




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__a          =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__a          =			'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'

class   __a(					unittest.TestCase       ):






				"""simple docstring"""





				def 							a__				(				self  )			->		int:
									UpperCAmelCase_      :							str       =      tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir       ,'''models/bert/'''  )  )
									UpperCAmelCase_      :							Optional[int]       =      self.transformer_dir
									shutil.copy(
									    os.path.join(_SCREAMING_SNAKE_CASE       ,'''src/transformers/models/bert/modeling_bert.py'''  )       ,os.path.join(self.transformer_dir       ,'''models/bert/modeling_bert.py'''  )       ,)





				def 							a__				(				self  )			->		Any:
									UpperCAmelCase_      :							Optional[Any]       =      'src/transformers'
									shutil.rmtree(self.transformer_dir  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		Union[str, Any]:
									UpperCAmelCase_      :							Tuple       =      comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
									if overwrite_result is not None:
														UpperCAmelCase_      :							List[str]       =      comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
									UpperCAmelCase_      :							Optional[Any]       =      black.Mode(target_versions={black.TargetVersion.PYaa}       ,line_length=119  )
									UpperCAmelCase_      :							Any       =      black.format_str(_SCREAMING_SNAKE_CASE       ,mode=_SCREAMING_SNAKE_CASE  )
									UpperCAmelCase_      :							Union[str, Any]       =      os.path.join(self.transformer_dir       ,'''new_code.py'''  )
									with open(_SCREAMING_SNAKE_CASE       ,'''w'''       ,newline='''\n'''  ) as f:
														f.write(_SCREAMING_SNAKE_CASE  )
									if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(_SCREAMING_SNAKE_CASE  )  ) == 0  )
									else:
														check_copies.is_copy_consistent(f.name       ,overwrite=_SCREAMING_SNAKE_CASE  )
														with open(_SCREAMING_SNAKE_CASE       ,'''r'''  ) as f:
																			self.assertTrue(f.read()       ,_SCREAMING_SNAKE_CASE  )





				def 							a__				(				self  )			->		Union[str, Any]:
									UpperCAmelCase_      :							Any       =      check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''  )
									self.assertEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





				def 							a__				(				self  )			->		int:
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''       ,'''BertLMPredictionHead'''       ,REFERENCE_CODE + '''\n'''       ,)

									# With no empty line at the end
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''       ,'''BertLMPredictionHead'''       ,_SCREAMING_SNAKE_CASE       ,)

									# Copy consistency with rename
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''       ,'''TestModelLMPredictionHead'''       ,re.sub('''Bert'''       ,'''TestModel'''       ,_SCREAMING_SNAKE_CASE  )       ,)

									# Copy consistency with a really long name
									UpperCAmelCase_      :							List[str]       =      'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
									self.check_copy_consistency(
									    f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''       ,f'''{long_class_name}LMPredictionHead'''       ,re.sub('''Bert'''       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )       ,)

									# Copy consistency with overwrite
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''       ,'''TestModelLMPredictionHead'''       ,_SCREAMING_SNAKE_CASE       ,overwrite_result=re.sub('''Bert'''       ,'''TestModel'''       ,_SCREAMING_SNAKE_CASE  )       ,)





				def 							a__				(				self  )			->		Optional[Any]:
									UpperCAmelCase_      :							int       =      check_copies.LOCALIZED_READMES['README_zh-hans.md']

									UpperCAmelCase_      :							List[str]       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
									    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
									    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
									    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
									    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
									    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
									    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
									    ' method has been applied to compress GPT2 into'
									    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
									    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
									    ' Multilingual BERT into'
									    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
									    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
									    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
									    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
									    ' Luong, Quoc V. Le, Christopher D. Manning.'
									)
									UpperCAmelCase_      :							Any       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
									    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
									    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
									    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
									)
									UpperCAmelCase_      :							Tuple       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
									    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
									    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
									    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
									    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
									    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
									    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
									    ' method has been applied to compress GPT2 into'
									    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
									    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
									    ' Multilingual BERT into'
									    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
									    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
									    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
									    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
									    ' Christopher D. Manning 发布。\n'
									)

									UpperCAmelCase_      :							str       =      check_copies.convert_to_localized_md(
									    _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,localized_readme['''format_model_list''']  )

									self.assertFalse(_SCREAMING_SNAKE_CASE  )
									self.assertEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

									UpperCAmelCase_      :							Any       =      check_copies.convert_to_localized_md(
									    _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,localized_readme['''format_model_list''']  )

									# Check whether the number of models is equal to README.md after conversion.
									self.assertTrue(_SCREAMING_SNAKE_CASE  )

									UpperCAmelCase_      :							Optional[int]       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
									    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
									    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
									    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
									)
									UpperCAmelCase_      :							Any       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
									    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
									    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
									    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
									)
									UpperCAmelCase_      :							Any       =      (
									    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
									    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
									    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
									    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
									)

									UpperCAmelCase_      :							str       =      check_copies.convert_to_localized_md(
									    _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,localized_readme['''format_model_list''']  )

									# Check if the model link is synchronized.
									self.assertEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





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)