
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 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     str:







				"""simple docstring"""



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

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

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

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

				# Add special tokens to the token vocabulary for downstream tasks
				__A             =			AddedToken("<ent>"		,       lstrip=_lowerCAmelCase		,       rstrip=_lowerCAmelCase						)
				__A             =			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:
								__A             =			json.load(_lowerCAmelCase						)
				__A             =			"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						)

				__A             =			MLukeTokenizer.from_pretrained(_lowerCAmelCase						)

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

				__A             =			state_dict["embeddings.word_embeddings.weight"]
				__A             =			word_emb[ent_init_index].unsqueeze(0						)
				__A             =			word_emb[enta_init_index].unsqueeze(0						)
				__A             =			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"]:
								__A             =			state_dict[bias_name]
								__A             =			decoder_bias[ent_init_index].unsqueeze(0						)
								__A             =			decoder_bias[enta_init_index].unsqueeze(0						)
								__A             =			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"]:
												__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]"]].unsqueeze(0						)
				__A             =			torch.cat([entity_emb, entity_mask_emb]						)
				# add [MASK2] for 'entity_predictions.bias'
				__A             =			state_dict["entity_predictions.bias"]
				__A             =			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0						)
				__A             =			torch.cat([entity_prediction_bias, entity_mask_bias]						)

				__A             =			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"						)
				__A             =			OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith("lm_head"						) or key.startswith("entity_predictions"						)):
												__A             =			state_dict[key]
								else:
												__A             =			state_dict[key]

				__A             =			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
				__A             =			MLukeTokenizer.from_pretrained(_lowerCAmelCase		,       task="entity_classification"						)

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

				__A             =			model(**_lowerCAmelCase						)

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								__A             =			torch.Size((1, 3_3, 7_6_8)						)
								__A             =			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
								__A             =			torch.Size((1, 1, 7_6_8)						)
								__A             =			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
				__A             =			MLukeTokenizer.from_pretrained(_lowerCAmelCase						)
				__A             =			"Tokyo is the capital of <mask>."
				__A             =			(2_4, 3_0)
				__A             =			tokenizer(_lowerCAmelCase		,       entity_spans=[span]		,       return_tensors="pt"						)

				__A             =			model(**_lowerCAmelCase						)

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

				__A             =			outputs.entity_logits[0][0].argmax().item()
				__A             =			[
				    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 						UpperCAmelCase      (     a_						)		->     Any:







				"""simple docstring"""



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

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[Any]										=    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.'
					)
					SCREAMING_SNAKE_CASE							:List[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,
					)


from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       )		->     float:







				"""simple docstring"""



				if donor_conc <= 0:
								raise ValueError("Donor concentration should be positive"						)
				elif acceptor_conc <= 0:
								raise ValueError("Acceptor concentration should be positive"						)
				elif intrinsic_conc <= 0:
								raise ValueError("Intrinsic concentration should be positive"						)
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    "Donor concentration should be greater than intrinsic concentration"						)
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    "Acceptor concentration should be greater than intrinsic concentration"						)
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
