

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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase													=[]
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					(	_a	:						str     ,   _a	:						int     ,   _a	:						Union[str, Any]				):



      """simple docstring"""






      A				  =		state_dict.pop(_a				)
      A				  =		val



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  A				  =		key.replace("""backbone.0.body"""     ,   """backbone.conv_encoder.model"""				)
                  A				  =		value
            else:
                  A				  =		value

      return new_state_dict



def   _A					(	_a	:						Optional[int]				):



      """simple docstring"""






      A				  =		""""""

      # 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)
            A				  =		state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'				)
            A				  =		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
            A				  =		in_proj_weight[:2_5_6, :]
            A				  =		in_proj_bias[:2_5_6]
            A				  =		in_proj_weight[2_5_6:5_1_2, :]
            A				  =		in_proj_bias[2_5_6:5_1_2]
            A				  =		in_proj_weight[-2_5_6:, :]
            A				  =		in_proj_bias[-2_5_6:]
      # 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
            A				  =		state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'				)
            A				  =		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
            A				  =		in_proj_weight[:2_5_6, :]
            A				  =		in_proj_bias[:2_5_6]
            A				  =		in_proj_weight[2_5_6:5_1_2, :]
            A				  =		in_proj_bias[2_5_6:5_1_2]
            A				  =		in_proj_weight[-2_5_6:, :]
            A				  =		in_proj_bias[-2_5_6:]
            # read in weights + bias of input projection layer of cross-attention
            A				  =		state_dict.pop(
                f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'				)
            A				  =		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
            A				  =		in_proj_weight_cross_attn[:2_5_6, :]
            A				  =		in_proj_bias_cross_attn[:2_5_6]
            A				  =		in_proj_weight_cross_attn[2_5_6:5_1_2, :]
            A				  =		in_proj_bias_cross_attn[2_5_6:5_1_2]
            A				  =		in_proj_weight_cross_attn[-2_5_6:, :]
            A				  =		in_proj_bias_cross_attn[-2_5_6:]



def   _A					(	_a	:						Tuple     ,   _a	:						List[str]				):



      """simple docstring"""






      A  ,			A				  =		image.size
      A				  =		max(_a     ,   _a				)
      A				  =		8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
      A				  =		target_max_size / current_max_size
      A				  =		image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

      return resized_image



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		F.to_tensor(_a				)
      A				  =		F.normalize(_a     ,   mean=[0.4_85, 0.4_56, 0.4_06]     ,   std=[0.2_29, 0.2_24, 0.2_25]				)
      return image



@torch.no_grad()
def   _A					(	_a	:						List[str]     ,   _a	:						Tuple     ,   _a	:						Any				):



      """simple docstring"""






      logger.info("""Converting model..."""				)

      # load original state dict
      A				  =		torch.hub.load_state_dict_from_url(_a     ,   map_location="""cpu"""				)
      # rename keys
      for src, dest in rename_keys:
            rename_key(_a     ,   _a     ,   _a				)
      A				  =		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
      A				  =		"""model."""
      for key in state_dict.copy().keys():
            if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
                  A				  =		state_dict.pop(_a				)
                  A				  =		val
    # create HuggingFace model and load state dict
      A				  =		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:
            A				  =		1_5
            A				  =		2
            A				  =		{0: """table""", 1: """table rotated"""}
            A				  =		idalabel
            A				  =		{v: k for k, v in idalabel.items()}
      else:
            A				  =		1_2_5
            A				  =		6
            A				  =		{
                0: """table""",
                1: """table column""",
                2: """table row""",
                3: """table column header""",
                4: """table projected row header""",
                5: """table spanning cell""",
            }
            A				  =		idalabel
            A				  =		{v: k for k, v in idalabel.items()}

      A				  =		DetrImageProcessor(
          format="""coco_detection"""     ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0				)
      A				  =		TableTransformerForObjectDetection(_a				)
      model.load_state_dict(_a				)
      model.eval()

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

      A				  =		model(_a				)

      if "detection" in checkpoint_url:
            A				  =		(1, 1_5, 3)
            A				  =		torch.tensor(
                [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]				)
            A				  =		torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]				)

      else:
            A				  =		(1, 1_2_5, 7)
            A				  =		torch.tensor(
                [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]				)
            A				  =		torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]				)

      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..."""				)
            A				  =		(
                """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													=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													=parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCAmelCase													=logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      str:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          self.check_model_type(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_    )    ->      List[Any]:
          A  ,			A				  =		{}, {}
          if padding is not None:
                A				  =		padding
          if truncation is not None:
                A				  =		truncation
          if top_k is not None:
                A				  =		top_k
          return preprocess_params, {}, postprocess_params




    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_    )    ->      Dict:
          if isinstance(lowerCamelCase_ ,(Image.Image, str)    ) and isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		{"""image""": image, """question""": question}
          else:
                A				  =		image
          A				  =		super().__call__(lowerCamelCase_ ,**lowerCamelCase_    )
          return results




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=False    )    ->      List[Any]:
          A				  =		load_image(inputs["""image"""]    )
          A				  =		self.tokenizer(
              inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_    )
          A				  =		self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework    )
          model_inputs.update(lowerCamelCase_    )
          return model_inputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		self.model(**lowerCamelCase_    )
          return model_outputs




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=5    )    ->      int:
          if top_k > self.model.config.num_labels:
                A				  =		self.model.config.num_labels

          if self.framework == "pt":
                A				  =		model_outputs.logits.sigmoid()[0]
                A  ,			A				  =		probs.topk(lowerCamelCase_    )
          else:
                raise ValueError(f'Unsupported framework: {self.framework}'    )

          A				  =		scores.tolist()
          A				  =		ids.tolist()
          return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase_ ,lowerCamelCase_    )]




