
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()
_snake_case      :							Tuple     	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case      :							Dict     	=    []
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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      state_dict.pop(__magic_name__						)
				lowercase         =      val

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase         =      key.replace("backbone.0.body"		,    "backbone.conv_encoder.model"						)
												lowercase         =      value
								else:
												lowercase         =      value

				return new_state_dict

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      ""

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

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase         =      image.size
				lowercase         =      max(__magic_name__		,    __magic_name__						)
				lowercase         =      800 if "detection" in checkpoint_url else 1000
				lowercase         =      target_max_size / current_max_size
				lowercase         =      image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

				return resized_image

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      F.to_tensor(__magic_name__						)
				lowercase         =      F.normalize(__magic_name__		,    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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


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

				# load original state dict
				lowercase         =      torch.hub.load_state_dict_from_url(__magic_name__		,    map_location="cpu"						)
				# rename keys
				for src, dest in rename_keys:
								rename_key(__magic_name__		,    __magic_name__		,    __magic_name__						)
				lowercase         =      rename_backbone_keys(__magic_name__						)
				# query, key and value matrices need special treatment
				read_in_q_k_v(__magic_name__						)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase         =      "model."
				for key in state_dict.copy().keys():
								if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
												lowercase         =      state_dict.pop(__magic_name__						)
												lowercase         =      val
    # create HuggingFace model and load state dict
				lowercase         =      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:
								lowercase         =      15
								lowercase         =      2
								lowercase         =      {0: "table", 1: "table rotated"}
								lowercase         =      idalabel
								lowercase         =      {v: k for k, v in idalabel.items()}
				else:
								lowercase         =      125
								lowercase         =      6
								lowercase         =      {
								    0: "table",
								    1: "table column",
								    2: "table row",
								    3: "table column header",
								    4: "table projected row header",
								    5: "table spanning cell",
								}
								lowercase         =      idalabel
								lowercase         =      {v: k for k, v in idalabel.items()}

				lowercase         =      DetrImageProcessor(
				    format="coco_detection"		,    max_size=800 if "detection" in checkpoint_url else 1000						)
				lowercase         =      TableTransformerForObjectDetection(__magic_name__						)
				model.load_state_dict(__magic_name__						)
				model.eval()

				# verify our conversion
				lowercase         =      "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
				lowercase         =      hf_hub_download(repo_id="nielsr/example-pdf"		,    repo_type="dataset"		,    filename=__magic_name__						)
				lowercase         =      Image.open(__magic_name__						).convert("RGB"						)
				lowercase         =      normalize(resize(__magic_name__		,    __magic_name__						)						).unsqueeze(0						)

				lowercase         =      model(__magic_name__						)

				if "detection" in checkpoint_url:
								lowercase         =      (1, 15, 3)
								lowercase         =      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]]						)
								lowercase         =      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:
								lowercase         =      (1, 125, 7)
								lowercase         =      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]]						)
								lowercase         =      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]		,    __magic_name__		,    atol=1e-4						)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,    __magic_name__		,    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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
								model.save_pretrained(__magic_name__						)
								image_processor.save_pretrained(__magic_name__						)

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


if __name__ == "__main__":
						_snake_case      :							Tuple     	=    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."
						)
						_snake_case      :							Dict     	=    parser.parse_args()
						convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:DDPMScheduler		,    lowerCAmelCase__							:Optional[Any]		,    )  ->Optional[int]:
										super().__init__()
										lowercase         =      value_function
										lowercase         =      unet
										lowercase         =      scheduler
										lowercase         =      env
										lowercase         =      env.get_dataset()
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].mean()
														except:  # noqa: E722
																		pass
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].std()
														except:  # noqa: E722
																		pass
										lowercase         =      env.observation_space.shape[0]
										lowercase         =      env.action_space.shape[0]





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										if type(lowerCAmelCase__     ) is dict:
														return {k: self.to_torch(lowerCAmelCase__     ) for k, v in x_in.items()}
										elif torch.is_tensor(lowerCAmelCase__     ):
														return x_in.to(self.unet.device     )
										return torch.tensor(lowerCAmelCase__		,    device=self.unet.device     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										lowercase         =      x.shape[0]
										lowercase         =      None
										for i in tqdm.tqdm(self.scheduler.timesteps     ):
														# create batch of timesteps to pass into model
														lowercase         =      torch.full((batch_size,)		,    lowerCAmelCase__		,    device=self.unet.device		,    dtype=torch.long     )
														for _ in range(lowerCAmelCase__     ):
																		with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						lowercase         =      self.value_function(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample
																						lowercase         =      torch.autograd.grad([y.sum()]		,    [x]     )[0]

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

														# TODO: verify deprecation of this kwarg
														lowercase         =      self.scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    predict_epsilon=lowerCAmelCase__     )["prev_sample"]

														# apply conditions to the trajectory (set the initial state)
														lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
														lowercase         =      self.to_torch(lowerCAmelCase__     )
										return x, y





						def __call__(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int=32		,    lowerCAmelCase__							:int=2		,    lowerCAmelCase__							:List[str]=0.1     )  ->Tuple:
										# normalize the observations and create  batch dimension
										lowercase         =      self.normalize(lowerCAmelCase__		,    "observations"     )
										lowercase         =      obs[None].repeat(lowerCAmelCase__		,    axis=0     )

										lowercase         =      {0: self.to_torch(lowerCAmelCase__     )}
										lowercase         =      (batch_size, planning_horizon, self.state_dim + self.action_dim)

										# generate initial noise and apply our conditions (to make the trajectories start at current state)
										lowercase         =      randn_tensor(lowerCAmelCase__		,    device=self.unet.device     )
										lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
										lowercase         =      self.to_torch(lowerCAmelCase__     )

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										# sort output trajectories by value
										lowercase         =      y.argsort(0		,    descending=lowerCAmelCase__     ).squeeze()
										lowercase         =      x[sorted_idx]
										lowercase         =      sorted_values[:, :, : self.action_dim]
										lowercase         =      actions.detach().cpu().numpy()
										lowercase         =      self.de_normalize(lowerCAmelCase__		,    key="actions"     )

										# select the action with the highest value
										if y is not None:
														lowercase         =      0
										else:
														# if we didn't run value guiding, select a random action
														lowercase         =      np.random.randint(0		,    lowerCAmelCase__     )

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
