

"""simple docstring"""


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A						       =      getLogger(__name__)


__A						       =      "cuda" if torch.cuda.is_available() else "cpu"






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 8 ,			__SCREAMING_SNAKE_CASE = DEFAULT_DEVICE ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE="summarization" ,			__SCREAMING_SNAKE_CASE=None ,			**__SCREAMING_SNAKE_CASE ,			)     ->     Dict:
		__lowerCAmelCase:	Optional[int]   				=      Path(__SCREAMING_SNAKE_CASE			).open("w" ,			encoding="utf-8"			)
		__lowerCAmelCase:	Any   				=      str(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			)
		if fpaa:
				__lowerCAmelCase:	Union[str, Any]   				=      model.half()

		__lowerCAmelCase:	List[Any]   				=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"			)  # if this is wrong, check config.model_type.

		__lowerCAmelCase:	List[str]   				=      time.time()
		# update config with task specific params
		use_task_specific_params(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		if prefix is None:
				__lowerCAmelCase:	Any   				=      prefix or getattr(model.config ,			"prefix" ,			""			) or ""
		for examples_chunk in tqdm(list(chunks(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)			):
				__lowerCAmelCase:	Any   				=      [prefix + text for text in examples_chunk]
				__lowerCAmelCase:	List[Any]   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			return_tensors="pt" ,			truncation=__SCREAMING_SNAKE_CASE ,			padding="longest"			).to(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Tuple   				=      model.generate(
				    input_ids=batch.input_ids ,			attention_mask=batch.attention_mask ,			**__SCREAMING_SNAKE_CASE ,			)
				__lowerCAmelCase:	Optional[Any]   				=      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,			skip_special_tokens=__SCREAMING_SNAKE_CASE ,			clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE			)
				for hypothesis in dec:
						fout.write(hypothesis + "\n"			)
						fout.flush()
		fout.close()
		__lowerCAmelCase:	Optional[int]   				=      int(time.time() - start_time			)  # seconds
		__lowerCAmelCase:	Optional[Any]   				=      len(__SCREAMING_SNAKE_CASE			)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,			4			)}






def    a__   (   )     ->     List[Any]:
		return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"			)






def    a__   (   __SCREAMING_SNAKE_CASE=True			)     ->     Dict:
		__lowerCAmelCase:	Union[str, Any]   				=      argparse.ArgumentParser()
		parser.add_argument("model_name" ,			type=__SCREAMING_SNAKE_CASE ,			help="like facebook/bart-large-cnn,t5-base, etc."			)
		parser.add_argument("input_path" ,			type=__SCREAMING_SNAKE_CASE ,			help="like cnn_dm/test.source"			)
		parser.add_argument("save_path" ,			type=__SCREAMING_SNAKE_CASE ,			help="where to save summaries"			)
		parser.add_argument("--reference_path" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			help="like cnn_dm/test.target"			)
		parser.add_argument("--score_path" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default="metrics.json" ,			help="where to save metrics"			)
		parser.add_argument("--device" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="cuda, cuda:1, cpu etc."			)
		parser.add_argument(
		    "--prefix" ,			type=__SCREAMING_SNAKE_CASE ,			required=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="will be added to the begininng of src examples"			)
		parser.add_argument("--task" ,			type=__SCREAMING_SNAKE_CASE ,			default="summarization" ,			help="used for task_specific_params + metrics"			)
		parser.add_argument("--bs" ,			type=__SCREAMING_SNAKE_CASE ,			default=8 ,			required=__SCREAMING_SNAKE_CASE ,			help="batch size"			)
		parser.add_argument(
		    "--n_obs" ,			type=__SCREAMING_SNAKE_CASE ,			default=-1 ,			required=__SCREAMING_SNAKE_CASE ,			help="How many observations. Defaults to all."			)
		parser.add_argument("--fp16" ,			action="store_true"			)
		parser.add_argument("--dump-args" ,			action="store_true" ,			help="print the custom hparams with the results"			)
		parser.add_argument(
		    "--info" ,			nargs="?" ,			type=__SCREAMING_SNAKE_CASE ,			const=datetime_now() ,			help=(
		        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
		        " lang=en-ru. If no value is passed, the current datetime string will be used."
		    ) ,			)
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      parser.parse_known_args()
		__lowerCAmelCase:	List[str]   				=      parse_numeric_n_bool_cl_kwargs(__SCREAMING_SNAKE_CASE			)
		if parsed_args and verbose:
				print(F"parsed the following generate kwargs: {parsed_args}"			)
		__lowerCAmelCase:	Any   				=      [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path			).readlines()]
		if args.n_obs > 0:
				__lowerCAmelCase:	Optional[int]   				=      examples[: args.n_obs]
		Path(args.save_path			).parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)

		if args.reference_path is None and Path(args.score_path			).exists():
				warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."			)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError("Can't mix --fp16 and --device cpu"			)

		__lowerCAmelCase:	Dict   				=      generate_summaries_or_translations(
		    __SCREAMING_SNAKE_CASE ,			args.save_path ,			args.model_name ,			batch_size=args.bs ,			device=args.device ,			fpaa=args.fpaa ,			task=args.task ,			prefix=args.prefix ,			**__SCREAMING_SNAKE_CASE ,			)

		if args.reference_path is None:
				return {}

		# Compute scores
		__lowerCAmelCase:	List[Any]   				=      calculate_bleu if "translation" in args.task else calculate_rouge
		__lowerCAmelCase:	Optional[int]   				=      [x.rstrip() for x in open(args.save_path			).readlines()]
		__lowerCAmelCase:	Optional[Any]   				=      [x.rstrip() for x in open(args.reference_path			).readlines()][: len(__SCREAMING_SNAKE_CASE			)]
		__lowerCAmelCase:	dict   				=      score_fn(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		scores.update(__SCREAMING_SNAKE_CASE			)

		if args.dump_args:
				scores.update(__SCREAMING_SNAKE_CASE			)
		if args.info:
				__lowerCAmelCase:	Optional[int]   				=      args.info

		if verbose:
				print(__SCREAMING_SNAKE_CASE			)

		if args.score_path is not None:
				json.dump(__SCREAMING_SNAKE_CASE ,			open(args.score_path ,			"w"			)			)

		return scores


if __name__ == "__main__":
			# Usage for MT:
			# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
			run_generate(verbose=True)





"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		# Load configuration defined in the metadata file
		with open(__SCREAMING_SNAKE_CASE			) as metadata_file:
				__lowerCAmelCase:	List[Any]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE ,			**metadata["model_config"]			)

		# Load in the weights from the checkpoint_path
		__lowerCAmelCase:	Optional[Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["module"]

		# Load the entity vocab file
		__lowerCAmelCase:	List[Any]   				=      load_original_entity_vocab(__SCREAMING_SNAKE_CASE			)
		# add an entry for [MASK2]
		__lowerCAmelCase:	Any   				=      max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

		__lowerCAmelCase:	Union[str, Any]   				=      XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

		# Add special tokens to the token vocabulary for downstream tasks
		__lowerCAmelCase:	str   				=      AddedToken("<ent>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      AddedToken("<ent2>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		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(__SCREAMING_SNAKE_CASE			)
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"r"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      "MLukeTokenizer"
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

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

		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# Initialize the embeddings of the special tokens
		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_tokens_to_ids(["@"]			)[0]
		__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(["#"]			)[0]

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

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

		__lowerCAmelCase:	Optional[int]   				=      LukeForMaskedLM(config=__SCREAMING_SNAKE_CASE			).eval()

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

		__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      model.load_state_dict(__SCREAMING_SNAKE_CASE ,			strict=__SCREAMING_SNAKE_CASE			)

		if set(__SCREAMING_SNAKE_CASE			) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"			)
		if set(__SCREAMING_SNAKE_CASE			) != {
		    "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
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,			task="entity_classification"			)

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

		__lowerCAmelCase:	int   				=      model(**__SCREAMING_SNAKE_CASE			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Dict   				=      torch.Size((1, 3_3, 7_6_8)			)
				__lowerCAmelCase:	Optional[int]   				=      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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Union[str, Any]   				=      torch.Size((1, 1, 7_6_8)			)
				__lowerCAmelCase:	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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify masked word/entity prediction
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      "Tokyo is the capital of <mask>."
		__lowerCAmelCase:	List[str]   				=      (2_4, 3_0)
		__lowerCAmelCase:	int   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	Union[str, Any]   				=      model(**__SCREAMING_SNAKE_CASE			)

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

		__lowerCAmelCase:	Any   				=      outputs.entity_logits[0][0].argmax().item()
		__lowerCAmelCase:	Union[str, 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(__SCREAMING_SNAKE_CASE			)			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Tuple   				=      ["[MASK]", "[PAD]", "[UNK]"]

		__lowerCAmelCase:	Optional[Any]   				=      [json.loads(__SCREAMING_SNAKE_CASE			) for line in open(__SCREAMING_SNAKE_CASE			)]

		__lowerCAmelCase:	str   				=      {}
		for entry in data:
				__lowerCAmelCase:	Tuple   				=      entry["id"]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__lowerCAmelCase:	Optional[int]   				=      entity_id
								break
						__lowerCAmelCase:	Optional[Any]   				=      F"{language}:{entity_name}"
						__lowerCAmelCase:	Optional[int]   				=      entity_id
		return new_mapping


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


