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  :      List[str]            =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a  :      Union[str, Any]            =    []
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 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	state_dict.pop(lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	val
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         lowerCAmelCase_:							Optional[Any]       =	key.replace("backbone.0.body"							,				"backbone.conv_encoder.model"					)
         lowerCAmelCase_:							Any       =	value
      else:
         lowerCAmelCase_:							List[str]       =	value

   return new_state_dict
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[Any]       =	""

   # 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)
      lowerCAmelCase_:							List[str]       =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
      lowerCAmelCase_:							Optional[Any]       =	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
      lowerCAmelCase_:							int       =	in_proj_weight[:256, :]
      lowerCAmelCase_:							Optional[int]       =	in_proj_bias[:256]
      lowerCAmelCase_:							str       =	in_proj_weight[256:512, :]
      lowerCAmelCase_:							str       =	in_proj_bias[256:512]
      lowerCAmelCase_:							Any       =	in_proj_weight[-256:, :]
      lowerCAmelCase_:							List[str]       =	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
      lowerCAmelCase_:							int       =	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
      lowerCAmelCase_:							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
      lowerCAmelCase_:							Any       =	in_proj_weight[:256, :]
      lowerCAmelCase_:							str       =	in_proj_bias[:256]
      lowerCAmelCase_:							str       =	in_proj_weight[256:512, :]
      lowerCAmelCase_:							Optional[Any]       =	in_proj_bias[256:512]
      lowerCAmelCase_:							List[str]       =	in_proj_weight[-256:, :]
      lowerCAmelCase_:							int       =	in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowerCAmelCase_:							Any       =	state_dict.pop(
          F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
      lowerCAmelCase_:							Any       =	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
      lowerCAmelCase_:							Any       =	in_proj_weight_cross_attn[:256, :]
      lowerCAmelCase_:							Optional[int]       =	in_proj_bias_cross_attn[:256]
      lowerCAmelCase_:							Union[str, Any]       =	in_proj_weight_cross_attn[256:512, :]
      lowerCAmelCase_:							Optional[int]       =	in_proj_bias_cross_attn[256:512]
      lowerCAmelCase_:							List[str]       =	in_proj_weight_cross_attn[-256:, :]
      lowerCAmelCase_:							Any       =	in_proj_bias_cross_attn[-256:]
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	image.size
   lowerCAmelCase_:							List[Any]       =	max(lowercase							,				lowercase					)
   lowerCAmelCase_:							str       =	800 if "detection" in checkpoint_url else 1000
   lowerCAmelCase_:							Dict       =	target_max_size / current_max_size
   lowerCAmelCase_:							Union[str, Any]       =	image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

   return resized_image
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							str       =	F.to_tensor(lowercase					)
   lowerCAmelCase_:							Optional[Any]       =	F.normalize(lowercase							,				mean=[0.485, 0.456, 0.406]							,				std=[0.229, 0.224, 0.225]					)
   return image
@torch.no_grad()
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   logger.info("Converting model..."					)

   # load original state dict
   lowerCAmelCase_:							Any       =	torch.hub.load_state_dict_from_url(lowercase							,				map_location="cpu"					)
   # rename keys
   for src, dest in rename_keys:
      rename_key(lowercase							,				lowercase							,				lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	rename_backbone_keys(lowercase					)
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase					)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowerCAmelCase_:							int       =	"model."
   for key in state_dict.copy().keys():
      if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
         lowerCAmelCase_:							int       =	state_dict.pop(lowercase					)
         lowerCAmelCase_:							List[str]       =	val
    # create HuggingFace model and load state dict
   lowerCAmelCase_:							Optional[Any]       =	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:
      lowerCAmelCase_:							str       =	15
      lowerCAmelCase_:							Dict       =	2
      lowerCAmelCase_:							List[Any]       =	{0: "table", 1: "table rotated"}
      lowerCAmelCase_:							Dict       =	idalabel
      lowerCAmelCase_:							Union[str, Any]       =	{v: k for k, v in idalabel.items()}
   else:
      lowerCAmelCase_:							str       =	125
      lowerCAmelCase_:							Optional[int]       =	6
      lowerCAmelCase_:							str       =	{
          0: "table",
          1: "table column",
          2: "table row",
          3: "table column header",
          4: "table projected row header",
          5: "table spanning cell",
      }
      lowerCAmelCase_:							Tuple       =	idalabel
      lowerCAmelCase_:							Dict       =	{v: k for k, v in idalabel.items()}

   lowerCAmelCase_:							int       =	DetrImageProcessor(
       format="coco_detection"							,				max_size=800 if "detection" in checkpoint_url else 1000					)
   lowerCAmelCase_:							int       =	TableTransformerForObjectDetection(lowercase					)
   model.load_state_dict(lowercase					)
   model.eval()

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

   lowerCAmelCase_:							Any       =	model(lowercase					)

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

   else:
      lowerCAmelCase_:							Dict       =	(1, 125, 7)
      lowerCAmelCase_:							int       =	torch.tensor(
          [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]					)
      lowerCAmelCase_:							Union[str, 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]							,				lowercase							,				atol=1E-4					)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,				lowercase							,				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(lowercase					).mkdir(exist_ok=lowercase					)
      model.save_pretrained(lowercase					)
      image_processor.save_pretrained(lowercase					)

   if push_to_hub:
      # Push model to HF hub
      logger.info("Pushing model to the hub..."					)
      lowerCAmelCase_:							List[str]       =	(
          "microsoft/table-transformer-detection"
          if "detection" in checkpoint_url
          else "microsoft/table-transformer-structure-recognition"
      )
      model.push_to_hub(lowercase					)
      image_processor.push_to_hub(lowercase					)


if __name__ == "__main__":
 a  :      Dict            =    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  :      Union[str, Any]            =    parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      Any            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    'transfo-xl'
    SCREAMING_SNAKE_CASE:				List[Any]          =    ['mems']
    SCREAMING_SNAKE_CASE:				List[Any]          =    {
        'n_token': 'vocab_size',
        'hidden_size': 'd_model',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=267_735       ,						lowerCamelCase__=[20_000, 40_000, 200_000]       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=16       ,						lowerCamelCase__=64       ,						lowerCamelCase__=4_096       ,						lowerCamelCase__=4       ,						lowerCamelCase__=False       ,						lowerCamelCase__=18       ,						lowerCamelCase__=1_600       ,						lowerCamelCase__=1_000       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0       ,						lowerCamelCase__=-1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=True       ,						lowerCamelCase__="normal"       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Dict       =	vocab_size
       lowerCAmelCase_:							Any       =	[]
       self.cutoffs.extend(lowerCamelCase__ )
       if proj_share_all_but_first:
          lowerCAmelCase_:							int       =	[False] + [True] * len(self.cutoffs )
       else:
          lowerCAmelCase_:							Any       =	[False] + [False] * len(self.cutoffs )
       lowerCAmelCase_:							Dict       =	d_model
       lowerCAmelCase_:							Union[str, Any]       =	d_embed
       lowerCAmelCase_:							Dict       =	d_head
       lowerCAmelCase_:							Dict       =	d_inner
       lowerCAmelCase_:							List[str]       =	div_val
       lowerCAmelCase_:							List[str]       =	pre_lnorm
       lowerCAmelCase_:							List[str]       =	n_layer
       lowerCAmelCase_:							List[str]       =	n_head
       lowerCAmelCase_:							str       =	mem_len
       lowerCAmelCase_:							Any       =	same_length
       lowerCAmelCase_:							Optional[Any]       =	attn_type
       lowerCAmelCase_:							int       =	clamp_len
       lowerCAmelCase_:							Optional[int]       =	sample_softmax
       lowerCAmelCase_:							Optional[Any]       =	adaptive
       lowerCAmelCase_:							Optional[int]       =	dropout
       lowerCAmelCase_:							Union[str, Any]       =	dropatt
       lowerCAmelCase_:							Tuple       =	untie_r
       lowerCAmelCase_:							Union[str, Any]       =	init
       lowerCAmelCase_:							Optional[Any]       =	init_range
       lowerCAmelCase_:							Optional[Any]       =	proj_init_std
       lowerCAmelCase_:							Tuple       =	init_std
       lowerCAmelCase_:							Tuple       =	layer_norm_epsilon
       super().__init__(eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )




    @property
    def      _a    (    self ):
       # Message copied from Transformer-XL documentation
       logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1




    @max_position_embeddings.setter
    def      _a    (    self       ,						lowerCamelCase__ ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )