



"""simple docstring"""







import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_   						=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_   						=   []
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
							)
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
							rename_keys.append(
							    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
							)
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
							rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
							)
							rename_keys.append(
							    (
							        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
							        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
							    )
							)
							rename_keys.append(
							    (
							        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
							        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
							    )
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
							)
							rename_keys.append(
							    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
							)
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
							rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		lowercase__   :    Optional[Any]				=			state_dict.pop(__lowerCamelCase	)
		lowercase__   :    Any				=			val



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowercase__   :    Any				=			key.replace('''backbone.0.body'''						,     '''backbone.conv_encoder.model'''	)
						lowercase__   :    str				=			value
				else:
						lowercase__   :    Optional[Any]				=			value

		return new_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Tuple:
		lowercase__   :    Any				=			''''''

		# first: transformer encoder
		for i in range(6	):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				lowercase__   :    int				=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
				lowercase__   :    List[str]				=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__   :    Tuple				=			in_proj_weight[:2_56, :]
				lowercase__   :    Union[str, Any]				=			in_proj_bias[:2_56]
				lowercase__   :    List[str]				=			in_proj_weight[2_56:5_12, :]
				lowercase__   :    Any				=			in_proj_bias[2_56:5_12]
				lowercase__   :    Any				=			in_proj_weight[-2_56:, :]
				lowercase__   :    Union[str, Any]				=			in_proj_bias[-2_56:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6	):
				# read in weights + bias of input projection layer of self-attention
				lowercase__   :    Tuple				=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
				lowercase__   :    Any				=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__   :    Any				=			in_proj_weight[:2_56, :]
				lowercase__   :    str				=			in_proj_bias[:2_56]
				lowercase__   :    Union[str, Any]				=			in_proj_weight[2_56:5_12, :]
				lowercase__   :    int				=			in_proj_bias[2_56:5_12]
				lowercase__   :    Optional[Any]				=			in_proj_weight[-2_56:, :]
				lowercase__   :    str				=			in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase__   :    Optional[Any]				=			state_dict.pop(
				    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
				lowercase__   :    Optional[Any]				=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				lowercase__   :    int				=			in_proj_weight_cross_attn[:2_56, :]
				lowercase__   :    Dict				=			in_proj_bias_cross_attn[:2_56]
				lowercase__   :    Any				=			in_proj_weight_cross_attn[2_56:5_12, :]
				lowercase__   :    Dict				=			in_proj_bias_cross_attn[2_56:5_12]
				lowercase__   :    Dict				=			in_proj_weight_cross_attn[-2_56:, :]
				lowercase__   :    Dict				=			in_proj_bias_cross_attn[-2_56:]



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		lowercase__		,			lowercase__   :    Tuple				=			image.size
		lowercase__   :    Optional[Any]				=			max(__lowerCamelCase						,     __lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			8_00 if '''detection''' in checkpoint_url else 10_00
		lowercase__   :    Any				=			target_max_size / current_max_size
		lowercase__   :    Optional[Any]				=			image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

		return resized_image



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		lowercase__   :    List[Any]				=			F.to_tensor(__lowerCamelCase	)
		lowercase__   :    Dict				=			F.normalize(__lowerCamelCase						,     mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]						,     std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	)
		return image



@torch.no_grad()
def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[Any]:
		logger.info('''Converting model...'''	)

		# load original state dict
		lowercase__   :    Tuple				=			torch.hub.load_state_dict_from_url(__lowerCamelCase						,     map_location='''cpu'''	)
		# rename keys
		for src, dest in rename_keys:
				rename_key(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		lowercase__   :    int				=			rename_backbone_keys(__lowerCamelCase	)
		# query, key and value matrices need special treatment
		read_in_q_k_v(__lowerCamelCase	)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase__   :    Tuple				=			'''model.'''
		for key in state_dict.copy().keys():
				if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
						lowercase__   :    Optional[Any]				=			state_dict.pop(__lowerCamelCase	)
						lowercase__   :    str				=			val
    # create HuggingFace model and load state dict
		lowercase__   :    Dict				=			TableTransformerConfig(
		    backbone='''resnet18'''						,     mask_loss_coefficient=1						,     dice_loss_coefficient=1						,     ce_loss_coefficient=1						,     bbox_loss_coefficient=5						,     giou_loss_coefficient=2						,     eos_coefficient=0.4						,     class_cost=1						,     bbox_cost=5						,     giou_cost=2						,     )

		if "detection" in checkpoint_url:
				lowercase__   :    int				=			15
				lowercase__   :    int				=			2
				lowercase__   :    Any				=			{0: '''table''', 1: '''table rotated'''}
				lowercase__   :    int				=			idalabel
				lowercase__   :    int				=			{v: k for k, v in idalabel.items()}
		else:
				lowercase__   :    List[Any]				=			1_25
				lowercase__   :    Optional[Any]				=			6
				lowercase__   :    Dict				=			{
				    0: '''table''',
				    1: '''table column''',
				    2: '''table row''',
				    3: '''table column header''',
				    4: '''table projected row header''',
				    5: '''table spanning cell''',
				}
				lowercase__   :    Optional[Any]				=			idalabel
				lowercase__   :    Optional[Any]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    Optional[int]				=			DetrImageProcessor(
		    format='''coco_detection'''						,     max_size=8_00 if '''detection''' in checkpoint_url else 10_00	)
		lowercase__   :    List[str]				=			TableTransformerForObjectDetection(__lowerCamelCase	)
		model.load_state_dict(__lowerCamelCase	)
		model.eval()

		# verify our conversion
		lowercase__   :    Optional[int]				=			'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
		lowercase__   :    Union[str, Any]				=			hf_hub_download(repo_id='''nielsr/example-pdf'''						,     repo_type='''dataset'''						,     filename=__lowerCamelCase	)
		lowercase__   :    List[Any]				=			Image.open(__lowerCamelCase	).convert('''RGB'''	)
		lowercase__   :    Any				=			normalize(resize(__lowerCamelCase						,     __lowerCamelCase	)	).unsqueeze(0	)

		lowercase__   :    int				=			model(__lowerCamelCase	)

		if "detection" in checkpoint_url:
				lowercase__   :    List[Any]				=			(1, 15, 3)
				lowercase__   :    Dict				=			torch.tensor(
				    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]	)
				lowercase__   :    Any				=			torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]	)

		else:
				lowercase__   :    Optional[Any]				=			(1, 1_25, 7)
				lowercase__   :    int				=			torch.tensor(
				    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]	)
				lowercase__   :    Tuple				=			torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]	)

		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, :3, :3]						,     __lowerCamelCase						,     atol=1E-4	)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,     __lowerCamelCase						,     atol=1E-4	)
		print('''Looks ok!'''	)

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
				Path(__lowerCamelCase	).mkdir(exist_ok=__lowerCamelCase	)
				model.save_pretrained(__lowerCamelCase	)
				image_processor.save_pretrained(__lowerCamelCase	)

		if push_to_hub:
				# Push model to HF hub
				logger.info('''Pushing model to the hub...'''	)
				lowercase__   :    Optional[int]				=			(
				    '''microsoft/table-transformer-detection'''
				    if '''detection''' in checkpoint_url
				    else '''microsoft/table-transformer-structure-recognition'''
				)
				model.push_to_hub(__lowerCamelCase	)
				image_processor.push_to_hub(__lowerCamelCase	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_url',
							    default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
							    type=str,
							    choices=[
							        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
							        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
							    ],
							    help='URL of the Table Transformer checkpoint you\'d like to convert.',
							)
							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', help='Whether or not to push the converted model to the 🤗 hub.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_table_transformer_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


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      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      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} 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()       )       )




