





'''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()
lowercase__						:      Any	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:      Dict	=    []
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  a__    (    lowercase :					int, lowercase :					str, lowercase :					int				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	state_dict.pop(_UpperCAmelCase				)
     _UpperCamelCase       =	val







def  a__    (    lowercase :					Optional[int]				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _UpperCamelCase       =	key.replace('''backbone.0.body''', '''backbone.conv_encoder.model'''				)
               _UpperCamelCase       =	value
          else:
               _UpperCamelCase       =	value

     return new_state_dict







def  a__    (    lowercase :					Optional[Any]				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	""

     # 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)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	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
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]
     # 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
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	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
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          _UpperCamelCase       =	state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
          _UpperCamelCase       =	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
          _UpperCamelCase       =	in_proj_weight_cross_attn[:256, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[:256]
          _UpperCamelCase       =	in_proj_weight_cross_attn[256:512, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[256:512]
          _UpperCamelCase       =	in_proj_weight_cross_attn[-256:, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[-256:]







def  a__    (    lowercase :					Dict, lowercase :					Tuple				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	image.size
     _UpperCamelCase       =	max(_UpperCAmelCase, _UpperCAmelCase				)
     _UpperCamelCase       =	800 if "detection" in checkpoint_url else 1000
     _UpperCamelCase       =	target_max_size / current_max_size
     _UpperCamelCase       =	image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

     return resized_image







def  a__    (    lowercase :					Optional[int]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	F.to_tensor(_UpperCAmelCase				)
     _UpperCamelCase       =	F.normalize(_UpperCAmelCase, 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  a__    (    lowercase :					Tuple, lowercase :					List[str], lowercase :					Dict				)		->		List[str]:





     """simple docstring"""

     logger.info('''Converting model...'''				)

     # load original state dict
     _UpperCamelCase       =	torch.hub.load_state_dict_from_url(_UpperCAmelCase, map_location='''cpu'''				)
     # rename keys
     for src, dest in rename_keys:
          rename_key(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase				)
     _UpperCamelCase       =	rename_backbone_keys(_UpperCAmelCase				)
     # query, key and value matrices need special treatment
     read_in_q_k_v(_UpperCAmelCase				)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _UpperCamelCase       =	"model."
     for key in state_dict.copy().keys():
          if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
               _UpperCamelCase       =	state_dict.pop(_UpperCAmelCase				)
               _UpperCamelCase       =	val
    # create HuggingFace model and load state dict
     _UpperCamelCase       =	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:
          _UpperCamelCase       =	15
          _UpperCamelCase       =	2
          _UpperCamelCase       =	{0: "table", 1: "table rotated"}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}
     else:
          _UpperCamelCase       =	125
          _UpperCamelCase       =	6
          _UpperCamelCase       =	{
              0: "table",
              1: "table column",
              2: "table row",
              3: "table column header",
              4: "table projected row header",
              5: "table spanning cell",
          }
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}

     _UpperCamelCase       =	DetrImageProcessor(
         format='''coco_detection''', max_size=800 if '''detection''' in checkpoint_url else 1000				)
     _UpperCamelCase       =	TableTransformerForObjectDetection(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # verify our conversion
     _UpperCamelCase       =	"example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
     _UpperCamelCase       =	hf_hub_download(repo_id='''nielsr/example-pdf''', repo_type='''dataset''', filename=_UpperCAmelCase				)
     _UpperCamelCase       =	Image.open(_UpperCAmelCase				).convert('''RGB'''				)
     _UpperCamelCase       =	normalize(resize(_UpperCAmelCase, _UpperCAmelCase				)				).unsqueeze(0				)

     _UpperCamelCase       =	model(_UpperCAmelCase				)

     if "detection" in checkpoint_url:
          _UpperCamelCase       =	(1, 15, 3)
          _UpperCamelCase       =	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]]				)
          _UpperCamelCase       =	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:
          _UpperCamelCase       =	(1, 125, 7)
          _UpperCamelCase       =	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]]				)
          _UpperCamelCase       =	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], _UpperCAmelCase, atol=1e-4				)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3], _UpperCAmelCase, 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(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
          model.save_pretrained(_UpperCAmelCase				)
          image_processor.save_pretrained(_UpperCAmelCase				)

     if push_to_hub:
          # Push model to HF hub
          logger.info('''Pushing model to the hub...'''				)
          _UpperCamelCase       =	(
              "microsoft/table-transformer-detection"
              if "detection" in checkpoint_url
              else "microsoft/table-transformer-structure-recognition"
          )
          model.push_to_hub(_UpperCAmelCase				)
          image_processor.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
   lowercase__						:      Optional[int]	=    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.'
   )
   lowercase__						:      Optional[Any]	=    parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)













'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

lowercase__						:      List[Any]	=    {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   'biogpt'





       def __init__(							self       :		Optional[Any]	,			lowerCAmelCase__       :		List[str]=42384	,			lowerCAmelCase__       :		Optional[int]=1024	,			lowerCAmelCase__       :		List[str]=24	,			lowerCAmelCase__       :		List[Any]=16	,			lowerCAmelCase__       :		Optional[int]=4096	,			lowerCAmelCase__       :		Optional[int]="gelu"	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		Optional[int]=0.1	,			lowerCAmelCase__       :		Union[str, Any]=1024	,			lowerCAmelCase__       :		List[str]=0.02	,			lowerCAmelCase__       :		Tuple=1e-1_2	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Union[str, Any]=0.0	,			lowerCAmelCase__       :		Optional[int]=1	,			lowerCAmelCase__       :		Union[str, Any]=0	,			lowerCAmelCase__       :		Optional[Any]=2	,			**lowerCAmelCase__       :		Optional[Any]	,			)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	scale_embedding
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	layerdrop
            _UpperCamelCase       =	activation_dropout
            super().__init__(pad_token_id=lowerCAmelCase__	,			bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			**lowerCAmelCase__					)



