
"""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()
UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_       :    Optional[Any]						      =			[]
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 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_								=    state_dict.pop(__a					)
					A_								=    val
def 				A_  (__a					):

					'''simple docstring'''





					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 				A_  (__a					):

					'''simple docstring'''





					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[:256, :]
										A_								=    in_proj_bias[:256]
										A_								=    in_proj_weight[256:512, :]
										A_								=    in_proj_bias[256:512]
										A_								=    in_proj_weight[-256:, :]
										A_								=    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
										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[:256, :]
										A_								=    in_proj_bias[:256]
										A_								=    in_proj_weight[256:512, :]
										A_								=    in_proj_bias[256:512]
										A_								=    in_proj_weight[-256:, :]
										A_								=    in_proj_bias[-256:]
										# 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[:256, :]
										A_								=    in_proj_bias_cross_attn[:256]
										A_								=    in_proj_weight_cross_attn[256:512, :]
										A_								=    in_proj_bias_cross_attn[256:512]
										A_								=    in_proj_weight_cross_attn[-256:, :]
										A_								=    in_proj_bias_cross_attn[-256:]
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_			,     A_								=    image.size
					A_								=    max(__a					, __a					)
					A_								=    800 if "detection" in checkpoint_url else 1000
					A_								=    target_max_size / current_max_size
					A_								=    image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

					return resized_image
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    F.to_tensor(__a					)
					A_								=    F.normalize(__a					, mean=[0.485, 0.456, 0.406]					, std=[0.229, 0.224, 0.225]					)
					return image
@torch.no_grad()
def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					logger.info("Converting model..."					)

					# load original state dict
					A_								=    torch.hub.load_state_dict_from_url(__a					, map_location="cpu"					)
					# rename keys
					for src, dest in rename_keys:
										rename_key(__a					, __a					, __a					)
					A_								=    rename_backbone_keys(__a					)
					# query, key and value matrices need special treatment
					read_in_q_k_v(__a					)
					# 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(__a					)
															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_								=    15
										A_								=    2
										A_								=    {0: "table", 1: "table rotated"}
										A_								=    idalabel
										A_								=    {v: k for k, v in idalabel.items()}
					else:
										A_								=    125
										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=800 if "detection" in checkpoint_url else 1000					)
					A_								=    TableTransformerForObjectDetection(__a					)
					model.load_state_dict(__a					)
					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=__a					)
					A_								=    Image.open(__a					).convert("RGB"					)
					A_								=    normalize(resize(__a					, __a					)					).unsqueeze(0					)

					A_								=    model(__a					)

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

					else:
										A_								=    (1, 125, 7)
										A_								=    torch.tensor(
										    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]					)
										A_								=    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]					, __a					, atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]					, __a					, 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(__a					).mkdir(exist_ok=__a					)
										model.save_pretrained(__a					)
										image_processor.save_pretrained(__a					)

					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(__a					)
										image_processor.push_to_hub(__a					)


if __name__ == "__main__":
		UpperCamelCase_       :    List[str]						      =			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_       :    Dict						      =			parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				A_  (__a					, __a					, __a					, __a					, __a					):

					'''simple docstring'''





					with open(__a					) as metadata_file:
										A_								=    json.load(__a					)
					A_								=    LukeConfig(use_entity_aware_attention=__a					, **metadata["model_config"]					)

					# Load in the weights from the checkpoint_path
					A_								=    torch.load(__a					, map_location="cpu"					)

					# Load the entity vocab file
					A_								=    load_entity_vocab(__a					)

					A_								=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]					)

					# Add special tokens to the token vocabulary for downstream tasks
					A_								=    AddedToken("<ent>"					, lstrip=__a					, rstrip=__a					)
					A_								=    AddedToken("<ent2>"					, lstrip=__a					, rstrip=__a					)
					tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}					)
					config.vocab_size += 2

					print(f'Saving tokenizer to {pytorch_dump_folder_path}'					)
					tokenizer.save_pretrained(__a					)
					with open(os.path.join(__a					, LukeTokenizer.vocab_files_names["entity_vocab_file"]					)					, "w"					) as f:
										json.dump(__a					, __a					)

					A_								=    LukeTokenizer.from_pretrained(__a					)

					# Initialize the embeddings of the special tokens
					A_								=    state_dict["embeddings.word_embeddings.weight"]
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["@"]					)[0]].unsqueeze(0					)
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["#"]					)[0]].unsqueeze(0					)
					A_								=    torch.cat([word_emb, ent_emb, enta_emb]					)

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers					):
										for matrix_name in ["query.weight", "query.bias"]:
															A_								=    f'encoder.layer.{layer_index}.attention.self.'
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					A_								=    state_dict["entity_embeddings.entity_embeddings.weight"]
					A_								=    entity_emb[entity_vocab["[MASK]"]]

					A_								=    LukeModel(config=__a					).eval()

					A_			,     A_								=    model.load_state_dict(__a					, strict=__a					)
					if not (len(__a					) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(f'Missing keys {", ".join(__a					)}. Expected only missing embeddings.position_ids'					)
					if not (all(key.startswith("entity_predictions"					) or key.startswith("lm_head"					) for key in unexpected_keys					)):
										raise ValueError(
										    "Unexpected keys"
										    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"					) or key.startswith("lm_head"					))]					)}'					)

					# Check outputs
					A_								=    LukeTokenizer.from_pretrained(__a					, task="entity_classification"					)

					A_								=    (
					    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
					    " new world number one avoid a humiliating second- round exit at Wimbledon ."
					)
					A_								=    (39, 42)
					A_								=    tokenizer(__a					, entity_spans=[span]					, add_prefix_space=__a					, return_tensors="pt"					)

					A_								=    model(**__a					)

					# Verify word hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 42, 1024)					)
										A_								=    torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]					)
					else:  # base
										A_								=    torch.Size((1, 42, 768)					)
										A_								=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]					)

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'					)
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 1, 1024)					)
										A_								=    torch.tensor([[0.0466, -0.0106, -0.0179]]					)
					else:  # base
										A_								=    torch.Size((1, 1, 768)					)
										A_								=    torch.tensor([[0.1457, 0.1044, 0.0174]]					)

					if not (outputs.entity_last_hidden_state.shape != expected_shape):
										raise ValueError(
										    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
										    f' {expected_shape}'					)
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Finally, save our PyTorch model and tokenizer
					print("Saving PyTorch model to {}".format(__a					)					)
					model.save_pretrained(__a					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    {}
					with open(__a					, "r"					, encoding="utf-8"					) as f:
										for index, line in enumerate(__a					):
															A_			,     A_								=    line.rstrip().split("\t"					)
															A_								=    index

					return entity_vocab


if __name__ == "__main__":
		UpperCamelCase_       :    Dict						      =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
		parser.add_argument(
		    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
		)
		parser.add_argument(
		    '''--entity_vocab_path''',
		    default=None,
		    type=str,
		    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
		)
		UpperCamelCase_       :    List[str]						      =			parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




