
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   =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase   =	[]
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__					:   int      ,					UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   List[Any]							)	->     int:
							lowerCamelCase_					=       state_dict.pop(UpperCAmelCase__							)
							lowerCamelCase_					=       val



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple							)	->     List[str]:
							lowerCamelCase_					=       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase_					=       key.replace("""backbone.0.body"""      ,					"""backbone.conv_encoder.model"""							)
																					lowerCamelCase_					=       value
														else:
																					lowerCamelCase_					=       value

							return new_state_dict



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]							)	->     str:
							lowerCamelCase_					=       """"""

							# 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)
														lowerCamelCase_					=       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
														lowerCamelCase_					=       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
														lowerCamelCase_					=       in_proj_weight[:2_5_6, :]
														lowerCamelCase_					=       in_proj_bias[:2_5_6]
														lowerCamelCase_					=       in_proj_weight[2_5_6:5_1_2, :]
														lowerCamelCase_					=       in_proj_bias[2_5_6:5_1_2]
														lowerCamelCase_					=       in_proj_weight[-2_5_6:, :]
														lowerCamelCase_					=       in_proj_bias[-2_5_6:]
							# 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
														lowerCamelCase_					=       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
														lowerCamelCase_					=       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
														lowerCamelCase_					=       in_proj_weight[:2_5_6, :]
														lowerCamelCase_					=       in_proj_bias[:2_5_6]
														lowerCamelCase_					=       in_proj_weight[2_5_6:5_1_2, :]
														lowerCamelCase_					=       in_proj_bias[2_5_6:5_1_2]
														lowerCamelCase_					=       in_proj_weight[-2_5_6:, :]
														lowerCamelCase_					=       in_proj_bias[-2_5_6:]
														# read in weights + bias of input projection layer of cross-attention
														lowerCamelCase_					=       state_dict.pop(
														    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
														lowerCamelCase_					=       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
														lowerCamelCase_					=       in_proj_weight_cross_attn[:2_5_6, :]
														lowerCamelCase_					=       in_proj_bias_cross_attn[:2_5_6]
														lowerCamelCase_					=       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
														lowerCamelCase_					=       in_proj_bias_cross_attn[2_5_6:5_1_2]
														lowerCamelCase_					=       in_proj_weight_cross_attn[-2_5_6:, :]
														lowerCamelCase_					=       in_proj_bias_cross_attn[-2_5_6:]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Dict							)	->     int:
							lowerCamelCase_	, lowerCamelCase_					=       image.size
							lowerCamelCase_					=       max(UpperCAmelCase__      ,					UpperCAmelCase__							)
							lowerCamelCase_					=       8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
							lowerCamelCase_					=       target_max_size / current_max_size
							lowerCamelCase_					=       image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

							return resized_image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     Optional[Any]:
							lowerCamelCase_					=       F.to_tensor(UpperCAmelCase__							)
							lowerCamelCase_					=       F.normalize(UpperCAmelCase__      ,					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   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   str							)	->     int:
							logger.info("""Converting model..."""							)

							# load original state dict
							lowerCamelCase_					=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      ,					map_location="""cpu"""							)
							# rename keys
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)
							lowerCamelCase_					=       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
							lowerCamelCase_					=       """model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
																					lowerCamelCase_					=       state_dict.pop(UpperCAmelCase__							)
																					lowerCamelCase_					=       val
    # create HuggingFace model and load state dict
							lowerCamelCase_					=       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:
														lowerCamelCase_					=       1_5
														lowerCamelCase_					=       2
														lowerCamelCase_					=       {0: """table""", 1: """table rotated"""}
														lowerCamelCase_					=       idalabel
														lowerCamelCase_					=       {v: k for k, v in idalabel.items()}
							else:
														lowerCamelCase_					=       1_2_5
														lowerCamelCase_					=       6
														lowerCamelCase_					=       {
														    0: """table""",
														    1: """table column""",
														    2: """table row""",
														    3: """table column header""",
														    4: """table projected row header""",
														    5: """table spanning cell""",
														}
														lowerCamelCase_					=       idalabel
														lowerCamelCase_					=       {v: k for k, v in idalabel.items()}

							lowerCamelCase_					=       DetrImageProcessor(
							    format="""coco_detection"""      ,					max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0							)
							lowerCamelCase_					=       TableTransformerForObjectDetection(UpperCAmelCase__							)
							model.load_state_dict(UpperCAmelCase__							)
							model.eval()

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

							lowerCamelCase_					=       model(UpperCAmelCase__							)

							if "detection" in checkpoint_url:
														lowerCamelCase_					=       (1, 1_5, 3)
														lowerCamelCase_					=       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]]							)
														lowerCamelCase_					=       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:
														lowerCamelCase_					=       (1, 1_2_5, 7)
														lowerCamelCase_					=       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]]							)
														lowerCamelCase_					=       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]      ,					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..."""							)
														lowerCamelCase_					=       (
														    """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__":
		lowercase   =	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   =	parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


if __name__ == "__main__":
		fire.Fire(save_randomly_initialized_version)





