



'''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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_=False		,     lowerCAmelCase_=False    )     -> int:
		_a	: List[Any]								=				'backbone.' if is_semantic else ''

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

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

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

				_a	: Optional[Any]								=				gamma_a
				_a	: Dict								=				gamma_a



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		_a	: Optional[int]								=				dct.pop(lowerCAmelCase_    )
		_a	: str								=				val



def 	__lowerCamelCase	( )     -> List[str]:
		_a	: Union[str, Any]								=				'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a	: str								=				Image.open(requests.get(lowerCAmelCase_		,     stream=lowerCAmelCase_    ).raw    )
		return im



@torch.no_grad()
def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> List[str]:
		_a	: Any								=				False if 'rvlcdip' in checkpoint_url else True
		_a	: Dict								=				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:
				_a	: Optional[int]								=				1024
				_a	: Optional[Any]								=				4096
				_a	: int								=				24
				_a	: Tuple								=				16

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

		# load state_dict of original model, remove and rename some keys
		_a	: List[str]								=				torch.hub.load_state_dict_from_url(lowerCAmelCase_		,     map_location='cpu'    )['model']

		_a	: List[str]								=				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
		_a	: Tuple								=				BeitForMaskedImageModeling(lowerCAmelCase_    ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_    )
		model.eval()
		model.load_state_dict(lowerCAmelCase_    )

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

		_a	: List[Any]								=				image_processor(images=lowerCAmelCase_		,     return_tensors='pt'    )
		_a	: str								=				encoding['pixel_values']

		_a	: Any								=				model(lowerCAmelCase_    )
		_a	: Any								=				outputs.logits

		# verify logits
		_a	: Tuple								=				[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:
						_a	: List[Any]								=				'dit-base' if 'base' in checkpoint_url else 'dit-large'
				else:
						_a	: List[Any]								=				'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		     =  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase		     =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




