

"""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__ :     str							  =     logging.get_logger(__name__)
def 					UpperCamelCase    (	_lowerCAmelCase   :  Union[str, Any],     _lowerCAmelCase   :  Tuple=False,     _lowerCAmelCase   :  Union[str, Any]=False       ) ->	Union[str, Any]:
	_UpperCAmelCase			:							List[str]	  =    "backbone." if is_semantic else ""

	_UpperCAmelCase			:							Tuple	  =    []
	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 					UpperCamelCase    (	_lowerCAmelCase   :  List[Any],     _lowerCAmelCase   :  List[str],     _lowerCAmelCase   :  List[str]=False,     _lowerCAmelCase   :  List[Any]=False       ) ->	int:







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

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

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

		_UpperCAmelCase			:							List[Any]	  =    gamma_a
		_UpperCAmelCase			:							Any	  =    gamma_a
def 					UpperCamelCase    (	_lowerCAmelCase   :  Dict,     _lowerCAmelCase   :  Any,     _lowerCAmelCase   :  List[str]       ) ->	List[str]:
	_UpperCAmelCase			:							int	  =    dct.pop(_UpperCAmelCase       )
	_UpperCAmelCase			:							Optional[int]	  =    val
def 					UpperCamelCase    (	) ->	List[str]:
	_UpperCAmelCase			:							Dict	  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
	_UpperCAmelCase			:							List[str]	  =    Image.open(requests.get(_UpperCAmelCase,     stream=_UpperCAmelCase       ).raw       )
	return im
@torch.no_grad()
def 					UpperCamelCase    (	_lowerCAmelCase   :  Optional[Any],     _lowerCAmelCase   :  str,     _lowerCAmelCase   :  List[Any]=False       ) ->	Dict:
	_UpperCAmelCase			:							List[str]	  =    False if "rvlcdip" in checkpoint_url else True
	_UpperCAmelCase			:							Tuple	  =    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:
		_UpperCAmelCase			:							Optional[int]	  =    1024
		_UpperCAmelCase			:							Union[str, Any]	  =    4096
		_UpperCAmelCase			:							Tuple	  =    24
		_UpperCAmelCase			:							int	  =    16

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

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

	_UpperCAmelCase			:							Tuple	  =    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
	_UpperCAmelCase			:							Union[str, Any]	  =    BeitForMaskedImageModeling(_UpperCAmelCase       ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase       )
	model.eval()
	model.load_state_dict(_UpperCAmelCase       )

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

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

	_UpperCAmelCase			:							List[str]	  =    model(_UpperCAmelCase       )
	_UpperCAmelCase			:							str	  =    outputs.logits

	# verify logits
	_UpperCAmelCase			:							Any	  =    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
	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:
			_UpperCAmelCase			:							Optional[int]	  =    "dit-base" if "base" in checkpoint_url else "dit-large"
		else:
			_UpperCAmelCase			:							List[str]	  =    "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__":
						lowerCamelCase__ :     Optional[Any]							  =     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)






'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

