





'''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()
_A:					str							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A:					Optional[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     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
 __UpperCAmelCase					=   state_dict.pop(_lowerCAmelCase    )
 __UpperCAmelCase					=   val







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
 __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     (      _lowerCAmelCase    )->     Tuple:
 __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     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					,    __UpperCAmelCase					=   image.size
 __UpperCAmelCase					=   max(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __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     (      _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   F.to_tensor(_lowerCAmelCase    )
 __UpperCAmelCase					=   F.normalize(_lowerCAmelCase       ,    mean=[0.485, 0.456, 0.406]       ,    std=[0.229, 0.224, 0.225]    )
 return image







@torch.no_grad()
def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 logger.info('Converting model...'    )

 # load original state dict
 __UpperCAmelCase					=   torch.hub.load_state_dict_from_url(_lowerCAmelCase       ,    map_location='cpu'    )
 # rename keys
 for src, dest in rename_keys:
  rename_key(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   rename_backbone_keys(_lowerCAmelCase    )
 # query, key and value matrices need special treatment
 read_in_q_k_v(_lowerCAmelCase    )
 # 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(_lowerCAmelCase    )
   __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(_lowerCAmelCase    )
 model.load_state_dict(_lowerCAmelCase    )
 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=_lowerCAmelCase    )
 __UpperCAmelCase					=   Image.open(_lowerCAmelCase    ).convert('RGB'    )
 __UpperCAmelCase					=   normalize(resize(_lowerCAmelCase       ,    _lowerCAmelCase    )    ).unsqueeze(0    )

 __UpperCAmelCase					=   model(_lowerCAmelCase    )

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

 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(_lowerCAmelCase    )
  image_processor.push_to_hub(_lowerCAmelCase    )


if __name__ == "__main__":
    _A:					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."""
    )
    _A:					str							=				parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 0:
      raise ValueError(
          'When using Ray for distributed fine-tuning, '
          'you\'ll need to provide the paths instead, '
          'as the dataset and the index are loaded '
          'separately. More info in examples/rag/use_own_knowledge_dataset.py '					)
     super().__init__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



    def 					__lowerCamelCase						(  self					):
     logger.info('initializing retrieval'					)

     if len(self.retrieval_workers					) > 0:
      ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
     else:
      # Non-distributed training. Load index into this same process.
      self.index.init_index()



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if len(self.retrieval_workers					) > 0:
      # Select a random retrieval actor.
      __UpperCAmelCase					=   self.retrieval_workers[random.randint(0     ,			len(self.retrieval_workers					) - 1					)]
      __UpperCAmelCase					,    __UpperCAmelCase					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





