





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	:       Union[str, Any]    	=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase	:       List[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__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[int]:
  __snake_case:						Optional[int]			  =						state_dict.pop(lowercase__)
  __snake_case:						List[str]			  =						val







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:
  __snake_case:						Any			  =						OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      __snake_case:						Union[str, Any]			  =						key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""")
      __snake_case:						Dict			  =						value
    else:
      __snake_case:						Union[str, Any]			  =						value

  return new_state_dict







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







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Tuple:
  __snake_case:						Tuple			  =						image.size
  __snake_case:						Any			  =						max(lowercase__ , lowercase__)
  __snake_case:						Optional[Any]			  =						800 if """detection""" in checkpoint_url else 1000
  __snake_case:						int			  =						target_max_size / current_max_size
  __snake_case:						Optional[Any]			  =						image.resize((int(round(scale * width)), int(round(scale * height))))

  return resized_image







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Optional[Any]:
  __snake_case:						List[str]			  =						F.to_tensor(lowercase__)
  __snake_case:						List[str]			  =						F.normalize(lowercase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225])
  return image







@torch.no_grad()
def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Tuple:
  logger.info("""Converting model...""")

  # load original state dict
  __snake_case:						int			  =						torch.hub.load_state_dict_from_url(lowercase__ , map_location="""cpu""")
  # rename keys
  for src, dest in rename_keys:
    rename_key(lowercase__ , lowercase__ , lowercase__)
  __snake_case:						Tuple			  =						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
  __snake_case:						Optional[Any]			  =						"""model."""
  for key in state_dict.copy().keys():
    if not key.startswith("""class_labels_classifier""") and not key.startswith("""bbox_predictor"""):
      __snake_case:						Union[str, Any]			  =						state_dict.pop(lowercase__)
      __snake_case:						str			  =						val
    # create HuggingFace model and load state dict
  __snake_case:						List[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:
    __snake_case:						str			  =						15
    __snake_case:						Any			  =						2
    __snake_case:						Union[str, Any]			  =						{0: """table""", 1: """table rotated"""}
    __snake_case:						List[Any]			  =						idalabel
    __snake_case:						Dict			  =						{v: k for k, v in idalabel.items()}
  else:
    __snake_case:						int			  =						125
    __snake_case:						Optional[Any]			  =						6
    __snake_case:						Optional[int]			  =						{
        0: """table""",
        1: """table column""",
        2: """table row""",
        3: """table column header""",
        4: """table projected row header""",
        5: """table spanning cell""",
    }
    __snake_case:						Optional[int]			  =						idalabel
    __snake_case:						List[Any]			  =						{v: k for k, v in idalabel.items()}

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

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

  __snake_case:						List[str]			  =						model(lowercase__)

  if "detection" in checkpoint_url:
    __snake_case:						List[str]			  =						(1, 15, 3)
    __snake_case:						Optional[Any]			  =						torch.tensor(
        [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]])
    __snake_case:						Union[str, Any]			  =						torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]])

  else:
    __snake_case:						List[Any]			  =						(1, 125, 7)
    __snake_case:						Dict			  =						torch.tensor(
        [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]])
    __snake_case:						Tuple			  =						torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]])

  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...""")
    __snake_case:						Dict			  =						(
        """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__":
      __UpperCAmelCase	:       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."
      )
      __UpperCAmelCase	:       Optional[int]    	=							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


__UpperCAmelCase	:       str    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       int    	=							{
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""rwkv"""
       lowerCAmelCase__						=					{"""max_position_embeddings""": """context_length"""}




       def __init__(  self			:   Dict							,    A			:   List[Any]=50_277							,    A			:   List[Any]=1_024							,    A			:   Union[str, Any]=4_096							,    A			:   Tuple=32							,    A			:   List[Any]=None							,    A			:   Tuple=None							,    A			:   Tuple=1E-5							,    A			:   int=0							,    A			:   Optional[int]=0							,    A			:   Dict=6							,    A			:   Dict=False							,    A			:   int=True							,    **A			:   List[Any]							,    ):
         __snake_case:						Tuple			  =						vocab_size
         __snake_case:						Any			  =						context_length
         __snake_case:						Dict			  =						hidden_size
         __snake_case:						Dict			  =						num_hidden_layers
         __snake_case:						Union[str, Any]			  =						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __snake_case:						str			  =						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __snake_case:						Any			  =						layer_norm_epsilon
         __snake_case:						int			  =						rescale_every
         __snake_case:						str			  =						use_cache

         __snake_case:						Dict			  =						bos_token_id
         __snake_case:						Union[str, Any]			  =						eos_token_id

         super().__init__(
             tie_word_embeddings=A							,    bos_token_id=A							,    eos_token_id=A							,    **A		)






