




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	:						int			=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase	:						Tuple			=     []
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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[str]      	=						state_dict.pop(lowerCamelCase   )
  lowercase    :int      	=						val



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[Any]      	=						OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowercase    :List[str]      	=						key.replace("backbone.0.body",			"backbone.conv_encoder.model"   )
      lowercase    :Dict      	=						value
    else:
      lowercase    :int      	=						value

  return new_state_dict



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



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

  return resized_image



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Dict      	=						F.to_tensor(lowerCamelCase   )
  lowercase    :Union[str, Any]      	=						F.normalize(lowerCamelCase,			mean=[0.485, 0.456, 0.406],			std=[0.229, 0.224, 0.225]   )
  return image



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  logger.info("Converting model..."   )

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

  lowercase    :List[str]      	=						DetrImageProcessor(
      format="coco_detection",			max_size=800 if "detection" in checkpoint_url else 1000   )
  lowercase    :str      	=						TableTransformerForObjectDetection(lowerCamelCase   )
  model.load_state_dict(lowerCamelCase   )
  model.eval()

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

  lowercase    :Any      	=						model(lowerCamelCase   )

  if "detection" in checkpoint_url:
    lowercase    :Union[str, Any]      	=						(1, 15, 3)
    lowercase    :str      	=						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]]   )
    lowercase    :int      	=						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:
    lowercase    :Optional[int]      	=						(1, 125, 7)
    lowercase    :List[Any]      	=						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]]   )
    lowercase    :Union[str, Any]      	=						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],			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..."   )
    lowercase    :Any      	=						(
        "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__":
     _UpperCAmelCase	:						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	:						Dict			=     parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						inspect.getfile(accelerate.test_utils				)
         lowercase    :List[Any]      	=						os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

         from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

         lowercase    :str      	=						test_metrics






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         debug_launcher(self.test_metrics.main       ,					num_processes=1				)






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         debug_launcher(self.test_metrics.main				)






       @require_single_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         self.test_metrics.main()






       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         print(F"Found {torch.cuda.device_count()} devices."				)
         lowercase    :str      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
           execute_subprocess_async(_lowerCAmelCase       ,					env=os.environ.copy()				)



