






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()
SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE 							=						[]
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 			_lowerCamelCase	(						__A    :							Tuple		,		__A    :							Tuple		,		__A    :							List[str] )      ->					Optional[Any]:
    _UpperCAmelCase				:      List[Any]			  =				state_dict.pop(__A )
    _UpperCAmelCase				:      List[Any]			  =				val

def 			_lowerCamelCase	(						__A    :							int )      ->					str:
    _UpperCAmelCase				:      int			  =				OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase				:      Optional[Any]			  =				key.replace('''backbone.0.body'''		,		'''backbone.conv_encoder.model''' )
            _UpperCAmelCase				:      int			  =				value
        else:
            _UpperCAmelCase				:      str			  =				value

    return new_state_dict

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

def 			_lowerCamelCase	(						__A    :							Optional[Any]		,		__A    :							Optional[Any] )      ->					Tuple:
    _UpperCAmelCase				:      int			  =				image.size
    _UpperCAmelCase				:      str			  =				max(__A		,		__A )
    _UpperCAmelCase				:      List[Any]			  =				800 if '''detection''' in checkpoint_url else 1_000
    _UpperCAmelCase				:      Dict			  =				target_max_size / current_max_size
    _UpperCAmelCase				:      List[str]			  =				image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

    return resized_image

def 			_lowerCamelCase	(						__A    :							List[str] )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				F.to_tensor(__A )
    _UpperCAmelCase				:      Dict			  =				F.normalize(__A		,		mean=[0.485, 0.456, 0.406]		,		std=[0.229, 0.224, 0.225] )
    return image

@torch.no_grad()
def 			_lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							int		,		__A    :							Dict )      ->					int:
    logger.info('''Converting model...''' )

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

    _UpperCAmelCase				:      Optional[int]			  =				DetrImageProcessor(
        format='''coco_detection'''		,		max_size=800 if '''detection''' in checkpoint_url else 1_000 )
    _UpperCAmelCase				:      str			  =				TableTransformerForObjectDetection(__A )
    model.load_state_dict(__A )
    model.eval()

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

    _UpperCAmelCase				:      Optional[Any]			  =				model(__A )

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

    else:
        _UpperCAmelCase				:      str			  =				(1, 125, 7)
        _UpperCAmelCase				:      int			  =				torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
        _UpperCAmelCase				:      List[str]			  =				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]		,		__A		,		atol=1e-4 )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,		__A		,		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(__A ).mkdir(exist_ok=__A )
        model.save_pretrained(__A )
        image_processor.save_pretrained(__A )

    if push_to_hub:
        # Push model to HF hub
        logger.info('''Pushing model to the hub...''' )
        _UpperCAmelCase				:      str			  =				(
            '''microsoft/table-transformer-detection'''
            if '''detection''' in checkpoint_url
            else '''microsoft/table-transformer-structure-recognition'''
        )
        model.push_to_hub(__A )
        image_processor.push_to_hub(__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						datasets.load_iris()
  SCREAMING_SNAKE_CASE 							=						iris.data[:, :2]
  SCREAMING_SNAKE_CASE 							=						(iris.target != 0) * 1

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




