
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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Dict     			=						state_dict.pop(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						val

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    return new_state_dict

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[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_ :    List[Any]     			=						state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
        snake_case_ :    Optional[int]     			=						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_ :    Tuple     			=						in_proj_weight[:2_5_6, :]
        snake_case_ :    Optional[int]     			=						in_proj_bias[:2_5_6]
        snake_case_ :    List[Any]     			=						in_proj_weight[2_5_6:5_1_2, :]
        snake_case_ :    Optional[Any]     			=						in_proj_bias[2_5_6:5_1_2]
        snake_case_ :    Any     			=						in_proj_weight[-2_5_6:, :]
        snake_case_ :    List[Any]     			=						in_proj_bias[-2_5_6:]
    # 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_ :    Any     			=						state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
        snake_case_ :    int     			=						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_ :    str     			=						in_proj_weight[:2_5_6, :]
        snake_case_ :    Dict     			=						in_proj_bias[:2_5_6]
        snake_case_ :    Optional[int]     			=						in_proj_weight[2_5_6:5_1_2, :]
        snake_case_ :    Union[str, Any]     			=						in_proj_bias[2_5_6:5_1_2]
        snake_case_ :    str     			=						in_proj_weight[-2_5_6:, :]
        snake_case_ :    Dict     			=						in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        snake_case_ :    Dict     			=						state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
        snake_case_ :    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
        snake_case_ :    Any     			=						in_proj_weight_cross_attn[:2_5_6, :]
        snake_case_ :    str     			=						in_proj_bias_cross_attn[:2_5_6]
        snake_case_ :    Any     			=						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        snake_case_ :    Dict     			=						in_proj_bias_cross_attn[2_5_6:5_1_2]
        snake_case_ :    Union[str, Any]     			=						in_proj_weight_cross_attn[-2_5_6:, :]
        snake_case_ :    str     			=						in_proj_bias_cross_attn[-2_5_6:]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						image.size
    snake_case_ :    Union[str, Any]     			=						max(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						8_0_0 if "detection" in checkpoint_url else 1_0_0_0
    snake_case_ :    Dict     			=						target_max_size / current_max_size
    snake_case_ :    Dict     			=						image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

    return resized_image

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Dict     			=						F.to_tensor(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						F.normalize(__SCREAMING_SNAKE_CASE,		mean=[0.485, 0.456, 0.406],		std=[0.229, 0.224, 0.225]       )
    return image

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    # load original state dict
    snake_case_ :    Optional[int]     			=						torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE,		map_location="cpu"       )
    # rename keys
    for src, dest in rename_keys:
        rename_key(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						rename_backbone_keys(__SCREAMING_SNAKE_CASE       )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__SCREAMING_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
    snake_case_ :    List[Any]     			=						"model."
    for key in state_dict.copy().keys():
        if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            snake_case_ :    Dict     			=						state_dict.pop(__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Optional[int]     			=						val
    # create HuggingFace model and load state dict
    snake_case_ :    Optional[int]     			=						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_ :    Dict     			=						1_5
        snake_case_ :    Union[str, Any]     			=						2
        snake_case_ :    List[str]     			=						{0: "table", 1: "table rotated"}
        snake_case_ :    Union[str, Any]     			=						idalabel
        snake_case_ :    Optional[int]     			=						{v: k for k, v in idalabel.items()}
    else:
        snake_case_ :    str     			=						1_2_5
        snake_case_ :    List[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[Any]     			=						idalabel
        snake_case_ :    Dict     			=						{v: k for k, v in idalabel.items()}

    snake_case_ :    Tuple     			=						DetrImageProcessor(
        format="coco_detection",		max_size=8_0_0 if "detection" in checkpoint_url else 1_0_0_0       )
    snake_case_ :    Dict     			=						TableTransformerForObjectDetection(__SCREAMING_SNAKE_CASE       )
    model.load_state_dict(__SCREAMING_SNAKE_CASE       )
    model.eval()

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

    snake_case_ :    Optional[Any]     			=						model(__SCREAMING_SNAKE_CASE       )

    if "detection" in checkpoint_url:
        snake_case_ :    List[Any]     			=						(1, 1_5, 3)
        snake_case_ :    Dict     			=						torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]       )
        snake_case_ :    Tuple     			=						torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]       )

    else:
        snake_case_ :    List[str]     			=						(1, 1_2_5, 7)
        snake_case_ :    Any     			=						torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
        snake_case_ :    Tuple     			=						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],		__SCREAMING_SNAKE_CASE,		atol=1E-4       )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3],		__SCREAMING_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(__SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE       )
        model.save_pretrained(__SCREAMING_SNAKE_CASE       )
        image_processor.save_pretrained(__SCREAMING_SNAKE_CASE       )

    if push_to_hub:
        # Push model to HF hub
        logger.info("Pushing model to the hub..."       )
        snake_case_ :    List[Any]     			=						(
            "microsoft/table-transformer-detection"
            if "detection" in checkpoint_url
            else "microsoft/table-transformer-structure-recognition"
        )
        model.push_to_hub(__SCREAMING_SNAKE_CASE       )
        image_processor.push_to_hub(__SCREAMING_SNAKE_CASE       )


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)






'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "This is a straightforward English test sentence.",
         "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
         "Now we're going to add some Chinese: 一 二 三 一二三",
         "And some much more rare Chinese: 齉 堃 齉堃",
         "Je vais aussi écrire en français pour tester les accents",
         "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




