





'''simple docstring'''

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				:      Any						      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase				:      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 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, Any]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   List[str]			):
    A__							=     state_dict.pop(_lowerCamelCase			)
    A__							=     val





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict			):
    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 		UpperCamelCase							(				_lowerCamelCase   :   Any			):
    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[:2_56, :]
        A__							=     in_proj_bias[:2_56]
        A__							=     in_proj_weight[2_56:5_12, :]
        A__							=     in_proj_bias[2_56:5_12]
        A__							=     in_proj_weight[-2_56:, :]
        A__							=     in_proj_bias[-2_56:]
    # 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[:2_56, :]
        A__							=     in_proj_bias[:2_56]
        A__							=     in_proj_weight[2_56:5_12, :]
        A__							=     in_proj_bias[2_56:5_12]
        A__							=     in_proj_weight[-2_56:, :]
        A__							=     in_proj_bias[-2_56:]
        # 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[:2_56, :]
        A__							=     in_proj_bias_cross_attn[:2_56]
        A__							=     in_proj_weight_cross_attn[2_56:5_12, :]
        A__							=     in_proj_bias_cross_attn[2_56:5_12]
        A__							=     in_proj_weight_cross_attn[-2_56:, :]
        A__							=     in_proj_bias_cross_attn[-2_56:]





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   Optional[int]			):
    A__,      A__							=     image.size
    A__							=     max(_lowerCamelCase		,			_lowerCamelCase			)
    A__							=     8_00 if "detection" in checkpoint_url else 10_00
    A__							=     target_max_size / current_max_size
    A__							=     image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

    return resized_image





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict			):
    A__							=     F.to_tensor(_lowerCamelCase			)
    A__							=     F.normalize(_lowerCamelCase		,			mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]		,			std=[0.2_2_9, 0.2_2_4, 0.2_2_5]			)
    return image





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, Any]		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   Optional[int]			):
    logger.info("Converting model..."			)

    # load original state dict
    A__							=     torch.hub.load_state_dict_from_url(_lowerCamelCase		,			map_location="cpu"			)
    # rename keys
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)
    A__							=     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
    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(_lowerCamelCase			)
            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__							=     1_25
        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=8_00 if "detection" in checkpoint_url else 10_00			)
    A__							=     TableTransformerForObjectDetection(_lowerCamelCase			)
    model.load_state_dict(_lowerCamelCase			)
    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=_lowerCamelCase			)
    A__							=     Image.open(_lowerCamelCase			).convert("RGB"			)
    A__							=     normalize(resize(_lowerCamelCase		,			_lowerCamelCase			)			).unsqueeze(0			)

    A__							=     model(_lowerCamelCase			)

    if "detection" in checkpoint_url:
        A__							=     (1, 15, 3)
        A__							=     torch.tensor(
            [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]			)
        A__							=     torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]			)

    else:
        A__							=     (1, 1_25, 7)
        A__							=     torch.tensor(
            [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]			)
        A__							=     torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]			)

    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..."			)
        A__							=     (
            "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__":
  __lowerCAmelCase				:      Dict						      =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				:      Dict						      =parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



