


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()
__snake_case									=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case									=		[]
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						(						_lowercase ,   _lowercase ,   _lowercase      ) ->      str:


     """simple docstring"""


     __UpperCamelCase      =       state_dict.pop(__A      )
     __UpperCamelCase      =       val






def 						_A						(						_lowercase      ) ->      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 						_A						(						_lowercase      ) ->      List[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 						_A						(						_lowercase ,   _lowercase      ) ->      Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase,  __UpperCamelCase      =       image.size
     __UpperCamelCase      =       max(__A ,   __A      )
     __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 						_A						(						_lowercase      ) ->      str:


     """simple docstring"""


     __UpperCamelCase      =       F.to_tensor(__A      )
     __UpperCamelCase      =       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						(						_lowercase ,   _lowercase ,   _lowercase      ) ->      Any:


     """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      =       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(__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(
              [[-6.78_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]]      )
          __UpperCamelCase      =       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:
          __UpperCamelCase      =       (1, 1_25, 7)
          __UpperCamelCase      =       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]]      )
          __UpperCamelCase      =       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...'      )
          __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__":
    __snake_case									=		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.'''
    )
    __snake_case									=		parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

