


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A	=[]
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      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					List[Any]        =     state_dict.pop(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     val







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[Any]        =     OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									UpperCAmelCase__  :					Any        =     key.replace("""backbone.0.body""" ,      """backbone.conv_encoder.model"""					)
									UpperCAmelCase__  :					Optional[int]        =     value
						else:
									UpperCAmelCase__  :					Optional[Any]        =     value

			return new_state_dict







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					int        =     """"""

			# 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)
						UpperCAmelCase__  :					Any        =     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
						UpperCAmelCase__  :					Dict        =     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
						UpperCAmelCase__  :					Optional[Any]        =     in_proj_weight[:2_5_6, :]
						UpperCAmelCase__  :					str        =     in_proj_bias[:2_5_6]
						UpperCAmelCase__  :					List[str]        =     in_proj_weight[2_5_6:5_1_2, :]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_bias[2_5_6:5_1_2]
						UpperCAmelCase__  :					List[Any]        =     in_proj_weight[-2_5_6:, :]
						UpperCAmelCase__  :					Optional[Any]        =     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
						UpperCAmelCase__  :					List[str]        =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
						UpperCAmelCase__  :					List[str]        =     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
						UpperCAmelCase__  :					Tuple        =     in_proj_weight[:2_5_6, :]
						UpperCAmelCase__  :					List[Any]        =     in_proj_bias[:2_5_6]
						UpperCAmelCase__  :					Tuple        =     in_proj_weight[2_5_6:5_1_2, :]
						UpperCAmelCase__  :					Any        =     in_proj_bias[2_5_6:5_1_2]
						UpperCAmelCase__  :					int        =     in_proj_weight[-2_5_6:, :]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_bias[-2_5_6:]
						# read in weights + bias of input projection layer of cross-attention
						UpperCAmelCase__  :					List[str]        =     state_dict.pop(
						    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
						UpperCAmelCase__  :					Dict        =     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
						UpperCAmelCase__  :					str        =     in_proj_weight_cross_attn[:2_5_6, :]
						UpperCAmelCase__  :					List[str]        =     in_proj_bias_cross_attn[:2_5_6]
						UpperCAmelCase__  :					Dict        =     in_proj_weight_cross_attn[2_5_6:5_1_2, :]
						UpperCAmelCase__  :					Union[str, Any]        =     in_proj_bias_cross_attn[2_5_6:5_1_2]
						UpperCAmelCase__  :					List[str]        =     in_proj_weight_cross_attn[-2_5_6:, :]
						UpperCAmelCase__  :					Tuple        =     in_proj_bias_cross_attn[-2_5_6:]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     image.size
			UpperCAmelCase__  :					int        =     max(UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
			UpperCAmelCase__  :					Union[str, Any]        =     target_max_size / current_max_size
			UpperCAmelCase__  :					int        =     image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

			return resized_image







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Dict        =     F.to_tensor(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     F.normalize(UpperCamelCase__ ,      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      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			logger.info("""Converting model..."""					)

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

			UpperCAmelCase__  :					Optional[int]        =     DetrImageProcessor(
			    format="""coco_detection""" ,      max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0					)
			UpperCAmelCase__  :					List[str]        =     TableTransformerForObjectDetection(UpperCamelCase__					)
			model.load_state_dict(UpperCamelCase__					)
			model.eval()

			# verify our conversion
			UpperCAmelCase__  :					Union[str, Any]        =     """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
			UpperCAmelCase__  :					int        =     hf_hub_download(repo_id="""nielsr/example-pdf""" ,      repo_type="""dataset""" ,      filename=UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     Image.open(UpperCamelCase__					).convert("""RGB"""					)
			UpperCAmelCase__  :					Optional[int]        =     normalize(resize(UpperCamelCase__ ,      UpperCamelCase__					)					).unsqueeze(0					)

			UpperCAmelCase__  :					Union[str, Any]        =     model(UpperCamelCase__					)

			if "detection" in checkpoint_url:
						UpperCAmelCase__  :					Optional[Any]        =     (1, 1_5, 3)
						UpperCAmelCase__  :					Union[str, Any]        =     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]]					)
						UpperCAmelCase__  :					int        =     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:
						UpperCAmelCase__  :					Union[str, Any]        =     (1, 1_2_5, 7)
						UpperCAmelCase__  :					List[Any]        =     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]]					)
						UpperCAmelCase__  :					int        =     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] ,      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..."""					)
						UpperCAmelCase__  :					Optional[Any]        =     (
						    """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__":
		__A	=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.'
		)
		__A	=parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A	={
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)