





'''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:					str							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A:					Optional[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     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
	__UpperCAmelCase					=   state_dict.pop(_lowerCAmelCase    )
	__UpperCAmelCase					=   val







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
	__UpperCAmelCase					=   OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__UpperCAmelCase					=   key.replace('backbone.0.body'       ,    'backbone.conv_encoder.model'    )
			__UpperCAmelCase					=   value
		else:
			__UpperCAmelCase					=   value

	return new_state_dict







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
	__UpperCAmelCase					=   ''

	# 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					=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'    )
		__UpperCAmelCase					=   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					=   in_proj_weight[:2_56, :]
		__UpperCAmelCase					=   in_proj_bias[:2_56]
		__UpperCAmelCase					=   in_proj_weight[2_56:5_12, :]
		__UpperCAmelCase					=   in_proj_bias[2_56:5_12]
		__UpperCAmelCase					=   in_proj_weight[-2_56:, :]
		__UpperCAmelCase					=   in_proj_bias[-2_56:]
	# 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					=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'    )
		__UpperCAmelCase					=   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					=   in_proj_weight[:2_56, :]
		__UpperCAmelCase					=   in_proj_bias[:2_56]
		__UpperCAmelCase					=   in_proj_weight[2_56:5_12, :]
		__UpperCAmelCase					=   in_proj_bias[2_56:5_12]
		__UpperCAmelCase					=   in_proj_weight[-2_56:, :]
		__UpperCAmelCase					=   in_proj_bias[-2_56:]
		# read in weights + bias of input projection layer of cross-attention
		__UpperCAmelCase					=   state_dict.pop(
		    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'    )
		__UpperCAmelCase					=   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					=   in_proj_weight_cross_attn[:2_56, :]
		__UpperCAmelCase					=   in_proj_bias_cross_attn[:2_56]
		__UpperCAmelCase					=   in_proj_weight_cross_attn[2_56:5_12, :]
		__UpperCAmelCase					=   in_proj_bias_cross_attn[2_56:5_12]
		__UpperCAmelCase					=   in_proj_weight_cross_attn[-2_56:, :]
		__UpperCAmelCase					=   in_proj_bias_cross_attn[-2_56:]







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
	__UpperCAmelCase					,    __UpperCAmelCase					=   image.size
	__UpperCAmelCase					=   max(_lowerCAmelCase       ,    _lowerCAmelCase    )
	__UpperCAmelCase					=   8_00 if 'detection' in checkpoint_url else 10_00
	__UpperCAmelCase					=   target_max_size / current_max_size
	__UpperCAmelCase					=   image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

	return resized_image







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
	__UpperCAmelCase					=   F.to_tensor(_lowerCAmelCase    )
	__UpperCAmelCase					=   F.normalize(_lowerCAmelCase       ,    mean=[0.485, 0.456, 0.406]       ,    std=[0.229, 0.224, 0.225]    )
	return image







@torch.no_grad()
def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
	logger.info('Converting model...'    )

	# load original state dict
	__UpperCAmelCase					=   torch.hub.load_state_dict_from_url(_lowerCAmelCase       ,    map_location='cpu'    )
	# rename keys
	for src, dest in rename_keys:
		rename_key(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )
	__UpperCAmelCase					=   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
	__UpperCAmelCase					=   'model.'
	for key in state_dict.copy().keys():
		if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
			__UpperCAmelCase					=   state_dict.pop(_lowerCAmelCase    )
			__UpperCAmelCase					=   val
			 # create HuggingFace model and load state dict
	__UpperCAmelCase					=   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					=   15
		__UpperCAmelCase					=   2
		__UpperCAmelCase					=   {0: 'table', 1: 'table rotated'}
		__UpperCAmelCase					=   idalabel
		__UpperCAmelCase					=   {v: k for k, v in idalabel.items()}
	else:
		__UpperCAmelCase					=   1_25
		__UpperCAmelCase					=   6
		__UpperCAmelCase					=   {
		    0: 'table',
		    1: 'table column',
		    2: 'table row',
		    3: 'table column header',
		    4: 'table projected row header',
		    5: 'table spanning cell',
		}
		__UpperCAmelCase					=   idalabel
		__UpperCAmelCase					=   {v: k for k, v in idalabel.items()}

	__UpperCAmelCase					=   DetrImageProcessor(
	    format='coco_detection'       ,    max_size=8_00 if 'detection' in checkpoint_url else 10_00    )
	__UpperCAmelCase					=   TableTransformerForObjectDetection(_lowerCAmelCase    )
	model.load_state_dict(_lowerCAmelCase    )
	model.eval()

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

	__UpperCAmelCase					=   model(_lowerCAmelCase    )

	if "detection" in checkpoint_url:
		__UpperCAmelCase					=   (1, 15, 3)
		__UpperCAmelCase					=   torch.tensor(
		    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]    )
		__UpperCAmelCase					=   torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]    )

	else:
		__UpperCAmelCase					=   (1, 1_25, 7)
		__UpperCAmelCase					=   torch.tensor(
		    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]    )
		__UpperCAmelCase					=   torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]    )

	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...'    )
		__UpperCAmelCase					=   (
		    '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__":
				_A:					Tuple							=				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:					str							=				parser.parse_args()
				convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''




from collections.abc import Sequence







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 __UpperCAmelCase					=   0.0
 for coeff in reversed(_lowerCAmelCase    ):
  __UpperCAmelCase					=   result * x + coeff
 return result


if __name__ == "__main__":
    _A:					str							=				(0.0, 0.0, 5.0, 9.3, 7.0)
    _A:					Dict							=				10.0
    print(evaluate_poly(poly, x))
    print(horner(poly, x))





