





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
         VideoMAEForPreTraining,
         VideoMAEForVideoClassification,
         VideoMAEModel,
     )
     from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VideoMAEImageProcessor



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   List[str]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Optional[Any]=13       , UpperCAmelCase_				:   Optional[Any]=10       , UpperCAmelCase_				:   Any=3       , UpperCAmelCase_				:   List[Any]=2       , UpperCAmelCase_				:   Optional[int]=2       , UpperCAmelCase_				:   Tuple=2       , UpperCAmelCase_				:   Union[str, Any]=True       , UpperCAmelCase_				:   List[Any]=True       , UpperCAmelCase_				:   int=32       , UpperCAmelCase_				:   Union[str, Any]=5       , UpperCAmelCase_				:   Any=4       , UpperCAmelCase_				:   Optional[int]=37       , UpperCAmelCase_				:   List[Any]="gelu"       , UpperCAmelCase_				:   Optional[int]=0.1       , UpperCAmelCase_				:   Dict=0.1       , UpperCAmelCase_				:   Optional[int]=10       , UpperCAmelCase_				:   Optional[int]=0.02       , UpperCAmelCase_				:   int=0.9       , UpperCAmelCase_				:   Any=None       , )       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =parent
   lowerCamelCase__:      Optional[Any]			  =batch_size
   lowerCamelCase__:      str			  =image_size
   lowerCamelCase__:      Optional[Any]			  =num_channels
   lowerCamelCase__:      Union[str, Any]			  =patch_size
   lowerCamelCase__:      Tuple			  =tubelet_size
   lowerCamelCase__:      Tuple			  =num_frames
   lowerCamelCase__:      Optional[int]			  =is_training
   lowerCamelCase__:      Dict			  =use_labels
   lowerCamelCase__:      Dict			  =hidden_size
   lowerCamelCase__:      List[str]			  =num_hidden_layers
   lowerCamelCase__:      Any			  =num_attention_heads
   lowerCamelCase__:      List[str]			  =intermediate_size
   lowerCamelCase__:      Optional[Any]			  =hidden_act
   lowerCamelCase__:      Dict			  =hidden_dropout_prob
   lowerCamelCase__:      Any			  =attention_probs_dropout_prob
   lowerCamelCase__:      Optional[int]			  =type_sequence_label_size
   lowerCamelCase__:      Any			  =initializer_range
   lowerCamelCase__:      Tuple			  =mask_ratio
   lowerCamelCase__:      str			  =scope

   # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
   lowerCamelCase__:      List[Any]			  =(image_size // patch_size) ** 2
   lowerCamelCase__:      Optional[Any]			  =(num_frames // tubelet_size) * self.num_patches_per_frame

   # use this variable to define bool_masked_pos
   lowerCamelCase__:      Union[str, Any]			  =int(mask_ratio * self.seq_length)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =floats_tensor(
       [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__:      List[Any]			  =None
   if self.use_labels:
     lowerCamelCase__:      Any			  =ids_tensor([self.batch_size]       , self.type_sequence_label_size)

   lowerCamelCase__:      List[str]			  =self.get_config()

   return config, pixel_values, labels

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''simple docstring'''



   return VideoMAEConfig(
       image_size=self.image_size       , patch_size=self.patch_size       , num_channels=self.num_channels       , num_frames=self.num_frames       , tubelet_size=self.tubelet_size       , hidden_size=self.hidden_size       , 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       , is_decoder=lowerCAmelCase__       , initializer_range=self.initializer_range       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Union[str, Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =VideoMAEModel(config=lowerCAmelCase__)
   model.to(lowerCAmelCase__)
   model.eval()
   lowerCamelCase__:      Tuple			  =model(lowerCAmelCase__)
   self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =VideoMAEForPreTraining(lowerCAmelCase__)
   model.to(lowerCAmelCase__)
   model.eval()
   # important: each video needs to have the same number of masked patches
   # hence we define a single mask, which we then repeat for each example in the batch
   lowerCamelCase__:      List[str]			  =torch.ones((self.num_masks,))
   lowerCamelCase__:      List[str]			  =torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
   lowerCamelCase__:      Union[str, Any]			  =mask.expand(self.batch_size       , -1).bool()

   lowerCamelCase__:      Any			  =model(lowerCAmelCase__       , lowerCAmelCase__)
   # model only returns predictions for masked patches
   lowerCamelCase__:      Union[str, Any]			  =mask.sum().item()
   lowerCamelCase__:      str			  =3 * self.tubelet_size * self.patch_size**2
   self.parent.assertEqual(result.logits.shape       , (self.batch_size, num_masked_patches, decoder_num_labels))







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.prepare_config_and_inputs()
   lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      str			  =config_and_inputs
   lowerCamelCase__:      str			  ={"pixel_values": pixel_values}
   return config, inputs_dict



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







 '''simple docstring'''
 lowercase_		=			(
     (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
 )
 lowercase_		=			(
     {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
     if is_torch_available()
     else {}
 )

 lowercase_		=			False
 lowercase_		=			False
 lowercase_		=			False
 lowercase_		=			False

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =VideoMAEModelTester(self)
   lowerCamelCase__:      int			  =ConfigTester(self       , config_class=lowerCAmelCase__       , has_text_modality=lowerCAmelCase__       , hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Tuple=False)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =copy.deepcopy(lowerCAmelCase__)

   if model_class == VideoMAEForPreTraining:
     # important: each video needs to have the same number of masked patches
     # hence we define a single mask, which we then repeat for each example in the batch
     lowerCamelCase__:      int			  =torch.ones((self.model_tester.num_masks,))
     lowerCamelCase__:      str			  =torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
     lowerCamelCase__:      Tuple			  =mask.expand(self.model_tester.batch_size       , -1).bool()
     lowerCamelCase__:      Optional[Any]			  =bool_masked_pos.to(lowerCAmelCase__)

   if return_labels:
     if model_class in [
         *get_values(lowerCAmelCase__),
     ]:
       lowerCamelCase__:      List[str]			  =torch.zeros(
           self.model_tester.batch_size       , dtype=torch.long       , device=lowerCAmelCase__)

   return inputs_dict

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->int:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="VideoMAE does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Any:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:      Optional[int]			  =model_class(lowerCAmelCase__)
     self.assertIsInstance(model.get_input_embeddings()       , (nn.Module))
     lowerCamelCase__:      Tuple			  =model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCAmelCase__       , nn.Linear))

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      int			  =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:      Any			  =model_class(lowerCAmelCase__)
     lowerCamelCase__:      Any			  =inspect.signature(model.forward)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCamelCase__:      Dict			  =[*signature.parameters.keys()]

     lowerCamelCase__:      List[Any]			  =["pixel_values"]
     self.assertListEqual(arg_names[:1]       , lowerCAmelCase__)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCAmelCase__)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__)

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->int:



   '''simple docstring'''



   for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:      Any			  =VideoMAEModel.from_pretrained(lowerCAmelCase__)
     self.assertIsNotNone(lowerCAmelCase__)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->int:



   '''simple docstring'''



   if not self.has_attentions:
     pass

   else:
     lowerCamelCase__  ,					lowerCamelCase__:      int			  =self.model_tester.prepare_config_and_inputs_for_common()
     lowerCamelCase__:      Union[str, Any]			  =True

     for model_class in self.all_model_classes:
       lowerCamelCase__:      str			  =self.model_tester.seq_length - self.model_tester.num_masks
       lowerCamelCase__:      Optional[Any]			  =(
           num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
       )

       lowerCamelCase__:      Union[str, Any]			  =True
       lowerCamelCase__:      Optional[Any]			  =False
       lowerCamelCase__:      Optional[Any]			  =True
       lowerCamelCase__:      Union[str, Any]			  =model_class(lowerCAmelCase__)
       model.to(lowerCAmelCase__)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__:      List[str]			  =model(**self._prepare_for_class(lowerCAmelCase__       , lowerCAmelCase__))
       lowerCamelCase__:      str			  =outputs.attentions
       self.assertEqual(len(lowerCAmelCase__)       , self.model_tester.num_hidden_layers)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       lowerCamelCase__:      str			  =True
       lowerCamelCase__:      Tuple			  =model_class(lowerCAmelCase__)
       model.to(lowerCAmelCase__)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__:      Dict			  =model(**self._prepare_for_class(lowerCAmelCase__       , lowerCAmelCase__))
       lowerCamelCase__:      Dict			  =outputs.attentions
       self.assertEqual(len(lowerCAmelCase__)       , self.model_tester.num_hidden_layers)

       self.assertListEqual(
           list(attentions[0].shape[-3:])       , [self.model_tester.num_attention_heads, seq_len, seq_len]       , )
       lowerCamelCase__:      Optional[Any]			  =len(lowerCAmelCase__)

       # Check attention is always last and order is fine
       lowerCamelCase__:      int			  =True
       lowerCamelCase__:      List[Any]			  =True
       lowerCamelCase__:      Dict			  =model_class(lowerCAmelCase__)
       model.to(lowerCAmelCase__)
       model.eval()
       with torch.no_grad():
         lowerCamelCase__:      Union[str, Any]			  =model(**self._prepare_for_class(lowerCAmelCase__       , lowerCAmelCase__))

       self.assertEqual(out_len + 1       , len(lowerCAmelCase__))

       lowerCamelCase__:      Optional[int]			  =outputs.attentions

       self.assertEqual(len(lowerCAmelCase__)       , self.model_tester.num_hidden_layers)
       self.assertListEqual(
           list(self_attentions[0].shape[-3:])       , [self.model_tester.num_attention_heads, seq_len, seq_len]       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Optional[Any]:



   '''simple docstring'''



   def check_hidden_states_output(UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   List[str]):
     lowerCamelCase__:      List[str]			  =model_class(lowerCAmelCase__)
     model.to(lowerCAmelCase__)
     model.eval()

     with torch.no_grad():
       lowerCamelCase__:      Optional[Any]			  =model(**self._prepare_for_class(lowerCAmelCase__       , lowerCAmelCase__))

     lowerCamelCase__:      int			  =outputs.hidden_states
     lowerCamelCase__:      Dict			  =self.model_tester.num_hidden_layers + 1
     self.assertEqual(len(lowerCAmelCase__)       , lowerCAmelCase__)

     lowerCamelCase__:      List[Any]			  =self.model_tester.seq_length - self.model_tester.num_masks
     lowerCamelCase__:      int			  =num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

     self.assertListEqual(
         list(hidden_states[0].shape[-2:])       , [seq_length, self.model_tester.hidden_size]       , )

   lowerCamelCase__  ,					lowerCamelCase__:      int			  =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:      Optional[Any]			  =True
     check_hidden_states_output(lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowerCamelCase__:      Tuple			  =True

     check_hidden_states_output(lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__)







 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->List[Any]:



   '''simple docstring'''



   pass





def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =hf_hub_download(
      repo_id="hf-internal-testing/spaghetti-video"	,    filename="eating_spaghetti.npy"	,    repo_type="dataset"		)
  lowerCamelCase__:      str			  =np.load(UpperCamelCase_		)
  return list(UpperCamelCase_		)






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   return (
       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       , image_std=[0.5, 0.5, 0.5])
       if is_vision_available()
       else None
   )

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to(
       lowerCAmelCase__)

   lowerCamelCase__:      List[str]			  =self.default_image_processor
   lowerCamelCase__:      Tuple			  =prepare_video()
   lowerCamelCase__:      Optional[int]			  =image_processor(lowerCAmelCase__       , return_tensors="pt").to(lowerCAmelCase__)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:      List[Any]			  =model(**lowerCAmelCase__)

   # verify the logits
   lowerCamelCase__:      Optional[int]			  =torch.Size((1, 400))
   self.assertEqual(outputs.logits.shape       , lowerCAmelCase__)

   lowerCamelCase__:      Any			  =torch.tensor([0.3669, -0.0688, -0.2421]).to(lowerCAmelCase__)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]       , lowerCAmelCase__       , atol=1E-4))







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(lowerCAmelCase__)

   lowerCamelCase__:      int			  =self.default_image_processor
   lowerCamelCase__:      str			  =prepare_video()
   lowerCamelCase__:      Dict			  =image_processor(lowerCAmelCase__       , return_tensors="pt").to(lowerCAmelCase__)

   # add boolean mask, indicating which patches to mask
   lowerCamelCase__:      Dict			  =hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"       , filename="bool_masked_pos.pt")
   lowerCamelCase__:      Optional[Any]			  =torch.load(lowerCAmelCase__)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:      int			  =model(**lowerCAmelCase__)

   # verify the logits
   lowerCamelCase__:      Any			  =torch.Size([1, 1_408, 1_536])
   lowerCamelCase__:      List[str]			  =torch.tensor(
       [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]       , device=lowerCAmelCase__)
   self.assertEqual(outputs.logits.shape       , lowerCAmelCase__)
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       , lowerCAmelCase__       , atol=1E-4))

   # verify the loss (`config.norm_pix_loss` = `True`)
   lowerCamelCase__:      Optional[int]			  =torch.tensor([0.5142]       , device=lowerCAmelCase__)
   self.assertTrue(torch.allclose(outputs.loss       , lowerCAmelCase__       , atol=1E-4))

   # verify the loss (`config.norm_pix_loss` = `False`)
   lowerCamelCase__:      str			  =VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"       , norm_pix_loss=lowerCAmelCase__).to(
       lowerCAmelCase__)

   with torch.no_grad():
     lowerCamelCase__:      int			  =model(**lowerCAmelCase__)

   lowerCamelCase__:      str			  =torch.tensor(torch.tensor([0.6469])       , device=lowerCAmelCase__)
   self.assertTrue(torch.allclose(outputs.loss       , lowerCAmelCase__       , atol=1E-4))



"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__       =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__       =			[]
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				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = val



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__SCREAMING_SNAKE_CASE            = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model"""				)
						__SCREAMING_SNAKE_CASE            = value
				else:
						__SCREAMING_SNAKE_CASE            = value

		return new_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = """"""

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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = image.size
		__SCREAMING_SNAKE_CASE            = max(UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = 800 if """detection""" in checkpoint_url else 1000
		__SCREAMING_SNAKE_CASE            = target_max_size / current_max_size
		__SCREAMING_SNAKE_CASE            = image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

		return resized_image



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = F.to_tensor(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = F.normalize(UpperCamelCase_  ,				mean=[0.485, 0.456, 0.406]  ,				std=[0.229, 0.224, 0.225]				)
		return image



@torch.no_grad()
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		logger.info("""Converting model..."""				)

		# load original state dict
		__SCREAMING_SNAKE_CASE            = torch.hub.load_state_dict_from_url(UpperCamelCase_  ,				map_location="""cpu"""				)
		# rename keys
		for src, dest in rename_keys:
				rename_key(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = rename_backbone_keys(UpperCamelCase_				)
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase_				)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		__SCREAMING_SNAKE_CASE            = """model."""
		for key in state_dict.copy().keys():
				if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
						__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = val
    # create HuggingFace model and load state dict
		__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = 15
				__SCREAMING_SNAKE_CASE            = 2
				__SCREAMING_SNAKE_CASE            = {0: """table""", 1: """table rotated"""}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}
		else:
				__SCREAMING_SNAKE_CASE            = 125
				__SCREAMING_SNAKE_CASE            = 6
				__SCREAMING_SNAKE_CASE            = {
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = DetrImageProcessor(
		    format="""coco_detection"""  ,				max_size=800 if """detection""" in checkpoint_url else 1000				)
		__SCREAMING_SNAKE_CASE            = TableTransformerForObjectDetection(UpperCamelCase_				)
		model.load_state_dict(UpperCamelCase_				)
		model.eval()

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

		__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)

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

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

		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, :3, :3]  ,				UpperCamelCase_  ,				atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				UpperCamelCase_  ,				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(UpperCamelCase_				).mkdir(exist_ok=UpperCamelCase_				)
				model.save_pretrained(UpperCamelCase_				)
				image_processor.save_pretrained(UpperCamelCase_				)

		if push_to_hub:
				# Push model to HF hub
				logger.info("""Pushing model to the hub..."""				)
				__SCREAMING_SNAKE_CASE            = (
				    """microsoft/table-transformer-detection"""
				    if """detection""" in checkpoint_url
				    else """microsoft/table-transformer-structure-recognition"""
				)
				model.push_to_hub(UpperCamelCase_				)
				image_processor.push_to_hub(UpperCamelCase_				)


if __name__ == "__main__":
							__magic_name__       =			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."
							)
							__magic_name__       =			parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




