



'''simple docstring'''


import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

_lowerCamelCase	:       Optional[int]      	=	(
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

_lowerCamelCase	:       Union[str, Any]      	=	(
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

_lowerCamelCase	:       Dict      	=	(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

_lowerCamelCase	:       Dict      	=	(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

_lowerCamelCase	:       Optional[Any]      	=	(
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

_lowerCamelCase	:       List[Any]      	=	(
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

_lowerCamelCase	:       List[str]      	=	(
    ("JH AH TH KH QH", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)







def 				__lowerCamelCase				(   )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  randrange(len(A__    )    ), randrange(len(A__    )    )
   UpperCamelCase					=  ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
   UpperCamelCase				,      UpperCamelCase					=  SORTED_HANDS[play], SORTED_HANDS[oppo]
   return hand, other, expected







def 				__lowerCamelCase				(   A__ = 100    )       ->	Optional[Any]:






   """simple docstring"""
   return (generate_random_hand() for _ in range(A__    ))







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_flush() == expected







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_straight() == expected







@pytest.mark.parametrize('hand, expected, card_values'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  PokerHand(A__    )
   assert player._is_five_high_straight() == expected
   assert player._card_values == card_values







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   assert PokerHand(A__    )._is_same_kind() == expected







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	str:






   """simple docstring"""
   assert PokerHand(A__    )._hand_type == expected







@pytest.mark.parametrize('hand, other, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







@pytest.mark.parametrize('hand, other, expected'    ,						generate_random_hands()    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







def 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  [PokerHand(A__    ) for hand in SORTED_HANDS]
   UpperCamelCase					=  poker_hands.copy()
   shuffle(A__    )
   UpperCamelCase					=  chain(sorted(A__    )    )
   for index, hand in enumerate(A__    ):
      assert hand == poker_hands[index]







def 				__lowerCamelCase				(   )       ->	Optional[int]:






   """simple docstring"""
   # Test that five high straights are compared correctly.
   UpperCamelCase					=  [PokerHand('2D AC 3H 4H 5S'    ), PokerHand('2S 3H 4H 5S 6C'    )]
   pokerhands.sort(reverse=A__    )
   assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"







def 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   # Multiple calls to five_high_straight function should still return True
   # and shouldn't mutate the list in every call other than the first.
   UpperCamelCase					=  PokerHand('2C 4S AS 3D 5C'    )
   UpperCamelCase					=  True
   UpperCamelCase					=  [5, 4, 3, 2, 14]
   for _ in range(10    ):
      assert pokerhand._is_five_high_straight() == expected
      assert pokerhand._card_values == expected_card_values







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   # Problem number 54 from Project Euler
   # Testing from poker_hands.txt file
   UpperCamelCase					=  0
   UpperCamelCase					=  os.path.abspath(os.path.dirname(A__    )    )
   UpperCamelCase					=  os.path.join(A__    ,						'poker_hands.txt'    )
   with open(A__    ) as file_hand:
      for line in file_hand:
         UpperCamelCase					=  line[:14].strip()
         UpperCamelCase					=  line[15:].strip()
         UpperCamelCase				,      UpperCamelCase					=  PokerHand(A__    ), PokerHand(A__    )
         UpperCamelCase					=  player.compare_with(A__    )
         if output == "Win":
            answer += 1
   assert answer == 376






'''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	:       int      	=	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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  state_dict.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    )       ->	int:






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

   return new_state_dict







def 				__lowerCamelCase				(   A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  ''

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






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

   return resized_image







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  F.to_tensor(A__    )
   UpperCamelCase					=  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__    ,						A__    ,						A__    )       ->	Optional[Any]:






   """simple docstring"""
   logger.info('Converting model...'    )

   # load original state dict
   UpperCamelCase					=  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					=  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					=  'model.'
   for key in state_dict.copy().keys():
      if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
         UpperCamelCase					=  state_dict.pop(A__    )
         UpperCamelCase					=  val
    # create HuggingFace model and load state dict
   UpperCamelCase					=  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					=  15
      UpperCamelCase					=  2
      UpperCamelCase					=  {0: 'table', 1: 'table rotated'}
      UpperCamelCase					=  idalabel
      UpperCamelCase					=  {v: k for k, v in idalabel.items()}
   else:
      UpperCamelCase					=  125
      UpperCamelCase					=  6
      UpperCamelCase					=  {
          0: 'table',
          1: 'table column',
          2: 'table row',
          3: 'table column header',
          4: 'table projected row header',
          5: 'table spanning cell',
      }
      UpperCamelCase					=  idalabel
      UpperCamelCase					=  {v: k for k, v in idalabel.items()}

   UpperCamelCase					=  DetrImageProcessor(
       format='coco_detection'    ,						max_size=800 if 'detection' in checkpoint_url else 1_000    )
   UpperCamelCase					=  TableTransformerForObjectDetection(A__    )
   model.load_state_dict(A__    )
   model.eval()

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

   UpperCamelCase					=  model(A__    )

   if "detection" in checkpoint_url:
      UpperCamelCase					=  (1, 15, 3)
      UpperCamelCase					=  torch.tensor(
          [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]    )
      UpperCamelCase					=  torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]    )

   else:
      UpperCamelCase					=  (1, 125, 7)
      UpperCamelCase					=  torch.tensor(
          [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]    )
      UpperCamelCase					=  torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]    )

   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					=  (
          '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	:       List[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	:       int      	=	parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

