





'''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()
lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:      List[str]	=    []
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  a__    (    lowercase :					str, lowercase :					Optional[Any], lowercase :					int				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	state_dict.pop(lowercase				)
     _UpperCamelCase       =	val







def  a__    (    lowercase :					List[Any]				)		->		Any:





     """simple docstring"""

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

     return new_state_dict







def  a__    (    lowercase :					Dict				)		->		str:





     """simple docstring"""

     _UpperCamelCase       =	''''''

     # first: transformer encoder
     for i in range(6				):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6				):
          # read in weights + bias of input projection layer of self-attention
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          _UpperCamelCase       =	state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          _UpperCamelCase       =	in_proj_weight_cross_attn[:256, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[:256]
          _UpperCamelCase       =	in_proj_weight_cross_attn[256:512, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[256:512]
          _UpperCamelCase       =	in_proj_weight_cross_attn[-256:, :]
          _UpperCamelCase       =	in_proj_bias_cross_attn[-256:]







def  a__    (    lowercase :					int, lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	image.size
     _UpperCamelCase       =	max(lowercase, lowercase				)
     _UpperCamelCase       =	800 if '''detection''' in checkpoint_url else 1000
     _UpperCamelCase       =	target_max_size / current_max_size
     _UpperCamelCase       =	image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

     return resized_image







def  a__    (    lowercase :					int				)		->		str:





     """simple docstring"""

     _UpperCamelCase       =	F.to_tensor(lowercase				)
     _UpperCamelCase       =	F.normalize(lowercase, mean=[0.4_8_5, 0.4_5_6, 0.4_0_6], std=[0.2_2_9, 0.2_2_4, 0.2_2_5]				)
     return image







@torch.no_grad()
def  a__    (    lowercase :					Dict, lowercase :					List[str], lowercase :					List[Any]				)		->		Dict:





     """simple docstring"""

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

     # load original state dict
     _UpperCamelCase       =	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       =	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       =	'''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
               _UpperCamelCase       =	state_dict.pop(lowercase				)
               _UpperCamelCase       =	val
    # create HuggingFace model and load state dict
     _UpperCamelCase       =	TableTransformerConfig(
         backbone='''resnet18''', mask_loss_coefficient=1, dice_loss_coefficient=1, ce_loss_coefficient=1, bbox_loss_coefficient=5, giou_loss_coefficient=2, eos_coefficient=0.4, class_cost=1, bbox_cost=5, giou_cost=2, )

     if "detection" in checkpoint_url:
          _UpperCamelCase       =	15
          _UpperCamelCase       =	2
          _UpperCamelCase       =	{0: '''table''', 1: '''table rotated'''}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}
     else:
          _UpperCamelCase       =	125
          _UpperCamelCase       =	6
          _UpperCamelCase       =	{
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}

     _UpperCamelCase       =	DetrImageProcessor(
         format='''coco_detection''', max_size=800 if '''detection''' in checkpoint_url else 1000				)
     _UpperCamelCase       =	TableTransformerForObjectDetection(lowercase				)
     model.load_state_dict(lowercase				)
     model.eval()

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

     _UpperCamelCase       =	model(lowercase				)

     if "detection" in checkpoint_url:
          _UpperCamelCase       =	(1, 15, 3)
          _UpperCamelCase       =	torch.tensor(
              [[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]]				)
          _UpperCamelCase       =	torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]				)

     else:
          _UpperCamelCase       =	(1, 125, 7)
          _UpperCamelCase       =	torch.tensor(
              [[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]]				)
          _UpperCamelCase       =	torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]				)

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










'''simple docstring'''

import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.roberta.modeling_flax_roberta import (
       FlaxRobertaForCausalLM,
       FlaxRobertaForMaskedLM,
       FlaxRobertaForMultipleChoice,
       FlaxRobertaForQuestionAnswering,
       FlaxRobertaForSequenceClassification,
       FlaxRobertaForTokenClassification,
       FlaxRobertaModel,
   )
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]=13	,			lowerCAmelCase__       :		Union[str, Any]=7	,			lowerCAmelCase__       :		str=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		int=99	,			lowerCAmelCase__       :		str=32	,			lowerCAmelCase__       :		str=5	,			lowerCAmelCase__       :		str=4	,			lowerCAmelCase__       :		str=37	,			lowerCAmelCase__       :		int="gelu"	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		int=0.1	,			lowerCAmelCase__       :		Optional[int]=512	,			lowerCAmelCase__       :		Dict=16	,			lowerCAmelCase__       :		List[Any]=2	,			lowerCAmelCase__       :		Any=0.02	,			lowerCAmelCase__       :		Union[str, Any]=4	,			)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	seq_length
            _UpperCamelCase       =	is_training
            _UpperCamelCase       =	use_attention_mask
            _UpperCamelCase       =	use_token_type_ids
            _UpperCamelCase       =	use_labels
            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	type_vocab_size
            _UpperCamelCase       =	type_sequence_label_size
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	num_choices





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size					)

            _UpperCamelCase       =	None
            if self.use_attention_mask:
                 _UpperCamelCase       =	random_attention_mask([self.batch_size, self.seq_length]					)

            _UpperCamelCase       =	None
            if self.use_token_type_ids:
                 _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.type_vocab_size					)

            _UpperCamelCase       =	RobertaConfig(
                vocab_size=self.vocab_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	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			is_decoder=lowerCAmelCase__	,			initializer_range=self.initializer_range	,			)

            return config, input_ids, token_type_ids, attention_mask





       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	config_and_inputs
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict







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

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	config_and_inputs

            _UpperCamelCase       =	True
            _UpperCamelCase       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2					)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )

@require_flax
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   True

       _snake_case     :					Optional[Any]						    =   (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	FlaxRobertaModelTester(self					)







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

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase       =	model_class_name.from_pretrained('''roberta-base'''	,			from_pt=lowerCAmelCase__					)
                 _UpperCamelCase       =	model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(lowerCAmelCase__					)



