






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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_					:   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 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					int					,   _a   :					Dict			)	->			int:
     lowerCAmelCase_					:       Any    =		state_dict.pop(_a			)
     lowerCAmelCase_					:       Dict    =		val






def 							_lowerCAmelCase (  _a   :					List[Any]			)	->			Union[str, Any]:
     lowerCAmelCase_					:       List[str]    =		OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               lowerCAmelCase_					:       List[str]    =		key.replace("""backbone.0.body"""					,   """backbone.conv_encoder.model"""			)
               lowerCAmelCase_					:       Union[str, Any]    =		value
          else:
               lowerCAmelCase_					:       int    =		value

     return new_state_dict






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






def 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Dict			)	->			Union[str, Any]:
     lowerCAmelCase_					:       Optional[int]    =		image.size
     lowerCAmelCase_					:       Tuple    =		max(_a					,   _a			)
     lowerCAmelCase_					:       Tuple    =		8_00 if """detection""" in checkpoint_url else 10_00
     lowerCAmelCase_					:       Union[str, Any]    =		target_max_size / current_max_size
     lowerCAmelCase_					:       Tuple    =		image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

     return resized_image






def 							_lowerCAmelCase (  _a   :					List[Any]			)	->			str:
     lowerCAmelCase_					:       List[str]    =		F.to_tensor(_a			)
     lowerCAmelCase_					:       Any    =		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   :					Optional[int]					,   _a   :					Dict					,   _a   :					str			)	->			str:
     logger.info("""Converting model..."""			)

     # load original state dict
     lowerCAmelCase_					:       Optional[int]    =		torch.hub.load_state_dict_from_url(_a					,   map_location="""cpu"""			)
     # rename keys
     for src, dest in rename_keys:
          rename_key(_a					,   _a					,   _a			)
     lowerCAmelCase_					:       Optional[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
     lowerCAmelCase_					:       Tuple    =		"""model."""
     for key in state_dict.copy().keys():
          if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
               lowerCAmelCase_					:       Dict    =		state_dict.pop(_a			)
               lowerCAmelCase_					:       Optional[int]    =		val
    # create HuggingFace model and load state dict
     lowerCAmelCase_					:       int    =		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:
          lowerCAmelCase_					:       List[Any]    =		15
          lowerCAmelCase_					:       Any    =		2
          lowerCAmelCase_					:       Optional[int]    =		{0: """table""", 1: """table rotated"""}
          lowerCAmelCase_					:       List[str]    =		idalabel
          lowerCAmelCase_					:       Optional[Any]    =		{v: k for k, v in idalabel.items()}
     else:
          lowerCAmelCase_					:       int    =		1_25
          lowerCAmelCase_					:       int    =		6
          lowerCAmelCase_					:       Optional[Any]    =		{
              0: """table""",
              1: """table column""",
              2: """table row""",
              3: """table column header""",
              4: """table projected row header""",
              5: """table spanning cell""",
          }
          lowerCAmelCase_					:       int    =		idalabel
          lowerCAmelCase_					:       Union[str, Any]    =		{v: k for k, v in idalabel.items()}

     lowerCAmelCase_					:       str    =		DetrImageProcessor(
         format="""coco_detection"""					,   max_size=8_00 if """detection""" in checkpoint_url else 10_00			)
     lowerCAmelCase_					:       int    =		TableTransformerForObjectDetection(_a			)
     model.load_state_dict(_a			)
     model.eval()

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

     lowerCAmelCase_					:       Optional[int]    =		model(_a			)

     if "detection" in checkpoint_url:
          lowerCAmelCase_					:       List[str]    =		(1, 15, 3)
          lowerCAmelCase_					:       Union[str, Any]    =		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]]			)
          lowerCAmelCase_					:       Union[str, Any]    =		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:
          lowerCAmelCase_					:       Union[str, Any]    =		(1, 1_25, 7)
          lowerCAmelCase_					:       List[str]    =		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]]			)
          lowerCAmelCase_					:       int    =		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..."""			)
          lowerCAmelCase_					:       str    =		(
              """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__":
      UpperCAmelCase_					:   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."""
      )
      UpperCAmelCase_					:   List[Any] 							=						parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
