
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()
UpperCAmelCase       =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase       =					[]
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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       state_dict.pop(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       val






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase    ,   lowercase	    =       image.size
	lowercase	    =       max(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	lowercase	    =       800 if 'detection' in checkpoint_url else 1000
	lowercase	    =       target_max_size / current_max_size
	lowercase	    =       image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

	return resized_image






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       F.to_tensor(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       F.normalize(__SCREAMING_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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	logger.info('Converting model...'				)

	# load original state dict
	lowercase	    =       torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE					,				map_location='cpu'				)
	# rename keys
	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	lowercase	    =       rename_backbone_keys(__SCREAMING_SNAKE_CASE				)
	# query, key and value matrices need special treatment
	read_in_q_k_v(__SCREAMING_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(__SCREAMING_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 1000				)
	lowercase	    =       TableTransformerForObjectDetection(__SCREAMING_SNAKE_CASE				)
	model.load_state_dict(__SCREAMING_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=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       Image.open(__SCREAMING_SNAKE_CASE				).convert('RGB'				)
	lowercase	    =       normalize(resize(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)				).unsqueeze(0				)

	lowercase	    =       model(__SCREAMING_SNAKE_CASE				)

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


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


"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_   ,      pad_token_id=snake_case_   ,      bos_token_id=snake_case_   ,      eos_token_id=snake_case_							)
										A__       :     Dict								=			hidden_size
										A__       :     Dict								=			feat_extract_norm
										A__       :     str								=			feat_extract_activation
										A__       :     Optional[Any]								=			list(snake_case_							)
										A__       :     str								=			list(snake_case_							)
										A__       :     Any								=			list(snake_case_							)
										A__       :     Any								=			conv_bias
										A__       :     Any								=			num_conv_pos_embeddings
										A__       :     Any								=			num_conv_pos_embedding_groups
										A__       :     str								=			len(self.conv_dim							)
										A__       :     Tuple								=			num_hidden_layers
										A__       :     int								=			intermediate_size
										A__       :     Union[str, Any]								=			squeeze_factor
										A__       :     Union[str, Any]								=			hidden_act
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			hidden_dropout
										A__       :     Dict								=			attention_dropout
										A__       :     Tuple								=			activation_dropout
										A__       :     Optional[int]								=			feat_proj_dropout
										A__       :     Optional[Any]								=			final_dropout
										A__       :     int								=			layerdrop
										A__       :     List[Any]								=			layer_norm_eps
										A__       :     int								=			initializer_range
										A__       :     Dict								=			vocab_size

										if (
										    (len(self.conv_stride							) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
										    or (len(self.conv_dim							) != self.num_feat_extract_layers)
										):
																	raise ValueError(
																	    """Configuration for convolutional layers is incorrect."""
																	    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																	    F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
																	    F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										A__       :     Optional[Any]								=			apply_spec_augment
										A__       :     int								=			mask_time_prob
										A__       :     Tuple								=			mask_time_length
										A__       :     Optional[Any]								=			mask_time_min_masks
										A__       :     Any								=			mask_feature_prob
										A__       :     List[Any]								=			mask_feature_length
										A__       :     Any								=			mask_feature_min_masks

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return functools.reduce(operator.mul   ,      self.conv_stride   ,      1							)





