




import heapq
import sys

import numpy as np

_lowerCamelCase			:	List[str]							 =     tuple[int, int]


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Optional[int])		->Dict:





           '''simple docstring'''

           A__   				=					[]
           A__   				=					set()


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           if not self.empty():
                  return self.elements[0][0]
           else:
                  return float('''inf''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[Any]:





           '''simple docstring'''

           return len(self.elements) == 0


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





           '''simple docstring'''

           if item not in self.set:
                  heapq.heappush(self.elements      ,				(priority, item))
                  self.set.add(_lowerCAmelCase)
           else:
                  # update
                  # print("update", item)
                  A__   				=					[]
                  ((A__)				,							(A__))   				=					heapq.heappop(self.elements)
                  while x != item:
                         temp.append((pri, x))
                         ((A__)				,							(A__))   				=					heapq.heappop(self.elements)
                  temp.append((priority, item))
                  for pro, xxx in temp:
                         heapq.heappush(self.elements      ,				(pro, xxx))


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





           '''simple docstring'''

           if item in self.set:
                  self.set.remove(_lowerCAmelCase)
                  A__   				=					[]
                  ((A__)				,							(A__))   				=					heapq.heappop(self.elements)
                  while x != item:
                         temp.append((pro, x))
                         ((A__)				,							(A__))   				=					heapq.heappop(self.elements)
                  for prito, yyy in temp:
                         heapq.heappush(self.elements      ,				(prito, yyy))


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[Any]:





           '''simple docstring'''

           return self.elements[0][1]





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->List[Any]:





           '''simple docstring'''

           ((A__)				,							(A__))   				=					heapq.heappop(self.elements)
           self.set.remove(_lowerCAmelCase)
           return (priority, item)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.array(lowercase_		)
       A__   				=					np.array(lowercase_		)
       return np.linalg.norm(a - b		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Any:



       """simple docstring"""

       return consistent_heuristic(lowercase_				,       lowercase_		) // t







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       return abs(p[0] - goal[0]		) + abs(p[1] - goal[1]		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       A__   				=					g_function[start] + Wa * heuristics[i](lowercase_				,       lowercase_		)
       return ans







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Any:



       """simple docstring"""

       A__   				=					np.chararray((n, n)		)
       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     A__   				=					'''*'''

       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     if (j, (n - 1) - i) in blocks:
                            A__   				=					'''#'''

       A__   				=					'''-'''
       A__   				=					back_pointer[goal]
       while x != start:
              ((A__)				,							(A__))   				=					x
              # print(x)
              A__   				=					'''-'''
              A__   				=					back_pointer[x]
       A__   				=					'''-'''

       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     if (i, j) == (0, n - 1):
                            print(grid[i][j]				,       end=''' '''		)
                            print('''<-- End position'''				,       end=''' '''		)
                     else:
                            print(grid[i][j]				,       end=''' '''		)
              print()
       print('''^'''		)
       print('''Start position'''		)
       print()
       print('''# is an obstacle'''		)
       print('''- is the path taken by algorithm'''		)
       print('''PATH TAKEN BY THE ALGORITHM IS:-'''		)
       A__   				=					back_pointer[goal]
       while x != start:
              print(lowercase_				,       end=''' '''		)
              A__   				=					back_pointer[x]
       print(lowercase_		)
       sys.exit()







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       if p[0] < 0 or p[0] > n - 1:
              return False
       if p[1] < 0 or p[1] > n - 1:
              return False
       return True







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       )     -> Union[str, Any]:



       """simple docstring"""

       for itera in range(lowercase_		):
              open_list[itera].remove_element(lowercase_		)
       # print("s", s)
       # print("j", j)
       ((A__)				,							(A__))   				=					s
       A__   				=					(x - 1, y)
       A__   				=					(x + 1, y)
       A__   				=					(x, y + 1)
       A__   				=					(x, y - 1)

       for neighbours in [left, right, up, down]:
              if neighbours not in blocks:
                     if valid(lowercase_		) and neighbours not in visited:
                            # print("neighbour", neighbours)
                            visited.add(lowercase_		)
                            A__   				=					-1
                            A__   				=					float('''inf'''		)

                     if valid(lowercase_		) and g_function[neighbours] > g_function[s] + 1:
                            A__   				=					g_function[s] + 1
                            A__   				=					s
                            if neighbours not in close_list_anchor:
                                   open_list[0].put(lowercase_				,       key(lowercase_				,       0				,       lowercase_				,       lowercase_		)		)
                                   if neighbours not in close_list_inad:
                                          for var in range(1				,       lowercase_		):
                                                 if key(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		) <= Wa * key(
                                                     lowercase_				,       0				,       lowercase_				,       lowercase_		):
                                                        open_list[j].put(
                                                            lowercase_				,       key(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)		)







def SCREAMING_SNAKE_CASE       ( )     -> List[Any]:



       """simple docstring"""

       A__   				=					[]
       for x in range(1				,       5		):
              for y in range(1				,       6		):
                     some_list.append((x, y)		)

       for x in range(15				,       20		):
              some_list.append((x, 17)		)

       for x in range(10				,       19		):
              for y in range(1				,       15		):
                     some_list.append((x, y)		)

    # L block
       for x in range(1				,       4		):
              for y in range(12				,       19		):
                     some_list.append((x, y)		)
       for x in range(3				,       13		):
              for y in range(16				,       19		):
                     some_list.append((x, y)		)
       return some_list


_lowerCamelCase			:	Optional[int]							 =     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCamelCase			:	Optional[Any]							 =     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
_lowerCamelCase			:	Optional[Any]							 =     make_common_ground()


_lowerCamelCase			:	Dict							 =     blocks_blk
# hyper parameters
_lowerCamelCase			:	Union[str, Any]							 =     1
_lowerCamelCase			:	Union[str, Any]							 =     1
_lowerCamelCase			:	Optional[int]							 =     20
_lowerCamelCase			:	List[str]							 =     3  # one consistent and two other inconsistent

# start and end destination
_lowerCamelCase			:	str							 =     (0, 0)
_lowerCamelCase			:	str							 =     (n - 1, n - 1)

_lowerCamelCase			:	Optional[Any]							 =     1







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					{start: 0, goal: float('''inf'''		)}
       A__   				=					{start: -1, goal: -1}
       A__   				=					[]
       A__   				=					set()

       for i in range(lowercase_		):
              open_list.append(PriorityQueue()		)
              open_list[i].put(lowercase_				,       key(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)		)

       A__   				=					[]
       A__   				=					[]
       while open_list[0].minkey() < float('''inf'''		):
              for i in range(1				,       lowercase_		):
                     # print(open_list[0].minkey(), open_list[i].minkey())
                     if open_list[i].minkey() <= Wa * open_list[0].minkey():
                            global t
                            t += 1
                            if g_function[goal] <= open_list[i].minkey():
                                   if g_function[goal] < float('''inf'''		):
                                          do_something(lowercase_				,       lowercase_				,       lowercase_		)
                            else:
                                   A__				,							A__   				=					open_list[i].top_show()
                                   visited.add(lowercase_		)
                                   expand_state(
                                       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       )
                                   close_list_inad.append(lowercase_		)
                     else:
                            if g_function[goal] <= open_list[0].minkey():
                                   if g_function[goal] < float('''inf'''		):
                                          do_something(lowercase_				,       lowercase_				,       lowercase_		)
                            else:
                                   A__   				=					open_list[0].top_show()
                                   visited.add(lowercase_		)
                                   expand_state(
                                       lowercase_				,       0				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       )
                                   close_list_anchor.append(lowercase_		)
       print('''No path found to goal'''		)
       print()
       for i in range(n - 1				,       -1				,       -1		):
              for j in range(lowercase_		):
                     if (j, i) in blocks:
                            print('''#'''				,       end=''' '''		)
                     elif (j, i) in back_pointer:
                            if (j, i) == (n - 1, n - 1):
                                   print('''*'''				,       end=''' '''		)
                            else:
                                   print('''-'''				,       end=''' '''		)
                     else:
                            print('''*'''				,       end=''' '''		)
                     if (j, i) == (n - 1, n - 1):
                            print('''<-- End position'''				,       end=''' '''		)
              print()
       print('''^'''		)
       print('''Start position'''		)
       print()
       print('''# is an obstacle'''		)
       print('''- is the path taken by algorithm'''		)


if __name__ == "__main__":
  multi_a_star(start, goal, n_heuristic)








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			:	str							 =     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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					state_dict.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       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[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					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
              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[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
              # 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[:256, :]
              A__   				=					in_proj_bias_cross_attn[:256]
              A__   				=					in_proj_weight_cross_attn[256:512, :]
              A__   				=					in_proj_bias_cross_attn[256:512]
              A__   				=					in_proj_weight_cross_attn[-256:, :]
              A__   				=					in_proj_bias_cross_attn[-256:]







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       A__				,							A__   				=					image.size
       A__   				=					max(lowercase_				,       lowercase_		)
       A__   				=					800 if '''detection''' in checkpoint_url else 1_000
       A__   				=					target_max_size / current_max_size
       A__   				=					image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

       return resized_image







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					F.to_tensor(lowercase_		)
       A__   				=					F.normalize(lowercase_				,       mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]		)
       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

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

       # load original state dict
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''		)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       A__   				=					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
       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(lowercase_		)
                     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__   				=					125
              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=800 if '''detection''' in checkpoint_url else 1_000		)
       A__   				=					TableTransformerForObjectDetection(lowercase_		)
       model.load_state_dict(lowercase_		)
       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=lowercase_		)
       A__   				=					Image.open(lowercase_		).convert('''RGB'''		)
       A__   				=					normalize(resize(lowercase_				,       lowercase_		)		).unsqueeze(0		)

       A__   				=					model(lowercase_		)

       if "detection" in checkpoint_url:
              A__   				=					(1, 15, 3)
              A__   				=					torch.tensor(
                  [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]		)
              A__   				=					torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]		)

       else:
              A__   				=					(1, 125, 7)
              A__   				=					torch.tensor(
                  [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]		)
              A__   				=					torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]		)

       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...'''		)
              A__   				=					(
                  '''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__":
  _lowerCamelCase			:	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."""
  )
  _lowerCamelCase			:	str							 =     parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


