




import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
     from transformers.models.esm.modeling_esm import (
         ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
         EsmEmbeddings,
         create_position_ids_from_input_ids,
     )

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=33       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		List[Any]:
         UpperCAmelCase_      :							Optional[Any]       =      parent
         UpperCAmelCase_      :							Union[str, Any]       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							Any       =      is_training
         UpperCAmelCase_      :							Optional[Any]       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Any       =      use_labels
         UpperCAmelCase_      :							Union[str, Any]       =      vocab_size
         UpperCAmelCase_      :							List[Any]       =      hidden_size
         UpperCAmelCase_      :							List[str]       =      num_hidden_layers
         UpperCAmelCase_      :							Optional[Any]       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							str       =      max_position_embeddings
         UpperCAmelCase_      :							Optional[Any]       =      type_vocab_size
         UpperCAmelCase_      :							int       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[Any]       =      initializer_range
         UpperCAmelCase_      :							int       =      num_labels
         UpperCAmelCase_      :							Tuple       =      num_choices
         UpperCAmelCase_      :							Any       =      scope





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Dict       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							int       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							Optional[int]       =      random_attention_mask([self.batch_size, self.seq_length]  )

         UpperCAmelCase_      :							List[Any]       =      None
         UpperCAmelCase_      :							int       =      None
         UpperCAmelCase_      :							Union[str, Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							Union[str, Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[Any]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							List[Any]       =      self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 							a__				(				self  )			->		Optional[int]:
         return EsmConfig(
             vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,pad_token_id=1       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							Tuple       =      EsmModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      model(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      model(_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape       ,(self.batch_size, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Any       =      EsmForMaskedLM(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Tuple       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      self.num_labels
         UpperCAmelCase_      :							Tuple       =      EsmForTokenClassification(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							str       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							List[Any]       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Optional[Any]       =      config_and_inputs
         UpperCAmelCase_      :							str       =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				False

    lowerCAmelCase        =				(
        (
            EsmForMaskedLM,
            EsmModel,
            EsmForSequenceClassification,
            EsmForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				()
    lowerCAmelCase        =				(
        {
            '''feature-extraction''': EsmModel,
            '''fill-mask''': EsmForMaskedLM,
            '''text-classification''': EsmForSequenceClassification,
            '''token-classification''': EsmForTokenClassification,
            '''zero-shot''': EsmForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase        =				True





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Optional[int]       =      EsmModelTester(self  )
         UpperCAmelCase_      :							int       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		int:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_      :							List[str]       =      type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		int:
         for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							Union[str, Any]       =      EsmModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_      :							Optional[Any]       =      EsmEmbeddings(config=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      torch.as_tensor([[12, 31, 13, model.padding_idx]]  )
         UpperCAmelCase_      :							Optional[int]       =      torch.as_tensor(
             [
                 [
                     0 + model.padding_idx + 1,
                     1 + model.padding_idx + 1,
                     2 + model.padding_idx + 1,
                     model.padding_idx,
                 ]
             ]  )
         UpperCAmelCase_      :							Optional[int]       =      create_position_ids_from_input_ids(_SCREAMING_SNAKE_CASE       ,model.padding_idx  )
         self.assertEqual(position_ids.shape       ,expected_positions.shape  )
         self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_      :							Any       =      EsmEmbeddings(config=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      torch.empty(2       ,4       ,30  )
         UpperCAmelCase_      :							Tuple       =      [
             0 + embeddings.padding_idx + 1,
             1 + embeddings.padding_idx + 1,
             2 + embeddings.padding_idx + 1,
             3 + embeddings.padding_idx + 1,
         ]
         UpperCAmelCase_      :							int       =      torch.as_tensor([expected_single_positions, expected_single_positions]  )
         UpperCAmelCase_      :							Union[str, Any]       =      embeddings.create_position_ids_from_inputs_embeds(_SCREAMING_SNAKE_CASE  )
         self.assertEqual(position_ids.shape       ,expected_positions.shape  )
         self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )  )





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		int:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		Union[str, Any]:
         pass


@require_torch
class   __a(					_a       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		Tuple:
         with torch.no_grad():
              UpperCAmelCase_      :							Tuple       =      EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''  )
              model.eval()
              UpperCAmelCase_      :							List[Any]       =      torch.tensor([[0, 1, 2, 3, 4, 5]]  )
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE  )[0]

              UpperCAmelCase_      :							Union[str, Any]       =      33

              UpperCAmelCase_      :							int       =      torch.Size((1, 6, vocab_size)  )
              self.assertEqual(output.shape       ,_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							str       =      torch.tensor(
                  [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]  )
              self.assertTrue(torch.allclose(output[:, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





    @slow
    def 							a__				(				self  )			->		Tuple:
         with torch.no_grad():
              UpperCAmelCase_      :							str       =      EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''  )
              model.eval()

              UpperCAmelCase_      :							Dict       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
              UpperCAmelCase_      :							Optional[Any]       =      model(_SCREAMING_SNAKE_CASE  )[0]
              # compare the actual values for a slice.
              UpperCAmelCase_      :							Any       =      torch.tensor(
                  [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]  )
              self.assertTrue(torch.allclose(output[:, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





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)