
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        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				# Load configuration defined in the metadata file
				with open(lowerCamelCase						) as metadata_file:
								UpperCamelCase_:							Dict	=						json.load(lowerCamelCase						)
				UpperCamelCase_:							Optional[int]	=						LukeConfig(use_entity_aware_attention=lowerCamelCase     ,    **metadata["""model_config"""]						)

				# Load in the weights from the checkpoint_path
				UpperCamelCase_:							Optional[int]	=						torch.load(lowerCamelCase     ,    map_location="""cpu"""						)["""module"""]

				# Load the entity vocab file
				UpperCamelCase_:							str	=						load_original_entity_vocab(lowerCamelCase						)
				# add an entry for [MASK2]
				UpperCamelCase_:							Tuple	=						max(entity_vocab.values()						) + 1
				config.entity_vocab_size += 1

				UpperCamelCase_:							str	=						XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

				# Add special tokens to the token vocabulary for downstream tasks
				UpperCamelCase_:							Any	=						AddedToken("""<ent>"""     ,    lstrip=lowerCamelCase     ,    rstrip=lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						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_:							Optional[Any]	=						json.load(lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						"""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_:							Optional[int]	=						MLukeTokenizer.from_pretrained(lowerCamelCase						)

				# Initialize the embeddings of the special tokens
				UpperCamelCase_:							Optional[Any]	=						tokenizer.convert_tokens_to_ids(["""@"""]						)[0]
				UpperCamelCase_:							Tuple	=						tokenizer.convert_tokens_to_ids(["""#"""]						)[0]

				UpperCamelCase_:							List[str]	=						state_dict["""embeddings.word_embeddings.weight"""]
				UpperCamelCase_:							int	=						word_emb[ent_init_index].unsqueeze(0						)
				UpperCamelCase_:							Optional[Any]	=						word_emb[enta_init_index].unsqueeze(0						)
				UpperCamelCase_:							Union[str, Any]	=						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_:							List[str]	=						state_dict[bias_name]
								UpperCamelCase_:							List[str]	=						decoder_bias[ent_init_index].unsqueeze(0						)
								UpperCamelCase_:							Any	=						decoder_bias[enta_init_index].unsqueeze(0						)
								UpperCamelCase_:							Dict	=						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_:							Any	=						F'''encoder.layer.{layer_index}.attention.self.'''
												UpperCamelCase_:							Union[str, Any]	=						state_dict[prefix + matrix_name]
												UpperCamelCase_:							int	=						state_dict[prefix + matrix_name]
												UpperCamelCase_:							Optional[Any]	=						state_dict[prefix + matrix_name]

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

				UpperCamelCase_:							List[str]	=						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_:							Any	=						OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith("""lm_head"""						) or key.startswith("""entity_predictions"""						)):
												UpperCamelCase_:							Dict	=						state_dict[key]
								else:
												UpperCamelCase_:							Any	=						state_dict[key]

				UpperCamelCase_,   UpperCamelCase_:							Any	=						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_:							int	=						MLukeTokenizer.from_pretrained(lowerCamelCase     ,    task="""entity_classification"""						)

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

				UpperCamelCase_:							Any	=						model(**lowerCamelCase						)

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								UpperCamelCase_:							Optional[Any]	=						torch.Size((1, 33, 7_68)						)
								UpperCamelCase_:							List[str]	=						torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]						)

				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_:							Any	=						torch.Size((1, 1, 7_68)						)
								UpperCamelCase_:							List[Any]	=						torch.tensor([[-0.1482, 0.0609, 0.0322]]						)

				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_:							List[str]	=						MLukeTokenizer.from_pretrained(lowerCamelCase						)
				UpperCamelCase_:							List[Any]	=						"""Tokyo is the capital of <mask>."""
				UpperCamelCase_:							Union[str, Any]	=						(24, 30)
				UpperCamelCase_:							str	=						tokenizer(lowerCamelCase     ,    entity_spans=[span]     ,    return_tensors="""pt"""						)

				UpperCamelCase_:							Dict	=						model(**lowerCamelCase						)

				UpperCamelCase_:							str	=						encoding["""input_ids"""][0].tolist()
				UpperCamelCase_:							str	=						input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""						)						)
				UpperCamelCase_:							Any	=						outputs.logits[0][mask_position_id].argmax(dim=-1						)
				assert "Japan" == tokenizer.decode(lowerCamelCase						)

				UpperCamelCase_:							Optional[int]	=						outputs.entity_logits[0][0].argmax().item()
				UpperCamelCase_:							Optional[Any]	=						[
				    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        A__					(						lowerCamelCase						)  ->	str:
				UpperCamelCase_:							Optional[int]	=						["""[MASK]""", """[PAD]""", """[UNK]"""]

				UpperCamelCase_:							Optional[Any]	=						[json.loads(lowerCamelCase						) for line in open(lowerCamelCase						)]

				UpperCamelCase_:							Union[str, Any]	=						{}
				for entry in data:
								UpperCamelCase_:							Any	=						entry["""id"""]
								for entity_name, language in entry["entities"]:
												if entity_name in SPECIAL_TOKENS:
																UpperCamelCase_:							List[str]	=						entity_id
																break
												UpperCamelCase_:							Union[str, Any]	=						F'''{language}:{entity_name}'''
												UpperCamelCase_:							Dict	=						entity_id
				return new_mapping


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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_						: int         =  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 cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						cva.imread(snake_case_						,	0      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						img.shape
							UpperCamelCase_:							list[list[int]]	=						[]
							UpperCamelCase_:							int	=						img.copy()
							UpperCamelCase_:							Any	=						cva.cvtColor(snake_case_						,	cva.COLOR_GRAY2RGB      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						np.gradient(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						dx**2
							UpperCamelCase_:							Dict	=						dy**2
							UpperCamelCase_:							Optional[Any]	=						dx * dy
							UpperCamelCase_:							str	=						0.04
							UpperCamelCase_:							int	=						self.window_size // 2
							for y in range(snake_case_						,	h - offset      ):
											for x in range(snake_case_						,	w - offset      ):
															UpperCamelCase_:							List[Any]	=						ixx[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							int	=						iyy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							List[str]	=						ixy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						det - k * (trace**2)
															# Can change the value
															if r > 0.5:
																			corner_list.append([x, y, r]      )
																			color_img.itemset((y, x, 0)						,	0      )
																			color_img.itemset((y, x, 1)						,	0      )
																			color_img.itemset((y, x, 2)						,	255      )
							return color_img, corner_list


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






