

"""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()
_A						 =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A						 =   []
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__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      int:
      UpperCAmelCase__       :				int						 =  state_dict.pop(__a  )
      UpperCAmelCase__       :				List[str]						 =  val




def  a__		(		lowerCAmelCase  )     ->      Optional[int]:
      UpperCAmelCase__       :				Any						 =  OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  UpperCAmelCase__       :				int						 =  key.replace("""backbone.0.body"""       ,     """backbone.conv_encoder.model"""  )
                  UpperCAmelCase__       :				List[str]						 =  value
            else:
                  UpperCAmelCase__       :				int						 =  value

      return new_state_dict




def  a__		(		lowerCAmelCase  )     ->      Tuple:
      UpperCAmelCase__       :				int						 =  ''''''

      # 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__       :				Union[str, Any]						 =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""  )
            UpperCAmelCase__       :				Tuple						 =  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__       :				Tuple						 =  in_proj_weight[:2_56, :]
            UpperCAmelCase__       :				str						 =  in_proj_bias[:2_56]
            UpperCAmelCase__       :				List[Any]						 =  in_proj_weight[2_56:5_12, :]
            UpperCAmelCase__       :				Union[str, Any]						 =  in_proj_bias[2_56:5_12]
            UpperCAmelCase__       :				Any						 =  in_proj_weight[-2_56:, :]
            UpperCAmelCase__       :				int						 =  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
            UpperCAmelCase__       :				List[str]						 =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
            UpperCAmelCase__       :				List[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
            UpperCAmelCase__       :				Any						 =  in_proj_weight[:2_56, :]
            UpperCAmelCase__       :				List[Any]						 =  in_proj_bias[:2_56]
            UpperCAmelCase__       :				Optional[int]						 =  in_proj_weight[2_56:5_12, :]
            UpperCAmelCase__       :				Tuple						 =  in_proj_bias[2_56:5_12]
            UpperCAmelCase__       :				Tuple						 =  in_proj_weight[-2_56:, :]
            UpperCAmelCase__       :				str						 =  in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCAmelCase__       :				Any						 =  state_dict.pop(
                F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""  )
            UpperCAmelCase__       :				str						 =  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__       :				Any						 =  in_proj_weight_cross_attn[:2_56, :]
            UpperCAmelCase__       :				Any						 =  in_proj_bias_cross_attn[:2_56]
            UpperCAmelCase__       :				List[str]						 =  in_proj_weight_cross_attn[2_56:5_12, :]
            UpperCAmelCase__       :				Optional[Any]						 =  in_proj_bias_cross_attn[2_56:5_12]
            UpperCAmelCase__       :				Union[str, Any]						 =  in_proj_weight_cross_attn[-2_56:, :]
            UpperCAmelCase__       :				List[str]						 =  in_proj_bias_cross_attn[-2_56:]




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

      return resized_image




def  a__		(		lowerCAmelCase  )     ->      Dict:
      UpperCAmelCase__       :				Optional[int]						 =  F.to_tensor(__a  )
      UpperCAmelCase__       :				Union[str, Any]						 =  F.normalize(__a       ,     mean=[0.485, 0.456, 0.406]       ,     std=[0.229, 0.224, 0.225]  )
      return image




@torch.no_grad()
def  a__		(		lowerCAmelCase       ,     lowerCAmelCase       ,     lowerCAmelCase  )     ->      Any:
      logger.info("""Converting model..."""  )

      # load original state dict
      UpperCAmelCase__       :				List[Any]						 =  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__       :				List[Any]						 =  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__       :				int						 =  '''model.'''
      for key in state_dict.copy().keys():
            if not key.startswith("""class_labels_classifier"""  ) and not key.startswith("""bbox_predictor"""  ):
                  UpperCAmelCase__       :				Dict						 =  state_dict.pop(__a  )
                  UpperCAmelCase__       :				int						 =  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__       :				int						 =  15
            UpperCAmelCase__       :				int						 =  2
            UpperCAmelCase__       :				Union[str, Any]						 =  {0: '''table''', 1: '''table rotated'''}
            UpperCAmelCase__       :				Optional[int]						 =  idalabel
            UpperCAmelCase__       :				Optional[int]						 =  {v: k for k, v in idalabel.items()}
      else:
            UpperCAmelCase__       :				Any						 =  1_25
            UpperCAmelCase__       :				Union[str, Any]						 =  6
            UpperCAmelCase__       :				List[str]						 =  {
                0: '''table''',
                1: '''table column''',
                2: '''table row''',
                3: '''table column header''',
                4: '''table projected row header''',
                5: '''table spanning cell''',
            }
            UpperCAmelCase__       :				Union[str, Any]						 =  idalabel
            UpperCAmelCase__       :				Dict						 =  {v: k for k, v in idalabel.items()}

      UpperCAmelCase__       :				List[Any]						 =  DetrImageProcessor(
          format="""coco_detection"""       ,     max_size=8_00 if """detection""" in checkpoint_url else 10_00  )
      UpperCAmelCase__       :				List[Any]						 =  TableTransformerForObjectDetection(__a  )
      model.load_state_dict(__a  )
      model.eval()

      # verify our conversion
      UpperCAmelCase__       :				Union[str, Any]						 =  '''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=__a  )
      UpperCAmelCase__       :				List[Any]						 =  Image.open(__a  ).convert("""RGB"""  )
      UpperCAmelCase__       :				str						 =  normalize(resize(__a       ,     __a  )  ).unsqueeze(0  )

      UpperCAmelCase__       :				Optional[int]						 =  model(__a  )

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

      else:
            UpperCAmelCase__       :				Optional[Any]						 =  (1, 1_25, 7)
            UpperCAmelCase__       :				Optional[Any]						 =  torch.tensor(
                [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]  )
            UpperCAmelCase__       :				Dict						 =  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]       ,     __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__       :				Tuple						 =  (
                '''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__":
    _A						 =   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."""
    )
    _A						 =   parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    int         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    int         =		kwargs.pop('''feature_extractor'''       )

												_a	:    int         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[Any]:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    str         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[int]:
												return self.tokenizer.decode(*_a       ,							**_a       )




							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



