



'''simple docstring'''

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__lowerCAmelCase						      =       NewType('''DataClass''', Any)
__lowerCAmelCase						      =       NewType('''DataClassType''', Any)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Optional[Any]:
		if isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ):
				return v
		if v.lower() in ("yes", "true", "t", "y", "1"):
				return True
		elif v.lower() in ("no", "false", "f", "n", "0"):
				return False
		else:
				raise ArgumentTypeError(
				    f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Callable[[str], Any]:
		_a					:				Union[str, Any]				=							{str(lowerCAmelCase_  ): choice for choice in choices}
		return lambda lowerCAmelCase_  : str_to_choice.get(lowerCAmelCase_						,      lowerCAmelCase_  )



def 					__lowerCamelCase					(				*,
    lowerCAmelCase_ = None						,      lowerCAmelCase_ = None						,      lowerCAmelCase_ = dataclasses.MISSING						,      lowerCAmelCase_ = dataclasses.MISSING						,      lowerCAmelCase_ = None						,      **lowerCAmelCase_						,      )      ->				dataclasses.Field:
		if metadata is None:
				# Important, don't use as default param in function signature because dict is mutable and shared across function calls
				_a					:				Union[str, Any]				=							{}
		if aliases is not None:
				_a					:				int				=							aliases
		if help is not None:
				_a					:				Any				=							help

		return dataclasses.field(metadata=lowerCAmelCase_						,      default=lowerCAmelCase_						,      default_factory=lowerCAmelCase_						,      **lowerCAmelCase_  )

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Iterable[DataClassType]







							def __init__(							self :	Dict     ,_UpperCAmelCase :	Union[DataClassType, Iterable[DataClassType]]     ,**_UpperCAmelCase :	Union[str, Any]		):
									# To make the default appear when using --help
									if "formatter_class" not in kwargs:
											_a					:				Optional[int]				=							ArgumentDefaultsHelpFormatter
									super().__init__(**_UpperCAmelCase		)
									if dataclasses.is_dataclass(_UpperCAmelCase		):
											_a					:				Optional[Any]				=							[dataclass_types]
									_a					:				Tuple				=							list(_UpperCAmelCase		)
									for dtype in self.dataclass_types:
											self._add_dataclass_arguments(_UpperCAmelCase		)







							@staticmethod
							def 							__lowercase      (							_UpperCAmelCase :	ArgumentParser     ,_UpperCAmelCase :	dataclasses.Field		):
									_a					:				int				=							F"""--{field.name}"""
									_a					:				Dict				=							field.metadata.copy()
									# field.metadata is not used at all by Data Classes,
									# it is provided as a third-party extension mechanism.
									if isinstance(field.type     ,_UpperCAmelCase		):
											raise RuntimeError(
											    'Unresolved type detected, which should have been done with the help of '
											    '`typing.get_type_hints` method by default'		)

									_a					:				Any				=							kwargs.pop('aliases'     ,[]		)
									if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		):
											_a					:				Optional[int]				=							[aliases]

									_a					:				List[Any]				=							getattr(field.type     ,'__origin__'     ,field.type		)
									if origin_type is Union or (hasattr(_UpperCAmelCase     ,'UnionType'		) and isinstance(_UpperCAmelCase     ,types.UnionType		)):
											if str not in field.type.__args__ and (
											    len(field.type.__args__		) != 2 or type(_UpperCAmelCase		) not in field.type.__args__
											):
													raise ValueError(
													    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
													    ' the argument parser only supports one type per argument.'
													    F""" Problem encountered in field '{field.name}'."""		)
											if type(_UpperCAmelCase		) not in field.type.__args__:
													# filter `str` in Union
													_a					:				List[Any]				=							field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
													_a					:				str				=							getattr(field.type     ,'__origin__'     ,field.type		)
											elif bool not in field.type.__args__:
													# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
													_a					:				Dict				=							(
													    field.type.__args__[0] if isinstance(_UpperCAmelCase     ,field.type.__args__[1]		) else field.type.__args__[1]
													)
													_a					:				int				=							getattr(field.type     ,'__origin__'     ,field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
									_a					:				Dict				=							{}
									if origin_type is Literal or (isinstance(field.type     ,_UpperCAmelCase		) and issubclass(field.type     ,_UpperCAmelCase		)):
											if origin_type is Literal:
													_a					:				str				=							field.type.__args__
											else:
													_a					:				List[Any]				=							[x.value for x in field.type]

											_a					:				int				=							make_choice_type_function(kwargs['choices']		)

											if field.default is not dataclasses.MISSING:
													_a					:				Optional[Any]				=							field.default
											else:
													_a					:				List[str]				=							True
									elif field.type is bool or field.type == Optional[bool]:
											# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
											# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
											_a					:				str				=							copy(_UpperCAmelCase		)

											# Hack because type=bool in argparse does not behave as we want.
											_a					:				Optional[int]				=							string_to_bool
											if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
													# Default value is False if we have no default when of type bool.
													_a					:				Dict				=							False if field.default is dataclasses.MISSING else field.default
													# This is the value that will get picked if we don't include --field_name in any way
													_a					:				Tuple				=							default
													# This tells argparse we accept 0 or 1 value after --field_name
													_a					:				List[str]				=							'?'
													# This is the value that will get picked if we do --field_name (without value)
													_a					:				Tuple				=							True
									elif isclass(_UpperCAmelCase		) and issubclass(_UpperCAmelCase     ,_UpperCAmelCase		):
											_a					:				Dict				=							field.type.__args__[0]
											_a					:				Dict				=							'+'
											if field.default_factory is not dataclasses.MISSING:
													_a					:				int				=							field.default_factory()
											elif field.default is dataclasses.MISSING:
													_a					:				str				=							True
									else:
											_a					:				str				=							field.type
											if field.default is not dataclasses.MISSING:
													_a					:				Any				=							field.default
											elif field.default_factory is not dataclasses.MISSING:
													_a					:				Any				=							field.default_factory()
											else:
													_a					:				Optional[Any]				=							True
									parser.add_argument(_UpperCAmelCase     ,*_UpperCAmelCase     ,**_UpperCAmelCase		)

									# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
									# Order is important for arguments with the same destination!
									# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
									# here and we do not need those changes/additional keys.
									if field.default is True and (field.type is bool or field.type == Optional[bool]):
											_a					:				Any				=							False
											parser.add_argument(F"""--no_{field.name}"""     ,action='store_false'     ,dest=field.name     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	DataClassType		):
									if hasattr(_UpperCAmelCase     ,'_argument_group_name'		):
											_a					:				Optional[Any]				=							self.add_argument_group(dtype._argument_group_name		)
									else:
											_a					:				Optional[int]				=							self

									try:
											_a					:				Dict[str, type]				=							get_type_hints(_UpperCAmelCase		)
									except NameError:
											raise RuntimeError(
											    F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
											    'removing line of `from __future__ import annotations` which opts in Postponed '
											    'Evaluation of Annotations (PEP 563)'		)
									except TypeError as ex:
											# Remove this block when we drop Python 3.9 support
											if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_UpperCAmelCase		):
													_a					:				List[str]				=							'.'.join(map(_UpperCAmelCase     ,sys.version_info[:3]		)		)
													raise RuntimeError(
													    F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
													    'line of `from __future__ import annotations` which opts in union types as '
													    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
													    'support Python versions that lower than 3.10, you need to use '
													    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
													    '`X | None`.'		) from ex
											raise

									for field in dataclasses.fields(_UpperCAmelCase		):
											if not field.init:
													continue
											_a					:				List[str]				=							type_hints[field.name]
											self._parse_dataclass_field(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	Union[str, Any]=False     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	List[Any]=None     ,):

									if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
											_a					:				Any				=							[]

											if args_filename:
													args_files.append(Path(_UpperCAmelCase		)		)
											elif look_for_args_file and len(sys.argv		):
													args_files.append(Path(sys.argv[0]		).with_suffix('.args'		)		)

											# args files specified via command line flag should overwrite default args files so we add them last
											if args_file_flag:
													# Create special parser just to extract the args_file_flag values
													_a					:				Dict				=							ArgumentParser()
													args_file_parser.add_argument(_UpperCAmelCase     ,type=_UpperCAmelCase     ,action='append'		)

													# Use only remaining args for further parsing (remove the args_file_flag)
													_a	, _a					:				Dict				=							args_file_parser.parse_known_args(args=_UpperCAmelCase		)
													_a					:				Any				=							vars(_UpperCAmelCase		).get(args_file_flag.lstrip('-'		)     ,_UpperCAmelCase		)

													if cmd_args_file_paths:
															args_files.extend([Path(_UpperCAmelCase		) for p in cmd_args_file_paths]		)

											_a					:				int				=							[]
											for args_file in args_files:
													if args_file.exists():
															file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
											_a					:				str				=							file_args + args if args is not None else file_args + sys.argv[1:]
									_a	, _a					:				List[str]				=							self.parse_known_args(args=_UpperCAmelCase		)
									_a					:				str				=							[]
									for dtype in self.dataclass_types:
											_a					:				List[str]				=							{f.name for f in dataclasses.fields(_UpperCAmelCase		) if f.init}
											_a					:				Any				=							{k: v for k, v in vars(_UpperCAmelCase		).items() if k in keys}
											for k in keys:
													delattr(_UpperCAmelCase     ,_UpperCAmelCase		)
											_a					:				Union[str, Any]				=							dtype(**_UpperCAmelCase		)
											outputs.append(_UpperCAmelCase		)
									if len(namespace.__dict__		) > 0:
											# additional namespace.
											outputs.append(_UpperCAmelCase		)
									if return_remaining_strings:
											return (*outputs, remaining_args)
									else:
											if remaining_args:
													raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""		)

											return (*outputs,)







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	Dict[str, Any]     ,_UpperCAmelCase :	bool = False		):
									_a					:				Union[str, Any]				=							set(args.keys()		)
									_a					:				int				=							[]
									for dtype in self.dataclass_types:
											_a					:				List[str]				=							{f.name for f in dataclasses.fields(_UpperCAmelCase		) if f.init}
											_a					:				Union[str, Any]				=							{k: v for k, v in args.items() if k in keys}
											unused_keys.difference_update(inputs.keys()		)
											_a					:				int				=							dtype(**_UpperCAmelCase		)
											outputs.append(_UpperCAmelCase		)
									if not allow_extra_keys and unused_keys:
											raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(_UpperCAmelCase		)}"""		)
									return tuple(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	bool = False		):
									with open(Path(_UpperCAmelCase		)     ,encoding='utf-8'		) as open_json_file:
											_a					:				Any				=							json.loads(open_json_file.read()		)
									_a					:				int				=							self.parse_dict(_UpperCAmelCase     ,allow_extra_keys=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	bool = False		):
									_a					:				str				=							self.parse_dict(yaml.safe_load(Path(_UpperCAmelCase		).read_text()		)     ,allow_extra_keys=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)









'''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  )      ->				Any:
		_a					:				Dict				=							'backbone.' if is_semantic else ''

		_a					:				Optional[int]				=							[]
		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  )      ->				int:
		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					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

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

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

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Dict:
		_a					:				List[Any]				=							dct.pop(lowerCAmelCase_  )
		_a					:				List[Any]				=							val



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



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

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

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

		_a					:				Dict				=							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					:				Dict				=							BeitForMaskedImageModeling(lowerCAmelCase_  ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_  )
		model.eval()
		model.load_state_dict(lowerCAmelCase_  )

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

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

		_a					:				Optional[Any]				=							model(lowerCAmelCase_  )
		_a					:				Optional[Any]				=							outputs.logits

		# verify logits
		_a					:				int				=							[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					:				Tuple				=							'dit-base' if 'base' in checkpoint_url else 'dit-large'
				else:
						_a					:				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(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)




