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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  state_dict.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


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

      return new_state_dict




def        lowerCAmelCase_			(					__A							)					->    Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  ""

      # 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__								=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
            UpperCAmelCase__								=  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__								=  in_proj_weight[:256, :]
            UpperCAmelCase__								=  in_proj_bias[:256]
            UpperCAmelCase__								=  in_proj_weight[256:512, :]
            UpperCAmelCase__								=  in_proj_bias[256:512]
            UpperCAmelCase__								=  in_proj_weight[-256:, :]
            UpperCAmelCase__								=  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__								=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
            UpperCAmelCase__								=  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__								=  in_proj_weight[:256, :]
            UpperCAmelCase__								=  in_proj_bias[:256]
            UpperCAmelCase__								=  in_proj_weight[256:512, :]
            UpperCAmelCase__								=  in_proj_bias[256:512]
            UpperCAmelCase__								=  in_proj_weight[-256:, :]
            UpperCAmelCase__								=  in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCAmelCase__								=  state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
            UpperCAmelCase__								=  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__								=  in_proj_weight_cross_attn[:256, :]
            UpperCAmelCase__								=  in_proj_bias_cross_attn[:256]
            UpperCAmelCase__								=  in_proj_weight_cross_attn[256:512, :]
            UpperCAmelCase__								=  in_proj_bias_cross_attn[256:512]
            UpperCAmelCase__								=  in_proj_weight_cross_attn[-256:, :]
            UpperCAmelCase__								=  in_proj_bias_cross_attn[-256:]




def        lowerCAmelCase_			(					__A,  __A							)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__		,				UpperCAmelCase__								=  image.size
      UpperCAmelCase__								=  max(__A,  __A							)
      UpperCAmelCase__								=  800 if "detection" in checkpoint_url else 1_000
      UpperCAmelCase__								=  target_max_size / current_max_size
      UpperCAmelCase__								=  image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

      return resized_image




def        lowerCAmelCase_			(					__A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  F.to_tensor(__A							)
      UpperCAmelCase__								=  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,  __A,  __A							)					->    int:







      '''simple docstring'''


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

      # load original state dict
      UpperCAmelCase__								=  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__								=  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__								=  "model."
      for key in state_dict.copy().keys():
            if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                  UpperCAmelCase__								=  state_dict.pop(__A							)
                  UpperCAmelCase__								=  val
    # create HuggingFace model and load state dict
      UpperCAmelCase__								=  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__								=  15
            UpperCAmelCase__								=  2
            UpperCAmelCase__								=  {0: "table", 1: "table rotated"}
            UpperCAmelCase__								=  idalabel
            UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
      else:
            UpperCAmelCase__								=  125
            UpperCAmelCase__								=  6
            UpperCAmelCase__								=  {
                0: "table",
                1: "table column",
                2: "table row",
                3: "table column header",
                4: "table projected row header",
                5: "table spanning cell",
            }
            UpperCAmelCase__								=  idalabel
            UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}

      UpperCAmelCase__								=  DetrImageProcessor(
          format="coco_detection",  max_size=800 if "detection" in checkpoint_url else 1_000							)
      UpperCAmelCase__								=  TableTransformerForObjectDetection(__A							)
      model.load_state_dict(__A							)
      model.eval()

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

      UpperCAmelCase__								=  model(__A							)

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

      else:
            UpperCAmelCase__								=  (1, 125, 7)
            UpperCAmelCase__								=  torch.tensor(
                [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]							)
            UpperCAmelCase__								=  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__								=  (
                "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)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



