




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







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

 # Load in the weights from the checkpoint_path
 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location="cpu"	)["module"]

 # Load the entity vocab file
 UpperCAmelCase__							 =						load_original_entity_vocab(_lowerCAmelCase	)
 # add an entry for [MASK2]
 UpperCAmelCase__							 =						max(entity_vocab.values()	) + 1
 config.entity_vocab_size += 1

 UpperCAmelCase__							 =						XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]	)

 # Add special tokens to the token vocabulary for downstream tasks
 UpperCAmelCase__							 =						AddedToken("<ent>"						,							lstrip=_lowerCAmelCase						,							rstrip=_lowerCAmelCase	)
 UpperCAmelCase__							 =						AddedToken("<ent2>"						,							lstrip=_lowerCAmelCase						,							rstrip=_lowerCAmelCase	)
 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(_lowerCAmelCase	)
 with open(os.path.join(_lowerCAmelCase						,							"tokenizer_config.json"	)						,							"r"	) as f:
  UpperCAmelCase__							 =						json.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						"MLukeTokenizer"
 with open(os.path.join(_lowerCAmelCase						,							"tokenizer_config.json"	)						,							"w"	) as f:
  json.dump(_lowerCAmelCase						,							_lowerCAmelCase	)

 with open(os.path.join(_lowerCAmelCase						,							MLukeTokenizer.vocab_files_names["entity_vocab_file"]	)						,							"w"	) as f:
  json.dump(_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase	)

 # Initialize the embeddings of the special tokens
 UpperCAmelCase__							 =						tokenizer.convert_tokens_to_ids(["@"]	)[0]
 UpperCAmelCase__							 =						tokenizer.convert_tokens_to_ids(["#"]	)[0]

 UpperCAmelCase__							 =						state_dict["embeddings.word_embeddings.weight"]
 UpperCAmelCase__							 =						word_emb[ent_init_index].unsqueeze(0	)
 UpperCAmelCase__							 =						word_emb[enta_init_index].unsqueeze(0	)
 UpperCAmelCase__							 =						torch.cat([word_emb, ent_emb, enta_emb]	)
 # add special tokens for 'entity_predictions.bias'
 for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
  UpperCAmelCase__							 =						state_dict[bias_name]
  UpperCAmelCase__							 =						decoder_bias[ent_init_index].unsqueeze(0	)
  UpperCAmelCase__							 =						decoder_bias[enta_init_index].unsqueeze(0	)
  UpperCAmelCase__							 =						torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]	)

 # 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"]:
   UpperCAmelCase__							 =						F'''encoder.layer.{layer_index}.attention.self.'''
   UpperCAmelCase__							 =						state_dict[prefix + matrix_name]
   UpperCAmelCase__							 =						state_dict[prefix + matrix_name]
   UpperCAmelCase__							 =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 UpperCAmelCase__							 =						state_dict["entity_embeddings.entity_embeddings.weight"]
 UpperCAmelCase__							 =						entity_emb[entity_vocab["[MASK]"]].unsqueeze(0	)
 UpperCAmelCase__							 =						torch.cat([entity_emb, entity_mask_emb]	)
 # add [MASK2] for 'entity_predictions.bias'
 UpperCAmelCase__							 =						state_dict["entity_predictions.bias"]
 UpperCAmelCase__							 =						entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0	)
 UpperCAmelCase__							 =						torch.cat([entity_prediction_bias, entity_mask_bias]	)

 UpperCAmelCase__							 =						LukeForMaskedLM(config=_lowerCAmelCase	).eval()

 state_dict.pop("entity_predictions.decoder.weight"	)
 state_dict.pop("lm_head.decoder.weight"	)
 state_dict.pop("lm_head.decoder.bias"	)
 UpperCAmelCase__							 =						OrderedDict()
 for key, value in state_dict.items():
  if not (key.startswith("lm_head"	) or key.startswith("entity_predictions"	)):
   UpperCAmelCase__							 =						state_dict[key]
  else:
   UpperCAmelCase__							 =						state_dict[key]

 UpperCAmelCase__ , UpperCAmelCase__							 =						model.load_state_dict(_lowerCAmelCase						,							strict=_lowerCAmelCase	)

 if set(_lowerCAmelCase	) != {"luke.embeddings.position_ids"}:
  raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''	)
 if set(_lowerCAmelCase	) != {
     "lm_head.decoder.weight",
     "lm_head.decoder.bias",
     "entity_predictions.decoder.weight",
 }:
  raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''	)

 model.tie_weights()
 assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
 assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

 # Check outputs
 UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase						,							task="entity_classification"	)

 UpperCAmelCase__							 =						"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
 UpperCAmelCase__							 =						(0, 9)
 UpperCAmelCase__							 =						tokenizer(_lowerCAmelCase						,							entity_spans=[span]						,							return_tensors="pt"	)

 UpperCAmelCase__							 =						model(**_lowerCAmelCase	)

 # Verify word hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  UpperCAmelCase__							 =						torch.Size((1, 33, 768)	)
  UpperCAmelCase__							 =						torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

 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]						,							_lowerCAmelCase						,							atol=1E-4	):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  UpperCAmelCase__							 =						torch.Size((1, 1, 768)	)
  UpperCAmelCase__							 =						torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

 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]						,							_lowerCAmelCase						,							atol=1E-4	):
  raise ValueError

 # Verify masked word/entity prediction
 UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase	)
 UpperCAmelCase__							 =						"Tokyo is the capital of <mask>."
 UpperCAmelCase__							 =						(24, 30)
 UpperCAmelCase__							 =						tokenizer(_lowerCAmelCase						,							entity_spans=[span]						,							return_tensors="pt"	)

 UpperCAmelCase__							 =						model(**_lowerCAmelCase	)

 UpperCAmelCase__							 =						encoding["input_ids"][0].tolist()
 UpperCAmelCase__							 =						input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"	)	)
 UpperCAmelCase__							 =						outputs.logits[0][mask_position_id].argmax(dim=-1	)
 assert "Japan" == tokenizer.decode(_lowerCAmelCase	)

 UpperCAmelCase__							 =						outputs.entity_logits[0][0].argmax().item()
 UpperCAmelCase__							 =						[
     entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
 ]
 assert [e for e in multilingual_predicted_entities if e.startswith("en:"	)][0] == "en:Japan"

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						["[MASK]", "[PAD]", "[UNK]"]

 UpperCAmelCase__							 =						[json.loads(_lowerCAmelCase	) for line in open(_lowerCAmelCase	)]

 UpperCAmelCase__							 =						{}
 for entry in data:
  UpperCAmelCase__							 =						entry["id"]
  for entity_name, language in entry["entities"]:
   if entity_name in SPECIAL_TOKENS:
    UpperCAmelCase__							 =						entity_id
    break
   UpperCAmelCase__							 =						F'''{language}:{entity_name}'''
   UpperCAmelCase__							 =						entity_id
 return new_mapping


if __name__ == "__main__":
  _lowerCAmelCase						:   List[str]		=   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."
  )
  _lowerCAmelCase						:   Optional[Any]		=   parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )











import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCAmelCase						:   Union[str, Any]		=   (7_2_0, 1_2_8_0)  # Height, Width
_lowerCAmelCase						:   Any		=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCAmelCase						:   List[str]		=   1 / 1_0_0
_lowerCAmelCase						:   Union[str, Any]		=   ""
_lowerCAmelCase						:   Any		=   ""
_lowerCAmelCase						:   List[str]		=   ""
_lowerCAmelCase						:   Union[str, Any]		=   2_5_0



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__ , UpperCAmelCase__							 =						get_dataset(_lowerCAmelCase						,							_lowerCAmelCase	)
 for index in range(_lowerCAmelCase	):
  UpperCAmelCase__							 =						random.sample(range(len(_lowerCAmelCase	)	)						,							4	)
  UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						update_image_and_anno(
      _lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							filter_scale=_lowerCAmelCase						,							)

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  UpperCAmelCase__							 =						random_chars(32	)
  UpperCAmelCase__							 =						path.split(os.sep	)[-1].rsplit("."						,							1	)[0]
  UpperCAmelCase__							 =						F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
  cva.imwrite(F'''{file_root}.jpg'''						,							_lowerCAmelCase						,							[cva.IMWRITE_JPEG_QUALITY, 85]	)
  print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''	)
  UpperCAmelCase__							 =						[]
  for anno in new_annos:
   UpperCAmelCase__							 =						anno[3] - anno[1]
   UpperCAmelCase__							 =						anno[4] - anno[2]
   UpperCAmelCase__							 =						anno[1] + width / 2
   UpperCAmelCase__							 =						anno[2] + height / 2
   UpperCAmelCase__							 =						F'''{anno[0]} {x_center} {y_center} {width} {height}'''
   annos_list.append(_lowerCAmelCase	)
  with open(F'''{file_root}.txt'''						,							"w"	) as outfile:
   outfile.write("\n".join(line for line in annos_list	)	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						[]
 for label_file in glob.glob(os.path.join(_lowerCAmelCase						,							"*.txt"	)	):
  UpperCAmelCase__							 =						label_file.split(os.sep	)[-1].rsplit("."						,							1	)[0]
  with open(_lowerCAmelCase	) as in_file:
   UpperCAmelCase__							 =						in_file.readlines()
  UpperCAmelCase__							 =						os.path.join(_lowerCAmelCase						,							F'''{label_name}.jpg'''	)

  UpperCAmelCase__							 =						[]
  for obj_list in obj_lists:
   UpperCAmelCase__							 =						obj_list.rstrip("\n"	).split(" "	)
   UpperCAmelCase__							 =						float(obj[1]	) - float(obj[3]	) / 2
   UpperCAmelCase__							 =						float(obj[2]	) - float(obj[4]	) / 2
   UpperCAmelCase__							 =						float(obj[1]	) + float(obj[3]	) / 2
   UpperCAmelCase__							 =						float(obj[2]	) + float(obj[4]	) / 2

   boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
  if not boxes:
   continue
  img_paths.append(_lowerCAmelCase	)
  labels.append(_lowerCAmelCase	)
 return img_paths, labels



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   tuple[int, int]						,							_lowerCAmelCase							:   tuple[float, float]						,							_lowerCAmelCase							:   float = 0.0						,							):




 """simple docstring"""







 UpperCAmelCase__							 =						np.zeros([output_size[0], output_size[1], 3]						,							dtype=np.uinta	)
 UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCAmelCase__							 =						int(scale_x * output_size[1]	)
 UpperCAmelCase__							 =						int(scale_y * output_size[0]	)

 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						[]
 for i, index in enumerate(_lowerCAmelCase	):
  UpperCAmelCase__							 =						all_img_list[index]
  path_list.append(_lowerCAmelCase	)
  UpperCAmelCase__							 =						all_annos[index]
  UpperCAmelCase__							 =						cva.imread(_lowerCAmelCase	)
  if i == 0:  # top-left
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						bbox[1] * scale_x
    UpperCAmelCase__							 =						bbox[2] * scale_y
    UpperCAmelCase__							 =						bbox[3] * scale_x
    UpperCAmelCase__							 =						bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  elif i == 1:  # top-right
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(output_size[1] - divid_point_x, divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
    UpperCAmelCase__							 =						bbox[2] * scale_y
    UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
    UpperCAmelCase__							 =						bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  elif i == 2:  # bottom-left
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, output_size[0] - divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						bbox[1] * scale_x
    UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
    UpperCAmelCase__							 =						bbox[3] * scale_x
    UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  else:  # bottom-right
   UpperCAmelCase__							 =						cva.resize(
       _lowerCAmelCase						,							(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
    UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
    UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
    UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  UpperCAmelCase__							 =						[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







 assert number_char > 1, "The number of character should greater than 1"
 UpperCAmelCase__							 =						ascii_lowercase + digits
 return "".join(random.choice(_lowerCAmelCase	) for _ in range(_lowerCAmelCase	)	)


if __name__ == "__main__":
  main()
  print("DONE ✅")





