



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

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_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        lowerCamelCase  (				_snake_case  :						List[str]     ,_snake_case  :						str     ,_snake_case  :						int       ):





				'''simple docstring'''


				lowercase__		       =						state_dict.pop(_snake_case       )
				lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						List[Any]       ):





				'''simple docstring'''


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

				return new_state_dict




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





				'''simple docstring'''


				lowercase__		       =						""

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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						Optional[int]       ):





				'''simple docstring'''


				lowercase__       ,       lowercase__		       =						image.size
				lowercase__		       =						max(_snake_case     ,_snake_case       )
				lowercase__		       =						800 if "detection" in checkpoint_url else 1_000
				lowercase__		       =						target_max_size / current_max_size
				lowercase__		       =						image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

				return resized_image




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]       ):





				'''simple docstring'''


				lowercase__		       =						F.to_tensor(_snake_case       )
				lowercase__		       =						F.normalize(_snake_case     ,mean=[0.485, 0.456, 0.406]     ,std=[0.229, 0.224, 0.225]       )
				return image




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						List[Any]     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple       ):





				'''simple docstring'''


				logger.info("Converting model..."       )

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

				lowercase__		       =						DetrImageProcessor(
				    format="coco_detection"     ,max_size=800 if "detection" in checkpoint_url else 1_000       )
				lowercase__		       =						TableTransformerForObjectDetection(_snake_case       )
				model.load_state_dict(_snake_case       )
				model.eval()

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

				lowercase__		       =						model(_snake_case       )

				if "detection" in checkpoint_url:
								lowercase__		       =						(1, 15, 3)
								lowercase__		       =						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]]       )
								lowercase__		       =						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:
								lowercase__		       =						(1, 125, 7)
								lowercase__		       =						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]]       )
								lowercase__		       =						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]     ,_snake_case     ,atol=1e-4       )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,_snake_case     ,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(_snake_case       ).mkdir(exist_ok=_snake_case       )
								model.save_pretrained(_snake_case       )
								image_processor.save_pretrained(_snake_case       )

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


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









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




