




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()
_lowerCamelCase     :		Optional[int]								=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase     :		Optional[int]								=       []
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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Union[str, Any]:
       A__							=   state_dict.pop(__snake_case						)
       A__							=   val







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Tuple:
       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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:
       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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Any:
       A__			,							A__							=   image.size
       A__							=   max(__snake_case			,    __snake_case						)
       A__							=   800 if '''detection''' in checkpoint_url else 1_000
       A__							=   target_max_size / current_max_size
       A__							=   image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

       return resized_image







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:
       A__							=   F.to_tensor(__snake_case						)
       A__							=   F.normalize(__snake_case			,    mean=[0.4_85, 0.4_56, 0.4_06]			,    std=[0.2_29, 0.2_24, 0.2_25]						)
       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Any:
       logger.info('''Converting model...'''						)

       # load original state dict
       A__							=   torch.hub.load_state_dict_from_url(__snake_case			,    map_location='''cpu'''						)
       # rename keys
       for src, dest in rename_keys:
              rename_key(__snake_case			,    __snake_case			,    __snake_case						)
       A__							=   rename_backbone_keys(__snake_case						)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__snake_case						)
       # 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(__snake_case						)
                     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 1_000						)
       A__							=   TableTransformerForObjectDetection(__snake_case						)
       model.load_state_dict(__snake_case						)
       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=__snake_case						)
       A__							=   Image.open(__snake_case						).convert('''RGB'''						)
       A__							=   normalize(resize(__snake_case			,    __snake_case						)						).unsqueeze(0						)

       A__							=   model(__snake_case						)

       if "detection" in checkpoint_url:
              A__							=   (1, 15, 3)
              A__							=   torch.tensor(
                  [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]						)
              A__							=   torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]						)

       else:
              A__							=   (1, 125, 7)
              A__							=   torch.tensor(
                  [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]						)
              A__							=   torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]						)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]			,    __snake_case			,    atol=1E-4						)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,    __snake_case			,    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(__snake_case						).mkdir(exist_ok=__snake_case						)
              model.save_pretrained(__snake_case						)
              image_processor.save_pretrained(__snake_case						)

       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(__snake_case						)
              image_processor.push_to_hub(__snake_case						)


if __name__ == "__main__":
  _lowerCamelCase     :		List[str]								=       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."""
  )
  _lowerCamelCase     :		str								=       parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


