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:	Union[str, Any]       			=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase:	int       			=		[]
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     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							state_dict.pop(snake_case  )
							_lowerCAmelCase							=							val






def _lowerCamelCase  (		snake_case  ):
							_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  (		snake_case  ):
							_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[:256, :]
														_lowerCAmelCase							=							in_proj_bias[:256]
														_lowerCAmelCase							=							in_proj_weight[256:512, :]
														_lowerCAmelCase							=							in_proj_bias[256:512]
														_lowerCAmelCase							=							in_proj_weight[-256:, :]
														_lowerCAmelCase							=							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
														_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[:256, :]
														_lowerCAmelCase							=							in_proj_bias[:256]
														_lowerCAmelCase							=							in_proj_weight[256:512, :]
														_lowerCAmelCase							=							in_proj_bias[256:512]
														_lowerCAmelCase							=							in_proj_weight[-256:, :]
														_lowerCAmelCase							=							in_proj_bias[-256:]
														# 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[:256, :]
														_lowerCAmelCase							=							in_proj_bias_cross_attn[:256]
														_lowerCAmelCase							=							in_proj_weight_cross_attn[256:512, :]
														_lowerCAmelCase							=							in_proj_bias_cross_attn[256:512]
														_lowerCAmelCase							=							in_proj_weight_cross_attn[-256:, :]
														_lowerCAmelCase							=							in_proj_bias_cross_attn[-256:]






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase			,		_lowerCAmelCase							=							image.size
							_lowerCAmelCase							=							max(snake_case     , snake_case  )
							_lowerCAmelCase							=							800 if 'detection' in checkpoint_url else 1_000
							_lowerCAmelCase							=							target_max_size / current_max_size
							_lowerCAmelCase							=							image.resize((int(round(scale * width  )  ), int(round(scale * height  )  ))  )

							return resized_image






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							F.to_tensor(snake_case  )
							_lowerCAmelCase							=							F.normalize(snake_case     , mean=[0.4_85, 0.4_56, 0.4_06]     , std=[0.2_29, 0.2_24, 0.2_25]  )
							return image






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							logger.info('Converting model...'  )

							# load original state dict
							_lowerCAmelCase							=							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  )
							_lowerCAmelCase							=							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
							_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(snake_case  )
																					_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							=							15
														_lowerCAmelCase							=							2
														_lowerCAmelCase							=							{0: 'table', 1: 'table rotated'}
														_lowerCAmelCase							=							idalabel
														_lowerCAmelCase							=							{v: k for k, v in idalabel.items()}
							else:
														_lowerCAmelCase							=							125
														_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=800 if 'detection' in checkpoint_url else 1_000  )
							_lowerCAmelCase							=							TableTransformerForObjectDetection(snake_case  )
							model.load_state_dict(snake_case  )
							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=snake_case  )
							_lowerCAmelCase							=							Image.open(snake_case  ).convert('RGB'  )
							_lowerCAmelCase							=							normalize(resize(snake_case     , snake_case  )  ).unsqueeze(0  )

							_lowerCAmelCase							=							model(snake_case  )

							if "detection" in checkpoint_url:
														_lowerCAmelCase							=							(1, 15, 3)
														_lowerCAmelCase							=							torch.tensor(
														    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]  )
														_lowerCAmelCase							=							torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]  )

							else:
														_lowerCAmelCase							=							(1, 125, 7)
														_lowerCAmelCase							=							torch.tensor(
														    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]  )
														_lowerCAmelCase							=							torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]  )

							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...'  )
														_lowerCAmelCase							=							(
														    '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__":
				_lowercase:	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:	str       			=		parser.parse_args()
				convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






