




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"efficientnet"

     def __init__(  self		,     _a = 3		,     _a = 600		,     _a = 2.0		,     _a = 3.1		,     _a = 8		,     _a = [3, 3, 5, 3, 5, 5, 3]		,     _a = [32, 16, 24, 40, 80, 112, 192]		,     _a = [16, 24, 40, 80, 112, 192, 320]		,     _a = []		,     _a = [1, 2, 2, 2, 1, 2, 1]		,     _a = [1, 2, 2, 3, 3, 4, 1]		,     _a = [1, 6, 6, 6, 6, 6, 6]		,     _a = 0.25		,     _a = "swish"		,     _a = 2560		,     _a = "mean"		,     _a = 0.02		,     _a = 0.001		,     _a = 0.99		,     _a = 0.5		,     _a = 0.2		,     **_a		,     )						->							List[str]:
           super().__init__(**_a				)

           _A							:     int			=						num_channels
           _A							:     Optional[Any]			=						image_size
           _A							:     int			=						width_coefficient
           _A							:     Tuple			=						depth_coefficient
           _A							:     Any			=						depth_divisor
           _A							:     Any			=						kernel_sizes
           _A							:     List[str]			=						in_channels
           _A							:     str			=						out_channels
           _A							:     str			=						depthwise_padding
           _A							:     int			=						strides
           _A							:     Optional[Any]			=						num_block_repeats
           _A							:     Optional[Any]			=						expand_ratios
           _A							:     List[str]			=						squeeze_expansion_ratio
           _A							:     str			=						hidden_act
           _A							:     Tuple			=						hidden_dim
           _A							:     Optional[int]			=						pooling_type
           _A							:     Any			=						initializer_range
           _A							:     Dict			=						batch_norm_eps
           _A							:     Tuple			=						batch_norm_momentum
           _A							:     Union[str, Any]			=						dropout_rate
           _A							:     Union[str, Any]			=						drop_connect_rate
           _A							:     Optional[int]			=						sum(_a				) * 4







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						version.parse("1.11"		)

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ]				)



     @property
     def 					a__	(  self				)						->							float:
           return 1e-5






'''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	:       int      	=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase	:       int      	=	[]
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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  state_dict.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    )       ->	int:






   """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 				__lowerCamelCase				(   A__    )       ->	Dict:






   """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 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  image.size
   UpperCamelCase					=  max(A__    ,						A__    )
   UpperCamelCase					=  800 if 'detection' in checkpoint_url else 1_000
   UpperCamelCase					=  target_max_size / current_max_size
   UpperCamelCase					=  image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

   return resized_image







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  F.to_tensor(A__    )
   UpperCamelCase					=  F.normalize(A__    ,						mean=[0.485, 0.456, 0.406]    ,						std=[0.229, 0.224, 0.225]    )
   return image







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[Any]:






   """simple docstring"""
   logger.info('Converting model...'    )

   # load original state dict
   UpperCamelCase					=  torch.hub.load_state_dict_from_url(A__    ,						map_location='cpu'    )
   # rename keys
   for src, dest in rename_keys:
      rename_key(A__    ,						A__    ,						A__    )
   UpperCamelCase					=  rename_backbone_keys(A__    )
   # query, key and value matrices need special treatment
   read_in_q_k_v(A__    )
   # 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(A__    )
         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 1_000    )
   UpperCamelCase					=  TableTransformerForObjectDetection(A__    )
   model.load_state_dict(A__    )
   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=A__    )
   UpperCamelCase					=  Image.open(A__    ).convert('RGB'    )
   UpperCamelCase					=  normalize(resize(A__    ,						A__    )    ).unsqueeze(0    )

   UpperCamelCase					=  model(A__    )

   if "detection" in checkpoint_url:
      UpperCamelCase					=  (1, 15, 3)
      UpperCamelCase					=  torch.tensor(
          [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]    )
      UpperCamelCase					=  torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]    )

   else:
      UpperCamelCase					=  (1, 125, 7)
      UpperCamelCase					=  torch.tensor(
          [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]    )
      UpperCamelCase					=  torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]    )

   assert outputs.logits.shape == expected_shape
   assert torch.allclose(outputs.logits[0, :3, :3]    ,						A__    ,						atol=1e-4    )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,						A__    ,						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(A__    ).mkdir(exist_ok=A__    )
      model.save_pretrained(A__    )
      image_processor.save_pretrained(A__    )

   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(A__    )
      image_processor.push_to_hub(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	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	:       int      	=	parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

