ,       organization="""nielsr"""       ,       commit_message="""Add model"""       ,       use_temp_dir=lowerCAmelCase_       ,       )


if __name__ == "__main__":
	lowerCamelCase : List[str]						   =       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 : List[str]						   =       parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)