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()
SCREAMING_SNAKE_CASE :	Tuple							     =		logging.get_logger(__name__)

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



 """simple docstring"""


 __a       =  state_dict.pop(_A					)
 __a       =  val



def   __A		(			_A					):



 """simple docstring"""


 __a       =  OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __a       =  key.replace("backbone.0.body"							,				"backbone.conv_encoder.model"					)
   __a       =  value
  else:
   __a       =  value

 return new_state_dict



def   __A		(			_A					):



 """simple docstring"""


 __a       =  ""

 # 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       =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
  __a       =  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       =  in_proj_weight[:256, :]
  __a       =  in_proj_bias[:256]
  __a       =  in_proj_weight[256:512, :]
  __a       =  in_proj_bias[256:512]
  __a       =  in_proj_weight[-256:, :]
  __a       =  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
  __a       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
  __a       =  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       =  in_proj_weight[:256, :]
  __a       =  in_proj_bias[:256]
  __a       =  in_proj_weight[256:512, :]
  __a       =  in_proj_bias[256:512]
  __a       =  in_proj_weight[-256:, :]
  __a       =  in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  __a       =  state_dict.pop(
      f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
  __a       =  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       =  in_proj_weight_cross_attn[:256, :]
  __a       =  in_proj_bias_cross_attn[:256]
  __a       =  in_proj_weight_cross_attn[256:512, :]
  __a       =  in_proj_bias_cross_attn[256:512]
  __a       =  in_proj_weight_cross_attn[-256:, :]
  __a       =  in_proj_bias_cross_attn[-256:]



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       ,     __a       =  image.size
 __a       =  max(_A							,				_A					)
 __a       =  800 if "detection" in checkpoint_url else 1000
 __a       =  target_max_size / current_max_size
 __a       =  image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

 return resized_image



def   __A		(			_A					):



 """simple docstring"""


 __a       =  F.to_tensor(_A					)
 __a       =  F.normalize(_A							,				mean=[0.485, 0.456, 0.406]							,				std=[0.229, 0.224, 0.225]					)
 return image



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 logger.info("Converting model..."					)

 # load original state dict
 __a       =  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       =  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       =  "model."
 for key in state_dict.copy().keys():
  if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
   __a       =  state_dict.pop(_A					)
   __a       =  val
    # create HuggingFace model and load state dict
 __a       =  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       =  15
  __a       =  2
  __a       =  {0: "table", 1: "table rotated"}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}
 else:
  __a       =  125
  __a       =  6
  __a       =  {
      0: "table",
      1: "table column",
      2: "table row",
      3: "table column header",
      4: "table projected row header",
      5: "table spanning cell",
  }
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 __a       =  DetrImageProcessor(
     format="coco_detection"							,				max_size=800 if "detection" in checkpoint_url else 1000					)
 __a       =  TableTransformerForObjectDetection(_A					)
 model.load_state_dict(_A					)
 model.eval()

 # verify our conversion
 __a       =  "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
 __a       =  hf_hub_download(repo_id="nielsr/example-pdf"							,				repo_type="dataset"							,				filename=_A					)
 __a       =  Image.open(_A					).convert("RGB"					)
 __a       =  normalize(resize(_A							,				_A					)					).unsqueeze(0					)

 __a       =  model(_A					)

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

 else:
  __a       =  (1, 125, 7)
  __a       =  torch.tensor(
      [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]					)
  __a       =  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       =  (
      "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__":
  SCREAMING_SNAKE_CASE :	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."""
  )
  SCREAMING_SNAKE_CASE :	int							     =		parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



from datetime import datetime as dt
import os

from github import Github


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def   __A		(			):



 """simple docstring"""


 __a       =  Github(os.environ["GITHUB_TOKEN"]					)
 __a       =  g.get_repo("huggingface/transformers"					)
 __a       =  repo.get_issues(state="open"					)

 for issue in open_issues:
  __a       =  sorted([comment for comment in issue.get_comments()]							,				key=lambda _A					: i.created_at							,				reverse=_A					)
  __a       =  comments[0] if len(_A					) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()					)
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="closed"					)
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()					)
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       "This issue has been automatically marked as stale because it has not had "
       "recent activity. If you think this still needs to be addressed "
       "please comment on this thread.\n\nPlease note that issues that do not follow the "
       "[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
       "are likely to be ignored."					)


if __name__ == "__main__":
  main()


