




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__a          =			logging.get_logger(__name__)


__a          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

__a          =			{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

__a          =			{
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase__       (      ):

					'''simple docstring'''


					UpperCAmelCase_      :							int       =      (
					    list(range(ord('''!'''				)			,						ord('''~'''				) + 1				)				) + list(range(ord('''¡'''				)			,						ord('''¬'''				) + 1				)				) + list(range(ord('''®'''				)			,						ord('''ÿ'''				) + 1				)				)
					)
					UpperCAmelCase_      :							Any       =      bs[:]
					UpperCAmelCase_      :							int       =      0
					for b in range(2**8				):
										if b not in bs:
															bs.append(_lowercase				)
															cs.append(2**8 + n				)
															n += 1
					UpperCAmelCase_      :							int       =      [chr(_lowercase				) for n in cs]
					return dict(zip(_lowercase			,						_lowercase				)				)







def lowerCamelCase__       (      _lowercase				):

					'''simple docstring'''


					UpperCAmelCase_      :							Dict       =      set()
					UpperCAmelCase_      :							int       =      word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)				)
										UpperCAmelCase_      :							Union[str, Any]       =      char
					return pairs



class   __a(					_a       ):






				"""simple docstring"""





				lowerCAmelCase        =				VOCAB_FILES_NAMES
				lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





				def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE="replace"       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="<mask>"       ,_SCREAMING_SNAKE_CASE=False       ,**_SCREAMING_SNAKE_CASE       ,)			->		Tuple:
									UpperCAmelCase_      :							Union[str, Any]       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else bos_token
									UpperCAmelCase_      :							Any       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else eos_token
									UpperCAmelCase_      :							Optional[int]       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else sep_token
									UpperCAmelCase_      :							Optional[Any]       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else cls_token
									UpperCAmelCase_      :							Any       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else unk_token
									UpperCAmelCase_      :							int       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									UpperCAmelCase_      :							str       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else mask_token

									super().__init__(
									    errors=_SCREAMING_SNAKE_CASE       ,bos_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,sep_token=_SCREAMING_SNAKE_CASE       ,cls_token=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,add_prefix_space=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)

									with open(_SCREAMING_SNAKE_CASE       ,encoding='''utf-8'''  ) as vocab_handle:
														UpperCAmelCase_      :							Tuple       =      json.load(_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							int       =      {v: k for k, v in self.encoder.items()}
									UpperCAmelCase_      :							Optional[int]       =      errors  # how to handle errors in decoding
									UpperCAmelCase_      :							Optional[int]       =      bytes_to_unicode()
									UpperCAmelCase_      :							Dict       =      {v: k for k, v in self.byte_encoder.items()}
									with open(_SCREAMING_SNAKE_CASE       ,encoding='''utf-8'''  ) as merges_handle:
														UpperCAmelCase_      :							Any       =      merges_handle.read().split('''\n'''  )[1:-1]
									UpperCAmelCase_      :							Tuple       =      [tuple(merge.split()  ) for merge in bpe_merges]
									UpperCAmelCase_      :							Optional[Any]       =      dict(zip(_SCREAMING_SNAKE_CASE       ,range(len(_SCREAMING_SNAKE_CASE  )  )  )  )
									UpperCAmelCase_      :							List[str]       =      {}
									UpperCAmelCase_      :							str       =      add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									UpperCAmelCase_      :							Any       =      re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''  )





				@property
				def 							a__				(				self  )			->		int:
									return len(self.encoder  )





				def 							a__				(				self  )			->		Optional[Any]:
									return dict(self.encoder       ,**self.added_tokens_encoder  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
									if token in self.cache:
														return self.cache[token]
									UpperCAmelCase_      :							int       =      tuple(_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							Any       =      get_pairs(_SCREAMING_SNAKE_CASE  )

									if not pairs:
														return token

									while True:
														UpperCAmelCase_      :							Dict       =      min(_SCREAMING_SNAKE_CASE       ,key=lambda _SCREAMING_SNAKE_CASE  : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE       ,float('''inf'''  )  )  )
														if bigram not in self.bpe_ranks:
																			break
														UpperCAmelCase_      :							Optional[int]       =      bigram
														UpperCAmelCase_      :							Optional[int]       =      []
														UpperCAmelCase_      :							Any       =      0
														while i < len(_SCREAMING_SNAKE_CASE  ):
																			try:
																								UpperCAmelCase_      :							Dict       =      word.index(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
																			except ValueError:
																								new_word.extend(word[i:]  )
																								break
																			else:
																								new_word.extend(word[i:j]  )
																								UpperCAmelCase_      :							Tuple       =      j

																			if word[i] == first and i < len(_SCREAMING_SNAKE_CASE  ) - 1 and word[i + 1] == second:
																								new_word.append(first + second  )
																								i += 2
																			else:
																								new_word.append(word[i]  )
																								i += 1
														UpperCAmelCase_      :							List[Any]       =      tuple(_SCREAMING_SNAKE_CASE  )
														UpperCAmelCase_      :							Union[str, Any]       =      new_word
														if len(_SCREAMING_SNAKE_CASE  ) == 1:
																			break
														else:
																			UpperCAmelCase_      :							Optional[int]       =      get_pairs(_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							Dict       =      ''' '''.join(_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							List[Any]       =      word
									return word





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
									UpperCAmelCase_      :							Dict       =      []
									for token in re.findall(self.pat       ,_SCREAMING_SNAKE_CASE  ):
														UpperCAmelCase_      :							Any       =      ''''''.join(
														    self.byte_encoder[b] for b in token.encode('''utf-8'''  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
														bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE  ).split(''' '''  )  )
									return bpe_tokens





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
									return self.encoder.get(_SCREAMING_SNAKE_CASE       ,self.encoder.get(self.unk_token  )  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
									return self.decoder.get(_SCREAMING_SNAKE_CASE  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
									UpperCAmelCase_      :							Optional[Any]       =      ''''''.join(_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							Union[str, Any]       =      bytearray([self.byte_decoder[c] for c in text]  ).decode('''utf-8'''       ,errors=self.errors  )
									return text





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
									if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
														return
									UpperCAmelCase_      :							Dict       =      os.path.join(
									    _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
									UpperCAmelCase_      :							Tuple       =      os.path.join(
									    _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

									with open(_SCREAMING_SNAKE_CASE       ,'''w'''       ,encoding='''utf-8'''  ) as f:
														f.write(json.dumps(self.encoder       ,indent=2       ,sort_keys=_SCREAMING_SNAKE_CASE       ,ensure_ascii=_SCREAMING_SNAKE_CASE  ) + '''\n'''  )

									UpperCAmelCase_      :							Optional[Any]       =      0
									with open(_SCREAMING_SNAKE_CASE       ,'''w'''       ,encoding='''utf-8'''  ) as writer:
														writer.write('''#version: 0.2\n'''  )
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,key=lambda _SCREAMING_SNAKE_CASE  : kv[1]  ):
																			if index != token_index:
																								logger.warning(
																								    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																								    ''' Please check that the tokenizer is not corrupted!'''  )
																								UpperCAmelCase_      :							List[str]       =      token_index
																			writer.write(''' '''.join(_SCREAMING_SNAKE_CASE  ) + '''\n'''  )
																			index += 1

									return vocab_file, merge_file





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		List[int]:
									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									UpperCAmelCase_      :							Dict       =      [self.cls_token_id]
									UpperCAmelCase_      :							Union[str, Any]       =      [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:
									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_SCREAMING_SNAKE_CASE       ,token_ids_a=_SCREAMING_SNAKE_CASE       ,already_has_special_tokens=_SCREAMING_SNAKE_CASE  )

									if token_ids_a is None:
														return [1] + ([0] * len(_SCREAMING_SNAKE_CASE  )) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE  )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE  )) + [1]





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		List[int]:
									UpperCAmelCase_      :							Tuple       =      [self.sep_token_id]
									UpperCAmelCase_      :							List[Any]       =      [self.cls_token_id]

									if token_ids_a is None:
														return len(cls + token_ids_a + sep  ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
									UpperCAmelCase_      :							str       =      kwargs.pop('''add_prefix_space'''       ,self.add_prefix_space  )
									if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE  ) > 0 and not text[0].isspace()):
														UpperCAmelCase_      :							List[Any]       =      ''' ''' + text
									return (text, kwargs)





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()
__a          =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a          =			[]
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__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      state_dict.pop(_lowercase				)
     UpperCAmelCase_      :							Optional[int]       =      val







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_      :							List[Any]       =      key.replace('''backbone.0.body'''			,						'''backbone.conv_encoder.model'''				)
               UpperCAmelCase_      :							Optional[Any]       =      value
          else:
               UpperCAmelCase_      :							Union[str, Any]       =      value

     return new_state_dict







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      ''''''

     # 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_      :							Dict       =      state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
          UpperCAmelCase_      :							Any       =      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_      :							Union[str, Any]       =      in_proj_weight[:256, :]
          UpperCAmelCase_      :							Optional[int]       =      in_proj_bias[:256]
          UpperCAmelCase_      :							Tuple       =      in_proj_weight[256:512, :]
          UpperCAmelCase_      :							List[Any]       =      in_proj_bias[256:512]
          UpperCAmelCase_      :							Union[str, Any]       =      in_proj_weight[-256:, :]
          UpperCAmelCase_      :							str       =      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_      :							Any       =      state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
          UpperCAmelCase_      :							Optional[int]       =      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_      :							Union[str, Any]       =      in_proj_weight[:256, :]
          UpperCAmelCase_      :							List[str]       =      in_proj_bias[:256]
          UpperCAmelCase_      :							Optional[int]       =      in_proj_weight[256:512, :]
          UpperCAmelCase_      :							str       =      in_proj_bias[256:512]
          UpperCAmelCase_      :							Optional[Any]       =      in_proj_weight[-256:, :]
          UpperCAmelCase_      :							Union[str, Any]       =      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_      :							List[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_      :							List[str]       =      in_proj_weight_cross_attn[:256, :]
          UpperCAmelCase_      :							str       =      in_proj_bias_cross_attn[:256]
          UpperCAmelCase_      :							int       =      in_proj_weight_cross_attn[256:512, :]
          UpperCAmelCase_      :							Tuple       =      in_proj_bias_cross_attn[256:512]
          UpperCAmelCase_      :							List[str]       =      in_proj_weight_cross_attn[-256:, :]
          UpperCAmelCase_      :							Dict       =      in_proj_bias_cross_attn[-256:]







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      image.size
     UpperCAmelCase_      :							List[Any]       =      max(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      800 if '''detection''' in checkpoint_url else 1000
     UpperCAmelCase_      :							Any       =      target_max_size / current_max_size
     UpperCAmelCase_      :							Any       =      image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

     return resized_image







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      F.to_tensor(_lowercase				)
     UpperCAmelCase_      :							Optional[Any]       =      F.normalize(_lowercase			,						mean=[0.485, 0.456, 0.406]			,						std=[0.229, 0.224, 0.225]				)
     return image







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


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

     # load original state dict
     UpperCAmelCase_      :							Any       =      torch.hub.load_state_dict_from_url(_lowercase			,						map_location='''cpu'''				)
     # rename keys
     for src, dest in rename_keys:
          rename_key(_lowercase			,						_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							Optional[int]       =      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
     UpperCAmelCase_      :							int       =      '''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
               UpperCAmelCase_      :							int       =      state_dict.pop(_lowercase				)
               UpperCAmelCase_      :							Dict       =      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_      :							Any       =      15
          UpperCAmelCase_      :							List[str]       =      2
          UpperCAmelCase_      :							Union[str, Any]       =      {0: '''table''', 1: '''table rotated'''}
          UpperCAmelCase_      :							str       =      idalabel
          UpperCAmelCase_      :							Optional[int]       =      {v: k for k, v in idalabel.items()}
     else:
          UpperCAmelCase_      :							Optional[Any]       =      125
          UpperCAmelCase_      :							Optional[Any]       =      6
          UpperCAmelCase_      :							Dict       =      {
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          UpperCAmelCase_      :							Optional[Any]       =      idalabel
          UpperCAmelCase_      :							Optional[int]       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Union[str, Any]       =      DetrImageProcessor(
         format='''coco_detection'''			,						max_size=800 if '''detection''' in checkpoint_url else 1000				)
     UpperCAmelCase_      :							Union[str, Any]       =      TableTransformerForObjectDetection(_lowercase				)
     model.load_state_dict(_lowercase				)
     model.eval()

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

     UpperCAmelCase_      :							Dict       =      model(_lowercase				)

     if "detection" in checkpoint_url:
          UpperCAmelCase_      :							Any       =      (1, 15, 3)
          UpperCAmelCase_      :							Optional[int]       =      torch.tensor(
              [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]				)
          UpperCAmelCase_      :							Any       =      torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]				)

     else:
          UpperCAmelCase_      :							Any       =      (1, 125, 7)
          UpperCAmelCase_      :							Any       =      torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]				)
          UpperCAmelCase_      :							str       =      torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]				)

     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...'''				)
          UpperCAmelCase_      :							List[Any]       =      (
              '''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__":
     __a          =			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.'
     )
     __a          =			parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)