'''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()
SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_:     int  =[]
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_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Any					,    snake_case_       : int			)    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   state_dict.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      List[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_				(					snake_case_       : Optional[int]			)    ->      str:


     '''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[:2_56, :]
          UpperCAmelCase_           =   in_proj_bias[:2_56]
          UpperCAmelCase_           =   in_proj_weight[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight[-2_56:, :]
          UpperCAmelCase_           =   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_           =   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[:2_56, :]
          UpperCAmelCase_           =   in_proj_bias[:2_56]
          UpperCAmelCase_           =   in_proj_weight[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight[-2_56:, :]
          UpperCAmelCase_           =   in_proj_bias[-2_56:]
          # 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[:2_56, :]
          UpperCAmelCase_           =   in_proj_bias_cross_attn[:2_56]
          UpperCAmelCase_           =   in_proj_weight_cross_attn[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias_cross_attn[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight_cross_attn[-2_56:, :]
          UpperCAmelCase_           =   in_proj_bias_cross_attn[-2_56:]





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Optional[int]			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_    , UpperCAmelCase_           =   image.size
     UpperCAmelCase_           =   max(snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   8_00 if "detection" in checkpoint_url else 10_00
     UpperCAmelCase_           =   target_max_size / current_max_size
     UpperCAmelCase_           =   image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

     return resized_image





def 	lowerCAmelCase_				(					snake_case_       : str			)    ->      Any:


     '''simple docstring'''




     UpperCAmelCase_           =   F.to_tensor(snake_case_			)
     UpperCAmelCase_           =   F.normalize(snake_case_					,    mean=[0.485, 0.456, 0.406]					,    std=[0.229, 0.224, 0.225]			)
     return image





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : List[Any]					,    snake_case_       : List[Any]			)    ->      int:


     '''simple docstring'''




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

     # load original state dict
     UpperCAmelCase_           =   torch.hub.load_state_dict_from_url(snake_case_					,    map_location="cpu"			)
     # rename keys
     for src, dest in rename_keys:
          rename_key(snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   rename_backbone_keys(snake_case_			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(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
     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(snake_case_			)
               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_           =   1_25
          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=8_00 if "detection" in checkpoint_url else 10_00			)
     UpperCAmelCase_           =   TableTransformerForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     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=snake_case_			)
     UpperCAmelCase_           =   Image.open(snake_case_			).convert("RGB"			)
     UpperCAmelCase_           =   normalize(resize(snake_case_					,    snake_case_			)			).unsqueeze(0			)

     UpperCAmelCase_           =   model(snake_case_			)

     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, 1_25, 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]					,    snake_case_					,    atol=1E-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,    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(snake_case_			).mkdir(exist_ok=snake_case_			)
          model.save_pretrained(snake_case_			)
          image_processor.save_pretrained(snake_case_			)

     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(snake_case_			)
          image_processor.push_to_hub(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Tuple  =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.'
    )
    SCREAMING_SNAKE_CASE_:     Dict  =parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  DistilBertTokenizer
    a__       :						Any          =  DistilBertTokenizerFast
    a__       :						str          =  True



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




