


'''simple docstring'''
from ..utils import DummyObject, requires_backends
class    lowercase	(							metaclass=A__					):

		"""simple docstring"""

		_a     =				['note_seq']




		def __init__(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							requires_backends(self    ,   ['''note_seq''']							)




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							requires_backends(cls    ,   ['''note_seq''']							)




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							requires_backends(cls    ,   ['''note_seq''']							)



'''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()
__snake_case							      =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case							      =     []
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      (			__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= state_dict.pop(__a   )
					UpperCamelCase__   :int												= val


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															UpperCamelCase__   :Dict												= key.replace('''backbone.0.body''' ,					'''backbone.conv_encoder.model'''   )
															UpperCamelCase__   :List[str]												= value
										else:
															UpperCamelCase__   :Dict												= value

					return new_state_dict


def 			a      (			__a   )  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= ''''''

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


def 			a      (			__a ,					__a   )  ->	Optional[int]:



					'''simple docstring'''






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

					return resized_image


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :Any												= F.to_tensor(__a   )
					UpperCamelCase__   :int												= F.normalize(__a ,					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      (			__a ,					__a ,					__a   )  ->	Dict:



					'''simple docstring'''






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

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

					UpperCamelCase__   :List[Any]												= DetrImageProcessor(
					    format='''coco_detection''' ,					max_size=800 if '''detection''' in checkpoint_url else 1000   )
					UpperCamelCase__   :int												= TableTransformerForObjectDetection(__a   )
					model.load_state_dict(__a   )
					model.eval()

					# verify our conversion
					UpperCamelCase__   :Dict												= '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
					UpperCamelCase__   :Optional[Any]												= hf_hub_download(repo_id='''nielsr/example-pdf''' ,					repo_type='''dataset''' ,					filename=__a   )
					UpperCamelCase__   :Tuple												= Image.open(__a   ).convert('''RGB'''   )
					UpperCamelCase__   :int												= normalize(resize(__a ,					__a   )   ).unsqueeze(0   )

					UpperCamelCase__   :Optional[int]												= model(__a   )

					if "detection" in checkpoint_url:
										UpperCamelCase__   :Dict												= (1, 15, 3)
										UpperCamelCase__   :List[Any]												= torch.tensor(
										    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]   )
										UpperCamelCase__   :Tuple												= 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__   :Optional[Any]												= (1, 125, 7)
										UpperCamelCase__   :Dict												= torch.tensor(
										    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]   )
										UpperCamelCase__   :List[Any]												= 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] ,					__a ,					atol=1e-4   )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,					__a ,					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(__a   ).mkdir(exist_ok=__a   )
										model.save_pretrained(__a   )
										image_processor.save_pretrained(__a   )

					if push_to_hub:
										# Push model to HF hub
										logger.info('''Pushing model to the hub...'''   )
										UpperCamelCase__   :Union[str, Any]												= (
										    '''microsoft/table-transformer-detection'''
										    if '''detection''' in checkpoint_url
										    else '''microsoft/table-transformer-structure-recognition'''
										)
										model.push_to_hub(__a   )
										image_processor.push_to_hub(__a   )


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