






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	:							int				=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase	:							Union[str, Any]				=[]
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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	List[str]:
     UpperCamelCase__			:   List[Any]       				=  state_dict.pop(__a			)
     UpperCamelCase__			:   int       				=  val







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     UpperCamelCase__			:   Tuple       				=  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__			:   List[str]       				=  value
          else:
               UpperCamelCase__			:   Dict       				=  value

     return new_state_dict







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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Optional[int]:
     UpperCamelCase__			:   str       				=  image.size
     UpperCamelCase__			:   Optional[Any]       				=  max(__a					,    __a			)
     UpperCamelCase__			:   List[Any]       				=  800 if '''detection''' in checkpoint_url else 1000
     UpperCamelCase__			:   Dict       				=  target_max_size / current_max_size
     UpperCamelCase__			:   Any       				=  image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

     return resized_image







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Any       				=  F.to_tensor(__a			)
     UpperCamelCase__			:   int       				=  F.normalize(__a					,    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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Dict:
     logger.info("Converting model..."			)

     # load original state dict
     UpperCamelCase__			:   Optional[Any]       				=  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__			:   Dict       				=  '''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
               UpperCamelCase__			:   Optional[Any]       				=  state_dict.pop(__a			)
               UpperCamelCase__			:   int       				=  val
    # create HuggingFace model and load state dict
     UpperCamelCase__			:   str       				=  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[str]       				=  15
          UpperCamelCase__			:   int       				=  2
          UpperCamelCase__			:   Tuple       				=  {0: '''table''', 1: '''table rotated'''}
          UpperCamelCase__			:   int       				=  idalabel
          UpperCamelCase__			:   Dict       				=  {v: k for k, v in idalabel.items()}
     else:
          UpperCamelCase__			:   int       				=  125
          UpperCamelCase__			:   List[str]       				=  6
          UpperCamelCase__			:   Optional[Any]       				=  {
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          UpperCamelCase__			:   Dict       				=  idalabel
          UpperCamelCase__			:   Optional[Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   List[Any]       				=  DetrImageProcessor(
         format="coco_detection"					,    max_size=800 if "detection" in checkpoint_url else 1000			)
     UpperCamelCase__			:   int       				=  TableTransformerForObjectDetection(__a			)
     model.load_state_dict(__a			)
     model.eval()

     # verify our conversion
     UpperCamelCase__			:   Dict       				=  '''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__			:   Tuple       				=  Image.open(__a			).convert("RGB"			)
     UpperCamelCase__			:   int       				=  normalize(resize(__a					,    __a			)			).unsqueeze(0			)

     UpperCamelCase__			:   Optional[int]       				=  model(__a			)

     if "detection" in checkpoint_url:
          UpperCamelCase__			:   Dict       				=  (1, 15, 3)
          UpperCamelCase__			:   List[Any]       				=  torch.tensor(
              [[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]]			)
          UpperCamelCase__			:   Tuple       				=  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:
          UpperCamelCase__			:   Optional[Any]       				=  (1, 125, 7)
          UpperCamelCase__			:   Dict       				=  torch.tensor(
              [[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]]			)
          UpperCamelCase__			:   List[Any]       				=  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]					,    __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__			:   Union[str, Any]       				=  (
              '''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__":
 lowerCamelCase	:							int				=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	:							str				=parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''
from collections import defaultdict
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= total  # total no of tasks (N)

							# DP table will have a dimension of (2^M)*N
							# initially all values are set to -1
							UpperCamelCase__   :Union[str, Any]												= [
							    [-1 for i in range(total + 1							)] for j in range(2 ** len(UpperCamelCase_							)							)
							]

							UpperCamelCase__   :str												= defaultdict(UpperCamelCase_							)  # stores the list of persons for each task

							# final_mask is used to check if all persons are included by setting all bits
							# to 1
							UpperCamelCase__   :Optional[int]												= (1 << len(UpperCamelCase_							)) - 1




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							if mask == self.final_mask:
												return 1

							# if not everyone gets the task and no more tasks are available, return 0
							if task_no > self.total_tasks:
												return 0

							# if case already considered
							if self.dp[mask][task_no] != -1:
												return self.dp[mask][task_no]

							# Number of ways when we don't this task in the arrangement
							UpperCamelCase__   :str												= self.count_ways_until(UpperCamelCase_    ,   task_no + 1							)

							# now assign the tasks one by one to all possible persons and recursively
							# assign for the remaining tasks.
							if task_no in self.task:
												for p in self.task[task_no]:
																	# if p is already given a task
																	if mask & (1 << p):
																						continue

																	# assign this task to p and change the mask value. And recursively
																	# assign tasks with the new mask value.
																	total_ways_util += self.count_ways_until(mask | (1 << p)    ,   task_no + 1							)

        # save the value.
							UpperCamelCase__   :Optional[int]												= total_ways_util

							return self.dp[mask][task_no]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							for i in range(len(UpperCamelCase_							)							):
												for j in task_performed[i]:
																	self.task[j].append(UpperCamelCase_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
							return self.count_ways_until(0    ,   1							)


if __name__ == "__main__":
		__snake_case							      =     5  # total no of tasks (the value of N)

		# the list of tasks that can be done by M persons.
		__snake_case							      =     [[1, 3, 4], [1, 2, 5], [3, 4]]
		print(
		    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
		        task_performed
		    )
		)