


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    :Optional[Any]				       =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase    :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 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			Any    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    state_dict.pop(_lowercase						)
     __UpperCAmelCase			:				List[Any]													=    val




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               __UpperCAmelCase			:				Dict													=    key.replace('''backbone.0.body'''    ,      '''backbone.conv_encoder.model'''						)
               __UpperCAmelCase			:				Dict													=    value
          else:
               __UpperCAmelCase			:				Tuple													=    value

     return new_state_dict




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    ''''''

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




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    image.size
     __UpperCAmelCase			:				str													=    max(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				str													=    800 if '''detection''' in checkpoint_url else 1000
     __UpperCAmelCase			:				int													=    target_max_size / current_max_size
     __UpperCAmelCase			:				Dict													=    image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

     return resized_image




def 			_a			(		_lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    F.to_tensor(_lowercase						)
     __UpperCAmelCase			:				Any													=    F.normalize(_lowercase    ,      mean=[0.485, 0.456, 0.406]    ,      std=[0.229, 0.224, 0.225]						)
     return image




@torch.no_grad()
def 			_a			(		_lowercase      :			Dict    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			List[str]						):





     '''simple docstring'''






     logger.info('''Converting model...'''						)

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

     __UpperCAmelCase			:				Any													=    DetrImageProcessor(
         format='''coco_detection'''    ,      max_size=800 if '''detection''' in checkpoint_url else 1000						)
     __UpperCAmelCase			:				Any													=    TableTransformerForObjectDetection(_lowercase						)
     model.load_state_dict(_lowercase						)
     model.eval()

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

     __UpperCAmelCase			:				Dict													=    model(_lowercase						)

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

     else:
          __UpperCAmelCase			:				Dict													=    (1, 125, 7)
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]						)
          __UpperCAmelCase			:				int													=    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]    ,      _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...'''						)
          __UpperCAmelCase			:				Tuple													=    (
              '''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    :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."
      )
      __UpperCAmelCase    :Dict				       =     parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__UpperCAmelCase    :List[str]				       =     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__UpperCAmelCase    :List[Any]				       =     "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''float'''    ),
                      '''references''': datasets.Value('''float'''    ),
                  }    )							,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}