




'''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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      snake_case__       :   int	      =    state_dict.pop(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Dict	      =    val






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   Union[str, Any]	      =    OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  snake_case__       :   Union[str, Any]	      =    key.replace('backbone.0.body'				,      'backbone.conv_encoder.model'		)
                  snake_case__       :   Tuple	      =    value
            else:
                  snake_case__       :   Optional[Any]	      =    value

      return new_state_dict






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Any:
      snake_case__       :   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)
            snake_case__       :   List[Any]	      =    state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
            snake_case__       :   Dict	      =    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
            snake_case__       :   str	      =    in_proj_weight[:256, :]
            snake_case__       :   Union[str, Any]	      =    in_proj_bias[:256]
            snake_case__       :   Dict	      =    in_proj_weight[256:512, :]
            snake_case__       :   Dict	      =    in_proj_bias[256:512]
            snake_case__       :   List[str]	      =    in_proj_weight[-256:, :]
            snake_case__       :   Tuple	      =    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
            snake_case__       :   int	      =    state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
            snake_case__       :   Optional[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
            snake_case__       :   str	      =    in_proj_weight[:256, :]
            snake_case__       :   Optional[int]	      =    in_proj_bias[:256]
            snake_case__       :   Union[str, Any]	      =    in_proj_weight[256:512, :]
            snake_case__       :   int	      =    in_proj_bias[256:512]
            snake_case__       :   List[Any]	      =    in_proj_weight[-256:, :]
            snake_case__       :   Dict	      =    in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            snake_case__       :   str	      =    state_dict.pop(
                f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
            snake_case__       :   List[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
            snake_case__       :   Optional[int]	      =    in_proj_weight_cross_attn[:256, :]
            snake_case__       :   List[str]	      =    in_proj_bias_cross_attn[:256]
            snake_case__       :   Optional[Any]	      =    in_proj_weight_cross_attn[256:512, :]
            snake_case__       :   Optional[Any]	      =    in_proj_bias_cross_attn[256:512]
            snake_case__       :   Dict	      =    in_proj_weight_cross_attn[-256:, :]
            snake_case__       :   Any	      =    in_proj_bias_cross_attn[-256:]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      snake_case__      ,							snake_case__       :   Tuple	      =    image.size
      snake_case__       :   Any	      =    max(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      snake_case__       :   int	      =    800 if 'detection' in checkpoint_url else 1000
      snake_case__       :   Tuple	      =    target_max_size / current_max_size
      snake_case__       :   Optional[Any]	      =    image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

      return resized_image






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   List[str]	      =    F.to_tensor(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    F.normalize(__SCREAMING_SNAKE_CASE				,      mean=[0.485, 0.456, 0.406]				,      std=[0.229, 0.224, 0.225]		)
      return image






@torch.no_grad()
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      logger.info('Converting model...'		)

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

      snake_case__       :   Dict	      =    DetrImageProcessor(
          format='coco_detection'				,      max_size=800 if 'detection' in checkpoint_url else 1000		)
      snake_case__       :   List[Any]	      =    TableTransformerForObjectDetection(__SCREAMING_SNAKE_CASE		)
      model.load_state_dict(__SCREAMING_SNAKE_CASE		)
      model.eval()

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

      snake_case__       :   Tuple	      =    model(__SCREAMING_SNAKE_CASE		)

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

      else:
            snake_case__       :   Any	      =    (1, 125, 7)
            snake_case__       :   int	      =    torch.tensor(
                [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]		)
            snake_case__       :   Tuple	      =    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]				,      __SCREAMING_SNAKE_CASE				,      atol=1E-4		)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,      __SCREAMING_SNAKE_CASE				,      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(__SCREAMING_SNAKE_CASE		).mkdir(exist_ok=__SCREAMING_SNAKE_CASE		)
            model.save_pretrained(__SCREAMING_SNAKE_CASE		)
            image_processor.save_pretrained(__SCREAMING_SNAKE_CASE		)

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


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)






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

      A_			      =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
