






"""simple docstring"""

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:Optional[Any] ,						__lowerCamelCase:Any ,						__lowerCamelCase:List[str] ,						__lowerCamelCase:str ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






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

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

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

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

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

						__magic_name__            =     MLukeTokenizer.from_pretrained(__lowerCamelCase   )

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

						__magic_name__            =     state_dict["embeddings.word_embeddings.weight"]
						__magic_name__            =     word_emb[ent_init_index].unsqueeze(0   )
						__magic_name__            =     word_emb[enta_init_index].unsqueeze(0   )
						__magic_name__            =     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"]:
												__magic_name__            =     state_dict[bias_name]
												__magic_name__            =     decoder_bias[ent_init_index].unsqueeze(0   )
												__magic_name__            =     decoder_bias[enta_init_index].unsqueeze(0   )
												__magic_name__            =     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"]:
																		__magic_name__            =     f'''encoder.layer.{layer_index}.attention.self.'''
																		__magic_name__            =     state_dict[prefix + matrix_name]
																		__magic_name__            =     state_dict[prefix + matrix_name]
																		__magic_name__            =     state_dict[prefix + matrix_name]

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

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

						__magic_name__      ,    __magic_name__            =     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
						__magic_name__            =     MLukeTokenizer.from_pretrained(__lowerCamelCase ,						task="entity_classification"   )

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

						__magic_name__            =     model(**__lowerCamelCase   )

						# Verify word hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												__magic_name__            =     torch.Size((1, 3_3, 7_6_8)   )
												__magic_name__            =     torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]   )

						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
												__magic_name__            =     torch.Size((1, 1, 7_6_8)   )
												__magic_name__            =     torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]   )

						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
						__magic_name__            =     MLukeTokenizer.from_pretrained(__lowerCamelCase   )
						__magic_name__            =     "Tokyo is the capital of <mask>."
						__magic_name__            =     (2_4, 3_0)
						__magic_name__            =     tokenizer(__lowerCamelCase ,						entity_spans=[span] ,						return_tensors="pt"   )

						__magic_name__            =     model(**__lowerCamelCase   )

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

						__magic_name__            =     outputs.entity_logits[0][0].argmax().item()
						__magic_name__            =     [
						    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:int   ):

						'''simple docstring'''






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

						__magic_name__            =     [json.loads(__lowerCamelCase   ) for line in open(__lowerCamelCase   )]

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


if __name__ == "__main__":
			lowercase    			=       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.'''
			)
			lowercase    			=       parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)














"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



