
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__		:     Optional[Any]     			=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__		:     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__							(					a		,		a		,		a      )      ->   Union[str, Any]:
   _A:  Any					=       state_dict.pop(a      )
   _A:  List[Any]					=       val






def   lowerCamelCase__							(					a      )      ->   str:
   _A:  Optional[int]					=       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _A:  str					=       key.replace('''backbone.0.body'''		,		'''backbone.conv_encoder.model'''      )
         _A:  Tuple					=       value
      else:
         _A:  int					=       value

   return new_state_dict






def   lowerCamelCase__							(					a      )      ->   int:
   _A:  Dict					=       ''''''

   # 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:  Optional[int]					=       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
      _A:  Optional[int]					=       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:  str					=       in_proj_weight[:2_56, :]
      _A:  Any					=       in_proj_bias[:2_56]
      _A:  Optional[int]					=       in_proj_weight[2_56:5_12, :]
      _A:  Optional[Any]					=       in_proj_bias[2_56:5_12]
      _A:  Union[str, Any]					=       in_proj_weight[-2_56:, :]
      _A:  Union[str, Any]					=       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
      _A:  List[Any]					=       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
      _A:  int					=       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:  Dict					=       in_proj_weight[:2_56, :]
      _A:  int					=       in_proj_bias[:2_56]
      _A:  Union[str, Any]					=       in_proj_weight[2_56:5_12, :]
      _A:  Optional[int]					=       in_proj_bias[2_56:5_12]
      _A:  Tuple					=       in_proj_weight[-2_56:, :]
      _A:  List[Any]					=       in_proj_bias[-2_56:]
      # read in weights + bias of input projection layer of cross-attention
      _A:  List[Any]					=       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""      )
      _A:  int					=       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:  Tuple					=       in_proj_weight_cross_attn[:2_56, :]
      _A:  Optional[int]					=       in_proj_bias_cross_attn[:2_56]
      _A:  Union[str, Any]					=       in_proj_weight_cross_attn[2_56:5_12, :]
      _A:  List[str]					=       in_proj_bias_cross_attn[2_56:5_12]
      _A:  str					=       in_proj_weight_cross_attn[-2_56:, :]
      _A:  Optional[int]					=       in_proj_bias_cross_attn[-2_56:]






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   _A			,  _A:  Tuple					=       image.size
   _A:  int					=       max(a		,		a      )
   _A:  Optional[int]					=       8_00 if '''detection''' in checkpoint_url else 10_00
   _A:  Optional[Any]					=       target_max_size / current_max_size
   _A:  List[Any]					=       image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

   return resized_image






def   lowerCamelCase__							(					a      )      ->   Optional[Any]:
   _A:  int					=       F.to_tensor(a      )
   _A:  List[str]					=       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      )      ->   List[str]:
   logger.info('''Converting model...'''      )

   # load original state dict
   _A:  Optional[int]					=       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:  Union[str, Any]					=       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:  Any					=       '''model.'''
   for key in state_dict.copy().keys():
      if not key.startswith('''class_labels_classifier'''      ) and not key.startswith('''bbox_predictor'''      ):
         _A:  List[str]					=       state_dict.pop(a      )
         _A:  Optional[Any]					=       val
    # create HuggingFace model and load state dict
   _A:  Optional[int]					=       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:  Union[str, Any]					=       15
      _A:  Dict					=       2
      _A:  Tuple					=       {0: '''table''', 1: '''table rotated'''}
      _A:  int					=       idalabel
      _A:  Any					=       {v: k for k, v in idalabel.items()}
   else:
      _A:  List[str]					=       1_25
      _A:  Optional[int]					=       6
      _A:  Tuple					=       {
          0: '''table''',
          1: '''table column''',
          2: '''table row''',
          3: '''table column header''',
          4: '''table projected row header''',
          5: '''table spanning cell''',
      }
      _A:  Union[str, Any]					=       idalabel
      _A:  Optional[int]					=       {v: k for k, v in idalabel.items()}

   _A:  Union[str, Any]					=       DetrImageProcessor(
       format='''coco_detection'''		,		max_size=8_00 if '''detection''' in checkpoint_url else 10_00      )
   _A:  Dict					=       TableTransformerForObjectDetection(a      )
   model.load_state_dict(a      )
   model.eval()

   # verify our conversion
   _A:  Any					=       '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
   _A:  str					=       hf_hub_download(repo_id='''nielsr/example-pdf'''		,		repo_type='''dataset'''		,		filename=a      )
   _A:  Optional[int]					=       Image.open(a      ).convert('''RGB'''      )
   _A:  Any					=       normalize(resize(a		,		a      )      ).unsqueeze(0      )

   _A:  Optional[Any]					=       model(a      )

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

   else:
      _A:  List[Any]					=       (1, 1_25, 7)
      _A:  Union[str, Any]					=       torch.tensor(
          [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
      _A:  Optional[Any]					=       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...'''      )
      _A:  Any					=       (
          '''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__		:     List[Any]     			=      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__		:     Any     			=      parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase__		:     List[Any]     			=      logging.get_logger(__name__)
logging.set_verbosity_info()






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if "xprophetnet" in prophetnet_checkpoint_path:
      _A:  List[Any]					=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Union[str, Any]					=       XLMProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )
   else:
      _A:  Dict					=       ProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Tuple					=       ProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )

   _A:  Optional[int]					=       ['''key_proj''', '''value_proj''', '''query_proj''']

   _A:  List[Any]					=       {
       '''self_attn''': '''ngram_self_attn''',
       '''cross_attn''': '''encoder_attn''',
       '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
       '''feed_forward_layer_norm''': '''final_layer_norm''',
       '''feed_forward''': '''''',
       '''intermediate''': '''fc1''',
       '''output''': '''fc2''',
       '''key_proj''': '''k_proj''',
       '''query_proj''': '''q_proj''',
       '''value_proj''': '''v_proj''',
       '''word_embeddings''': '''embed_tokens''',
       '''embeddings_layer_norm''': '''emb_layer_norm''',
       '''relative_pos_embeddings''': '''relative_linear''',
       '''ngram_embeddings''': '''ngram_input_embed''',
       '''position_embeddings''': '''embed_positions''',
   }

   for key in loading_info["missing_keys"]:
      _A:  List[str]					=       key.split('''.'''      )

      if attributes[0] == "lm_head":
         _A:  Optional[int]					=       prophet
         _A:  Tuple					=       prophet_old
      else:
         _A:  Tuple					=       prophet.prophetnet
         _A:  Any					=       prophet_old.model

      _A:  int					=       False
      for attribute in attributes:
         if attribute in mapping:
            _A:  Optional[int]					=       mapping[attribute]
            if not hasattr(a		,		a      ) and len(a      ) > 0:
               _A:  int					=       attribute
         elif hasattr(a		,		a      ):
            _A:  Tuple					=       attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _A:  Union[str, Any]					=       old_model.weight
            logger.info(f"""{attribute} is initialized."""      )
            _A:  Any					=       True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _A:  str					=       old_model.bias
            logger.info(f"""{attribute} is initialized"""      )
            _A:  Dict					=       True
            break
         elif attribute in special_keys and hasattr(a		,		'''in_proj_weight'''      ):
            _A:  Optional[int]					=       old_model.in_proj_weight.shape[0] // 3
            _A:  Tuple					=       getattr(a		,		a      )
            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
            if attribute == "query_proj":
               _A:  List[str]					=       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

            elif attribute == "key_proj":
               _A:  int					=       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _A:  Optional[int]					=       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _A:  int					=       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _A:  Tuple					=       True
            break
         elif attribute == "position_embeddings":
            assert (
                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
            ), "Hidden size has to match"
            assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
            _A:  Union[str, Any]					=       nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
            _A:  List[Any]					=       True
            break

         if attribute.isdigit():
            _A:  Tuple					=       model[int(a      )]
            _A:  int					=       old_model[int(a      )]
         else:
            _A:  Union[str, Any]					=       getattr(a		,		a      )

            if old_attribute == "":
               _A:  Union[str, Any]					=       old_model
            else:
               if not hasattr(a		,		a      ):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
               _A:  List[Any]					=       getattr(a		,		a      )

      if not is_key_init:
         raise ValueError(f"""{key} was not correctly initialized!"""      )

   print(f"""Saving model to {pytorch_dump_folder_path}"""      )
   prophet.save_pretrained(a      )


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   UpperCAmelCase__		:     Tuple     			=      parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



