


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

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case							: Dict      =   []
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 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       List[str]     ):
							a__    =  state_dict.pop(__lowerCAmelCase     )
							a__    =  val






def 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					a__    =  key.replace('backbone.0.body'      ,					'backbone.conv_encoder.model'     )
																					a__    =  value
														else:
																					a__    =  value

							return new_state_dict






def 							__lowercase     (				__lowerCAmelCase							:       List[str]     ):
							a__    =  ''

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






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       str     ):
							a__   ,      a__    =  image.size
							a__    =  max(__lowerCAmelCase      ,					__lowerCAmelCase     )
							a__    =  8_0_0 if 'detection' in checkpoint_url else 1_0_0_0
							a__    =  target_max_size / current_max_size
							a__    =  image.resize((int(round(scale * width     )     ), int(round(scale * height     )     ))     )

							return resized_image






def 							__lowercase     (				__lowerCAmelCase							:       str     ):
							a__    =  F.to_tensor(__lowerCAmelCase     )
							a__    =  F.normalize(__lowerCAmelCase      ,					mean=[0.485, 0.456, 0.406]      ,					std=[0.229, 0.224, 0.225]     )
							return image






@torch.no_grad()
def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       Optional[Any]     ):
							logger.info('Converting model...'     )

							# load original state dict
							a__    =  torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,					map_location='cpu'     )
							# rename keys
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							a__    =  rename_backbone_keys(__lowerCAmelCase     )
							# query, key and value matrices need special treatment
							read_in_q_k_v(__lowerCAmelCase     )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							a__    =  'model.'
							for key in state_dict.copy().keys():
														if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
																					a__    =  state_dict.pop(__lowerCAmelCase     )
																					a__    =  val
    # create HuggingFace model and load state dict
							a__    =  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:
														a__    =  1_5
														a__    =  2
														a__    =  {0: 'table', 1: 'table rotated'}
														a__    =  idalabel
														a__    =  {v: k for k, v in idalabel.items()}
							else:
														a__    =  1_2_5
														a__    =  6
														a__    =  {
														    0: 'table',
														    1: 'table column',
														    2: 'table row',
														    3: 'table column header',
														    4: 'table projected row header',
														    5: 'table spanning cell',
														}
														a__    =  idalabel
														a__    =  {v: k for k, v in idalabel.items()}

							a__    =  DetrImageProcessor(
							    format='coco_detection'      ,					max_size=8_0_0 if 'detection' in checkpoint_url else 1_0_0_0     )
							a__    =  TableTransformerForObjectDetection(__lowerCAmelCase     )
							model.load_state_dict(__lowerCAmelCase     )
							model.eval()

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

							a__    =  model(__lowerCAmelCase     )

							if "detection" in checkpoint_url:
														a__    =  (1, 1_5, 3)
														a__    =  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]]     )
														a__    =  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:
														a__    =  (1, 1_2_5, 7)
														a__    =  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]]     )
														a__    =  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]      ,					__lowerCAmelCase      ,					atol=1E-4     )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,					__lowerCAmelCase      ,					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(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
														model.save_pretrained(__lowerCAmelCase     )
														image_processor.save_pretrained(__lowerCAmelCase     )

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


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




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						snake_case							: Union[str, Any]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




