



"""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()
a_				   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Dict=False				,    SCREAMING_SNAKE_CASE__ :  int=False			):



					"""simple docstring"""





					snake_case_					:		str 					=					"""backbone.""" if is_semantic else """"""

					snake_case_					:		str 					=					[]
					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__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=False				,    SCREAMING_SNAKE_CASE__ :  int=False			):



					"""simple docstring"""





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

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

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

										snake_case_					:		int 					=					gamma_a
										snake_case_					:		Any 					=					gamma_a







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		str 					=					dct.pop(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					val







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					snake_case_					:		int 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)
					return im







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=False			):



					"""simple docstring"""





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

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										snake_case_					:		Any 					=					1_0_2_4
										snake_case_					:		Optional[Any] 					=					4_0_9_6
										snake_case_					:		Any 					=					2_4
										snake_case_					:		Optional[int] 					=					1_6

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

					# load state_dict of original model, remove and rename some keys
					snake_case_					:		Tuple 					=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				,    map_location="""cpu"""			)["""model"""]

					snake_case_					:		int 					=					create_rename_keys(SCREAMING_SNAKE_CASE__				,    has_lm_head=SCREAMING_SNAKE_CASE__			)
					for src, dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					read_in_q_k_v(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    has_lm_head=SCREAMING_SNAKE_CASE__			)

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

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

					snake_case_					:		Dict 					=					image_processor(images=SCREAMING_SNAKE_CASE__				,    return_tensors="""pt"""			)
					snake_case_					:		Optional[int] 					=					encoding["""pixel_values"""]

					snake_case_					:		int 					=					model(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[int] 					=					outputs.logits

					# verify logits
					snake_case_					:		Any 					=					[1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
					assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE__			), "Shape of logits not as expected"

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

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


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









"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""upernet"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=5_12   ,		lowercase__=0.02   ,		lowercase__=[1, 2, 3, 6]   ,		lowercase__=True   ,		lowercase__=0.4   ,		lowercase__=3_84   ,		lowercase__=2_56   ,		lowercase__=1   ,		lowercase__=False   ,		lowercase__=2_55   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											if backbone_config is None:
																logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
																snake_case_					:		List[Any] 					=					CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
											elif isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		Tuple 					=					backbone_config.get("""model_type"""       )
																snake_case_					:		List[str] 					=					CONFIG_MAPPING[backbone_model_type]
																snake_case_					:		List[Any] 					=					config_class.from_dict(lowercase__       )

											snake_case_					:		List[Any] 					=					backbone_config
											snake_case_					:		Optional[Any] 					=					hidden_size
											snake_case_					:		Any 					=					initializer_range
											snake_case_					:		str 					=					pool_scales
											snake_case_					:		Dict 					=					use_auxiliary_head
											snake_case_					:		str 					=					auxiliary_loss_weight
											snake_case_					:		List[str] 					=					auxiliary_in_channels
											snake_case_					:		Optional[Any] 					=					auxiliary_channels
											snake_case_					:		Any 					=					auxiliary_num_convs
											snake_case_					:		List[Any] 					=					auxiliary_concat_input
											snake_case_					:		List[str] 					=					loss_ignore_index







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Union[str, Any] 					=					self.backbone_config.to_dict()
											snake_case_					:		Any 					=					self.__class__.model_type
											return output




