'''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()
snake_case__					:      Union[str, Any]				      =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__					:      str				      =  []
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   (				lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Any     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       int       =				state_dict.pop(lowerCAmelCase__       )
     UpperCAmelCase_							:       List[Any]       =				val


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_							:       List[Any]       =				key.replace('backbone.0.body'     ,					'backbone.conv_encoder.model'       )
               UpperCAmelCase_							:       str       =				value
          else:
               UpperCAmelCase_							:       List[str]       =				value

     return new_state_dict


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       str       =				""""""

     # 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_							:       Tuple       =				state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
          UpperCAmelCase_							:       int       =				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_							:       Union[str, Any]       =				in_proj_weight[:256, :]
          UpperCAmelCase_							:       Optional[Any]       =				in_proj_bias[:256]
          UpperCAmelCase_							:       Optional[int]       =				in_proj_weight[256:512, :]
          UpperCAmelCase_							:       Optional[Any]       =				in_proj_bias[256:512]
          UpperCAmelCase_							:       List[Any]       =				in_proj_weight[-256:, :]
          UpperCAmelCase_							:       Optional[Any]       =				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_							:       Tuple       =				state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''       )
          UpperCAmelCase_							:       Optional[int]       =				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_							:       Tuple       =				in_proj_weight[:256, :]
          UpperCAmelCase_							:       Optional[int]       =				in_proj_bias[:256]
          UpperCAmelCase_							:       str       =				in_proj_weight[256:512, :]
          UpperCAmelCase_							:       str       =				in_proj_bias[256:512]
          UpperCAmelCase_							:       Optional[int]       =				in_proj_weight[-256:, :]
          UpperCAmelCase_							:       List[Any]       =				in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCAmelCase_							:       List[str]       =				state_dict.pop(
              F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''       )
          UpperCAmelCase_							:       Optional[int]       =				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_							:       Optional[Any]       =				in_proj_weight_cross_attn[:256, :]
          UpperCAmelCase_							:       List[Any]       =				in_proj_bias_cross_attn[:256]
          UpperCAmelCase_							:       List[Any]       =				in_proj_weight_cross_attn[256:512, :]
          UpperCAmelCase_							:       List[Any]       =				in_proj_bias_cross_attn[256:512]
          UpperCAmelCase_							:       Any       =				in_proj_weight_cross_attn[-256:, :]
          UpperCAmelCase_							:       Optional[Any]       =				in_proj_bias_cross_attn[-256:]


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				image.size
     UpperCAmelCase_							:       Optional[Any]       =				max(lowerCAmelCase__     ,					lowerCAmelCase__       )
     UpperCAmelCase_							:       Dict       =				800 if """detection""" in checkpoint_url else 1000
     UpperCAmelCase_							:       Optional[int]       =				target_max_size / current_max_size
     UpperCAmelCase_							:       List[str]       =				image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

     return resized_image


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				F.to_tensor(lowerCAmelCase__       )
     UpperCAmelCase_							:       str       =				F.normalize(lowerCAmelCase__     ,					mean=[0.485, 0.456, 0.406]     ,					std=[0.229, 0.224, 0.225]       )
     return image


@torch.no_grad()
def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





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

     # load original state dict
     UpperCAmelCase_							:       Any       =				torch.hub.load_state_dict_from_url(lowerCAmelCase__     ,					map_location='cpu'       )
     # rename keys
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase__     ,					lowerCAmelCase__     ,					lowerCAmelCase__       )
     UpperCAmelCase_							:       Tuple       =				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
     UpperCAmelCase_							:       Tuple       =				"""model."""
     for key in state_dict.copy().keys():
          if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
               UpperCAmelCase_							:       Union[str, Any]       =				state_dict.pop(lowerCAmelCase__       )
               UpperCAmelCase_							:       Dict       =				val
    # create HuggingFace model and load state dict
     UpperCAmelCase_							:       Union[str, Any]       =				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_							:       List[str]       =				15
          UpperCAmelCase_							:       Union[str, Any]       =				2
          UpperCAmelCase_							:       str       =				{0: """table""", 1: """table rotated"""}
          UpperCAmelCase_							:       List[Any]       =				idalabel
          UpperCAmelCase_							:       int       =				{v: k for k, v in idalabel.items()}
     else:
          UpperCAmelCase_							:       Optional[Any]       =				125
          UpperCAmelCase_							:       Any       =				6
          UpperCAmelCase_							:       Dict       =				{
              0: """table""",
              1: """table column""",
              2: """table row""",
              3: """table column header""",
              4: """table projected row header""",
              5: """table spanning cell""",
          }
          UpperCAmelCase_							:       Any       =				idalabel
          UpperCAmelCase_							:       int       =				{v: k for k, v in idalabel.items()}

     UpperCAmelCase_							:       str       =				DetrImageProcessor(
         format='coco_detection'     ,					max_size=800 if 'detection' in checkpoint_url else 1000       )
     UpperCAmelCase_							:       Optional[Any]       =				TableTransformerForObjectDetection(lowerCAmelCase__       )
     model.load_state_dict(lowerCAmelCase__       )
     model.eval()

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

     UpperCAmelCase_							:       Dict       =				model(lowerCAmelCase__       )

     if "detection" in checkpoint_url:
          UpperCAmelCase_							:       Optional[int]       =				(1, 15, 3)
          UpperCAmelCase_							:       Optional[int]       =				torch.tensor(
              [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]       )
          UpperCAmelCase_							:       str       =				torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]       )

     else:
          UpperCAmelCase_							:       List[str]       =				(1, 125, 7)
          UpperCAmelCase_							:       Dict       =				torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
          UpperCAmelCase_							:       Any       =				torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]       )

     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...'       )
          UpperCAmelCase_							:       Tuple       =				(
              """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__":
  snake_case__					:      List[Any]				      =  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.'''
  )
  snake_case__					:      Optional[Any]				      =  parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__					:      Dict				      =  {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		'''albert'''


  def __init__(			self				,		snake_case_=3_0_0_0_0				,		snake_case_=1_2_8				,		snake_case_=4_0_9_6				,		snake_case_=1_2				,		snake_case_=1				,		snake_case_=6_4				,		snake_case_=1_6_3_8_4				,		snake_case_=1				,		snake_case_="gelu_new"				,		snake_case_=0				,		snake_case_=0				,		snake_case_=5_1_2				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=1E-12				,		snake_case_=0.1				,		snake_case_="absolute"				,		snake_case_=0				,		snake_case_=2				,		snake_case_=3				,		**snake_case_				,		):




       '''simple docstring'''

       super().__init__(pad_token_id=snake_case_				,		bos_token_id=snake_case_				,		eos_token_id=snake_case_				,		**snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				vocab_size
       UpperCAmelCase_							:       Dict       =				embedding_size
       UpperCAmelCase_							:       str       =				hidden_size
       UpperCAmelCase_							:       Any       =				num_hidden_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_hidden_groups
       UpperCAmelCase_							:       List[str]       =				num_attention_heads
       UpperCAmelCase_							:       Any       =				inner_group_num
       UpperCAmelCase_							:       Optional[int]       =				hidden_act
       UpperCAmelCase_							:       Tuple       =				intermediate_size
       UpperCAmelCase_							:       Any       =				hidden_dropout_prob
       UpperCAmelCase_							:       List[str]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Union[str, Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Dict       =				type_vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				initializer_range
       UpperCAmelCase_							:       Optional[Any]       =				layer_norm_eps
       UpperCAmelCase_							:       Dict       =				classifier_dropout_prob
       UpperCAmelCase_							:       Tuple       =				position_embedding_type







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.task == "multiple-choice":
            UpperCAmelCase_							:       Optional[Any]       =				{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            UpperCAmelCase_							:       List[Any]       =				{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]						)






