






"""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      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Any=False,						UpperCAmelCase__       :					Any=False      )		->Tuple:
						A__						:  List[Any]		  =  """backbone.""" if is_semantic else """"""

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

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

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

												A__						:  List[str]		  =  gamma_a
												A__						:  Optional[int]		  =  gamma_a




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					int      )		->List[Any]:
						A__						:  Optional[int]		  =  dct.pop(UpperCAmelCase__      )
						A__						:  str		  =  val




def      _lowerCAmelCase		(							)		->List[str]:
						A__						:  Optional[Any]		  =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__						:  int		  =  Image.open(requests.get(UpperCAmelCase__,						stream=UpperCAmelCase__      ).raw      )
						return im




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Optional[int]=False      )		->Union[str, Any]:
						A__						:  Any		  =  False if """rvlcdip""" in checkpoint_url else True
						A__						:  List[Any]		  =  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__						:  Tuple		  =  1_0_2_4
												A__						:  Optional[int]		  =  4_0_9_6
												A__						:  int		  =  2_4
												A__						:  Optional[Any]		  =  1_6

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

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

						A__						:  str		  =  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__						:  Tuple		  =  BeitForMaskedImageModeling(UpperCAmelCase__      ) if has_lm_head else BeitForImageClassification(UpperCAmelCase__      )
						model.eval()
						model.load_state_dict(UpperCAmelCase__      )

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

						A__						:  Tuple		  =  image_processor(images=UpperCAmelCase__,						return_tensors="""pt"""      )
						A__						:  Tuple		  =  encoding["""pixel_values"""]

						A__						:  str		  =  model(UpperCAmelCase__      )
						A__						:  Any		  =  outputs.logits

						# verify logits
						A__						:  Any		  =  [1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
						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__						:  Tuple		  =  """dit-base""" if """base""" in checkpoint_url else """dit-large"""
												else:
																		A__						:  Optional[int]		  =  """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__":
						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"""



from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													if not self.is_available():
																			raise RuntimeError(
																			    F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'							)


							@classmethod
							def 						_UpperCamelCase       (			cls      :						Optional[int]							):
													'''simple docstring'''





													return F'`pip install {cls.pip_package or cls.name}`'






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'optuna'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_optuna_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_optuna(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return default_hp_space_optuna(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'ray'
							snake_case_     =							'\'ray[tune]\''


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_ray_available()


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Tuple			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													return run_hp_search_ray(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict							):
													'''simple docstring'''





													return default_hp_space_ray(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'sigopt'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_sigopt_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return run_hp_search_sigopt(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Any							):
													'''simple docstring'''





													return default_hp_space_sigopt(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'wandb'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_wandb_available()


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_wandb(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return default_hp_space_wandb(snake_case							)




A_    =							{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def      _lowerCAmelCase		(							)		->str:
						A__						:  List[Any]		  =  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
						if len(UpperCAmelCase__      ) > 0:
												A__						:  Dict		  =  available_backends[0].name
												if len(UpperCAmelCase__      ) > 1:
																		logger.info(
																		    f'{len(UpperCAmelCase__      )} hyperparameter search backends available. Using {name} as the default.'      )
												return name
						raise RuntimeError(
						    """No hyperparameter search backend available.\n"""
						    + """\n""".join(
						        f' - To install {backend.name} run {backend.pip_install()}'
						        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()      )      )






