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_    					=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_    					=       []
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    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						List[str] =					state_dict.pop(lowerCamelCase	)
 UpperCamelCase_					:						Dict =					val





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						Optional[Any] =					OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   UpperCamelCase_					:						Any =					key.replace('backbone.0.body'  ,  'backbone.conv_encoder.model'	)
   UpperCamelCase_					:						Union[str, Any] =					value
  else:
   UpperCamelCase_					:						Dict =					value

 return new_state_dict





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						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)
  UpperCamelCase_					:						List[str] =					state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
  UpperCamelCase_					:						List[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
  UpperCamelCase_					:						List[Any] =					in_proj_weight[:256, :]
  UpperCamelCase_					:						Dict =					in_proj_bias[:256]
  UpperCamelCase_					:						int =					in_proj_weight[256:512, :]
  UpperCamelCase_					:						Tuple =					in_proj_bias[256:512]
  UpperCamelCase_					:						Dict =					in_proj_weight[-256:, :]
  UpperCamelCase_					:						Optional[Any] =					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
  UpperCamelCase_					:						Tuple =					state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
  UpperCamelCase_					:						Union[str, Any] =					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_					:						Optional[int] =					in_proj_weight[:256, :]
  UpperCamelCase_					:						Any =					in_proj_bias[:256]
  UpperCamelCase_					:						Optional[int] =					in_proj_weight[256:512, :]
  UpperCamelCase_					:						Union[str, Any] =					in_proj_bias[256:512]
  UpperCamelCase_					:						Dict =					in_proj_weight[-256:, :]
  UpperCamelCase_					:						List[str] =					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  UpperCamelCase_					:						Optional[int] =					state_dict.pop(
      F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
  UpperCamelCase_					:						List[str] =					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_					:						Tuple =					in_proj_weight_cross_attn[:256, :]
  UpperCamelCase_					:						Dict =					in_proj_bias_cross_attn[:256]
  UpperCamelCase_					:						Dict =					in_proj_weight_cross_attn[256:512, :]
  UpperCamelCase_					:						Optional[int] =					in_proj_bias_cross_attn[256:512]
  UpperCamelCase_					:						List[str] =					in_proj_weight_cross_attn[-256:, :]
  UpperCamelCase_					:						Optional[int] =					in_proj_bias_cross_attn[-256:]





def    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     int	):
 UpperCamelCase_, UpperCamelCase_					:						Tuple =					image.size
 UpperCamelCase_					:						Tuple =					max(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						int =					800 if 'detection' in checkpoint_url else 1000
 UpperCamelCase_					:						Any =					target_max_size / current_max_size
 UpperCamelCase_					:						Tuple =					image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

 return resized_image





def    __lowercase     (						lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Dict =					F.to_tensor(lowerCamelCase	)
 UpperCamelCase_					:						int =					F.normalize(lowerCamelCase  ,  mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,  std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	)
 return image





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     List[Any]	):
 logger.info('Converting model...'	)

 # load original state dict
 UpperCamelCase_					:						Any =					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_					:						Any =					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_					:						List[Any] =					'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
   UpperCamelCase_					:						Union[str, Any] =					state_dict.pop(lowerCamelCase	)
   UpperCamelCase_					:						Tuple =					val
    # create HuggingFace model and load state dict
 UpperCamelCase_					:						List[str] =					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_					:						List[str] =					15
  UpperCamelCase_					:						Tuple =					2
  UpperCamelCase_					:						Optional[Any] =					{0: 'table', 1: 'table rotated'}
  UpperCamelCase_					:						List[Any] =					idalabel
  UpperCamelCase_					:						List[Any] =					{v: k for k, v in idalabel.items()}
 else:
  UpperCamelCase_					:						Optional[Any] =					125
  UpperCamelCase_					:						Optional[Any] =					6
  UpperCamelCase_					:						Tuple =					{
      0: 'table',
      1: 'table column',
      2: 'table row',
      3: 'table column header',
      4: 'table projected row header',
      5: 'table spanning cell',
  }
  UpperCamelCase_					:						Dict =					idalabel
  UpperCamelCase_					:						List[str] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						Dict =					DetrImageProcessor(
     format='coco_detection'  ,  max_size=800 if 'detection' in checkpoint_url else 1000	)
 UpperCamelCase_					:						Optional[int] =					TableTransformerForObjectDetection(lowerCamelCase	)
 model.load_state_dict(lowerCamelCase	)
 model.eval()

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

 UpperCamelCase_					:						str =					model(lowerCamelCase	)

 if "detection" in checkpoint_url:
  UpperCamelCase_					:						Dict =					(1, 15, 3)
  UpperCamelCase_					:						Tuple =					torch.tensor(
      [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]	)
  UpperCamelCase_					:						int =					torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]	)

 else:
  UpperCamelCase_					:						Union[str, Any] =					(1, 125, 7)
  UpperCamelCase_					:						Dict =					torch.tensor(
      [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]	)
  UpperCamelCase_					:						List[str] =					torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]	)

 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_					:						Tuple =					(
      '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_    					=       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_    					=       parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						int =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					json.load(open(hf_hub_download(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)  ,  'r'	)	)
 UpperCamelCase_					:						str =					{int(lowerCamelCase	): v for k, v in idalabel.items()}
 UpperCamelCase_					:						Optional[int] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						str =					'std_conv' if 'bit' in model_name else False

 # note that when using BiT as backbone for ViT-hybrid checkpoints,
 # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
 # config.conv_layer = "std_conv_same"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

 # load original model from timm
 UpperCamelCase_					:						Dict =					create_model(lowerCamelCase  ,  pretrained=lowerCamelCase	)
 timm_model.eval()

 # load state_dict of original model
 UpperCamelCase_					:						str =					timm_model.state_dict()
 for key in state_dict.copy().keys():
  UpperCamelCase_					:						Tuple =					state_dict.pop(lowerCamelCase	)
  UpperCamelCase_					:						str =					val.squeeze() if 'head' in key else val

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

 UpperCamelCase_					:						str =					{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 UpperCamelCase_					:						Tuple =					BitImageProcessor(
     do_resize=lowerCamelCase  ,  size={'shortest_edge': timm_transforms[0].size}  ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,  do_center_crop=lowerCamelCase  ,  crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}  ,  do_normalize=lowerCamelCase  ,  image_mean=timm_transforms[-1].mean.tolist()  ,  image_std=timm_transforms[-1].std.tolist()  ,  )

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

 # verify pixel values
 assert torch.allclose(lowerCamelCase  ,  lowerCamelCase	)

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

 print('Logits:'  ,  logits[0, :3]	)
 print('Predicted class:'  ,  model.config.idalabel[logits.argmax(-1	).item()]	)
 UpperCamelCase_					:						List[Any] =					timm_model(lowerCamelCase	)
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(lowerCamelCase  ,  outputs.logits  ,  atol=1e-3	)
 print('Looks ok!'	)

 if pytorch_dump_folder_path is not None:
  Path(lowerCamelCase	).mkdir(exist_ok=lowerCamelCase	)
  print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"	)
  model.save_pretrained(lowerCamelCase	)
  processor.save_pretrained(lowerCamelCase	)

 if push_to_hub:
  print(F"Pushing model {model_name} and processor to the hub"	)
  model.push_to_hub(F"ybelkada/{model_name}"	)
  processor.push_to_hub(F"ybelkada/{model_name}"	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='resnetv2_50x1_bitm',
       type=str,
       help='Name of the BiT timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether to push the model to the hub.',
   )

   a_    					=       parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



