


"""simple docstring"""






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_lowerCamelCase  =	logging.getLogger()
_lowerCamelCase  =	torch.cuda.is_available()
_lowerCamelCase  =	{
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}


def __lowercase  (   lowerCamelCase_	:   Path    ,       lowerCamelCase_	:   list	):
				SCREAMING_SNAKE_CASE__  				=					"\n".join(lowerCamelCase_	)
				Path(lowerCamelCase_	).open("w"	).writelines(lowerCamelCase_	)


_lowerCamelCase  =	[' Sam ate lunch today.', 'Sams lunch ingredients.']
_lowerCamelCase  =	['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_lowerCamelCase  =	'patrickvonplaten/t5-tiny-random'
_lowerCamelCase  =	'sshleifer/t5-tinier-random'
_lowerCamelCase  =	'sshleifer/bart-tiny-random'
_lowerCamelCase  =	'sshleifer/tiny-mbart'
_lowerCamelCase  =	'sshleifer/tiny-marian-en-de'
_lowerCamelCase  =	'stas/tiny-wmt19-en-de'


_lowerCamelCase  =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def __lowercase  (   lowerCamelCase_	:   int	):
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(lowerCamelCase_    ,       F'''{split}.source'''	)    ,       lowerCamelCase_	)
								_dump_articles(os.path.join(lowerCamelCase_    ,       F'''{split}.target'''	)    ,       lowerCamelCase_	)
				return tmp_dir


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				@classmethod
				def 	lowerCAmelCase__		(    cls  ):
								logging.disable(logging.CRITICAL  )  # remove noisy download output from tracebacks
								return cls



				@slow
				@require_torch_gpu
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					list_models()
								SCREAMING_SNAKE_CASE__  				=					"sshleifer"
								SCREAMING_SNAKE_CASE__  				=					[x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase__  )]
								SCREAMING_SNAKE_CASE__  				=					["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
								SCREAMING_SNAKE_CASE__  				=					[]
								for m in model_ids:
												if m in allowed_to_be_broken:
																continue
												try:
																AutoConfig.from_pretrained(UpperCAmelCase__  )
												except Exception:
																failures.append(UpperCAmelCase__  )
								assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
								self._test_distiller_cli(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					{
								    "student_encoder_layers": 2,
								    "student_decoder_layers": 1,
								    "max_epochs": 4,
								    "val_check_interval": 0.25,
								    "alpha_hid": 2.0,
								    "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
								}
								SCREAMING_SNAKE_CASE__  				=					self._test_distiller_cli(UpperCAmelCase__							,		check_contents=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					list(Path(model.output_dir  ).glob("*.ckpt"  )  )
								self.assertEqual(1							,		len(UpperCAmelCase__  )  )
								SCREAMING_SNAKE_CASE__  				=					list(Path(model.output_dir  ).glob("**/*.bin"  )  )
								self.assertEqual(len(UpperCAmelCase__  )							,		2  )
								SCREAMING_SNAKE_CASE__  				=					lmap(str.strip							,		Path(model.hparams.data_dir  ).joinpath("test.source"  ).open().readlines()  )
								SCREAMING_SNAKE_CASE__  				=					tempfile.mktemp()  # XXX: not being cleaned up
								generate_summaries_or_translations(UpperCAmelCase__							,		UpperCAmelCase__							,		str(model.output_dir / "best_tfmr"  )  )
								self.assertTrue(Path(UpperCAmelCase__  ).exists()  )

								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()
								convert_pl_to_hf(ckpts[0]							,		transformer_ckpts[0].parent							,		UpperCAmelCase__  )
								assert os.path.exists(os.path.join(UpperCAmelCase__							,		"pytorch_model.bin"  )  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
								SCREAMING_SNAKE_CASE__  				=					torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]							,		dtype=torch.long							,		device=model.device  )
								SCREAMING_SNAKE_CASE__  				=					target_ids[:, :-1].contiguous()  # Why this line?
								SCREAMING_SNAKE_CASE__  				=					target_ids[:, 1:].clone()  # why clone?
								SCREAMING_SNAKE_CASE__  				=					model(
								    UpperCAmelCase__							,		attention_mask=UpperCAmelCase__							,		decoder_input_ids=UpperCAmelCase__							,		labels=UpperCAmelCase__							,		use_cache=UpperCAmelCase__  ).loss

								SCREAMING_SNAKE_CASE__  				=					model(UpperCAmelCase__							,		attention_mask=UpperCAmelCase__							,		decoder_input_ids=UpperCAmelCase__							,		use_cache=UpperCAmelCase__  ).logits

								SCREAMING_SNAKE_CASE__  				=					nn.functional.log_softmax(UpperCAmelCase__							,		dim=-1  )
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					label_smoothed_nll_loss(
								    UpperCAmelCase__							,		UpperCAmelCase__							,		0.1							,		ignore_index=model.config.pad_token_id  )
								with self.assertRaises(UpperCAmelCase__  ):
												# TODO: understand why this breaks
												self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					{
								    "student_encoder_layers": 2,
								    "student_decoder_layers": 1,
								    "num_train_epochs": 4,
								    "val_check_interval": 0.25,
								    "alpha_hid": 2.0,
								    "task": "translation",
								    "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
								    "tokenizer_name": MBART_TINY,
								    "teacher": MBART_TINY,
								    "src_lang": "en_XX",
								    "tgt_lang": "ro_RO",
								}
								SCREAMING_SNAKE_CASE__  				=					self._test_distiller_cli(UpperCAmelCase__							,		check_contents=UpperCAmelCase__  )
								assert model.model.config.model_type == "mbart"

								SCREAMING_SNAKE_CASE__  				=					list(Path(model.output_dir  ).glob("*.ckpt"  )  )
								self.assertEqual(1							,		len(UpperCAmelCase__  )  )
								SCREAMING_SNAKE_CASE__  				=					list(Path(model.output_dir  ).glob("**/*.bin"  )  )
								SCREAMING_SNAKE_CASE__  				=					list(Path(model.output_dir  ).glob("best_tfmr/*"  )  )
								assert len(UpperCAmelCase__  ) > 2
								self.assertEqual(len(UpperCAmelCase__  )							,		2  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					{
								    "student_encoder_layers": 1,
								    "student_decoder_layers": 1,
								    "alpha_hid": 2.0,
								    "teacher": T5_TINY,
								    "model_name_or_path": T5_TINY,
								    "tokenizer_name": T5_TINY,
								}
								self._test_distiller_cli(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					{
								    "teacher": T5_TINY,
								    "student": T5_TINIER,
								    "model_name_or_path": T5_TINIER,
								    "tokenizer_name": T5_TINIER,
								}
								self._test_distiller_cli(UpperCAmelCase__  )






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__=True  ):
								SCREAMING_SNAKE_CASE__  				=					{
								    "label_smoothing": 0.0,
								    "early_stopping_patience": -1,
								    "train_batch_size": 1,
								    "eval_batch_size": 2,
								    "max_epochs": 2,
								    "alpha_mlm": 0.2,
								    "alpha_ce": 0.8,
								    "do_predict": True,
								    "model_name_or_path": "sshleifer/tinier_bart",
								    "teacher": CHEAP_ARGS["model_name_or_path"],
								    "val_check_interval": 0.5,
								}
								default_updates.update(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					CHEAP_ARGS.copy()
								SCREAMING_SNAKE_CASE__  				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()

								args_d.update(data_dir=UpperCAmelCase__							,		output_dir=UpperCAmelCase__							,		**UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					distill_main(argparse.Namespace(**UpperCAmelCase__  )  )
								if not check_contents:
												return model
								SCREAMING_SNAKE_CASE__  				=					os.listdir(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					{os.path.basename(UpperCAmelCase__  ) for p in contents}
								SCREAMING_SNAKE_CASE__  				=					[p for p in contents if p.endswith("ckpt"  )]
								assert len(UpperCAmelCase__  ) > 0

								self.assertIn("test_generations.txt"							,		UpperCAmelCase__  )
								self.assertIn("test_results.txt"							,		UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					load_json(model.metrics_save_path  )
								SCREAMING_SNAKE_CASE__  				=					metrics["val"][-1]
								self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]							,		0.01  )
								self.assertGreaterEqual(1.0							,		last_step_stats["val_avg_gen_time"]  )
								self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1  )
								self.assertEqual(len(metrics["val"]  )							,		UpperCAmelCase__  )
								self.assertEqual(len(metrics["test"]  )							,		1  )
								return model


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]							,		)
				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					CHEAP_ARGS.copy()
								SCREAMING_SNAKE_CASE__  				=					"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
								SCREAMING_SNAKE_CASE__  				=					0.1 if task == "translation" else 0

								SCREAMING_SNAKE_CASE__  				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()
								args_d.update(
								    data_dir=UpperCAmelCase__							,		model_name_or_path=UpperCAmelCase__							,		tokenizer_name=UpperCAmelCase__							,		train_batch_size=2							,		eval_batch_size=2							,		output_dir=UpperCAmelCase__							,		do_predict=UpperCAmelCase__							,		task=UpperCAmelCase__							,		src_lang="en_XX"							,		tgt_lang="ro_RO"							,		freeze_encoder=UpperCAmelCase__							,		freeze_embeds=UpperCAmelCase__							,		)
								assert "n_train" in args_d
								SCREAMING_SNAKE_CASE__  				=					argparse.Namespace(**UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					main(UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					module.model.get_input_embeddings()
								assert not input_embeds.weight.requires_grad
								if model == T5_TINY:
												SCREAMING_SNAKE_CASE__  				=					module.model.lm_head
												assert not lm_head.weight.requires_grad
												assert (lm_head.weight == input_embeds.weight).all().item()
								elif model == FSMT_TINY:
												SCREAMING_SNAKE_CASE__  				=					module.model.model
												SCREAMING_SNAKE_CASE__  				=					fsmt.decoder.embed_positions
												assert not embed_pos.weight.requires_grad
												assert not fsmt.decoder.embed_tokens.weight.requires_grad
												# check that embeds are not the same
												assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
								else:
												SCREAMING_SNAKE_CASE__  				=					module.model.model
												SCREAMING_SNAKE_CASE__  				=					bart.decoder.embed_positions
												assert not embed_pos.weight.requires_grad
												assert not bart.shared.weight.requires_grad
												# check that embeds are the same
												assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
												assert bart.decoder.embed_tokens == bart.shared

								SCREAMING_SNAKE_CASE__  				=					load_json(module.output_dir / "text_batch.json"  )
								assert isinstance(UpperCAmelCase__							,		UpperCAmelCase__  )
								assert len(UpperCAmelCase__  ) >= 4



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					CHEAP_ARGS.copy()

								SCREAMING_SNAKE_CASE__  				=					"summarization"
								SCREAMING_SNAKE_CASE__  				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

								args_d.update(
								    data_dir=UpperCAmelCase__							,		tokenizer_name=UpperCAmelCase__							,		train_batch_size=2							,		eval_batch_size=2							,		do_predict=UpperCAmelCase__							,		task=UpperCAmelCase__							,		src_lang="en_XX"							,		tgt_lang="ro_RO"							,		freeze_encoder=UpperCAmelCase__							,		freeze_embeds=UpperCAmelCase__							,		)

								# test models whose config includes the extra_model_args
								SCREAMING_SNAKE_CASE__  				=					BART_TINY
								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE__  				=					args_d.copy()
								args_da.update(
								    model_name_or_path=UpperCAmelCase__							,		output_dir=UpperCAmelCase__							,		)
								SCREAMING_SNAKE_CASE__  				=					("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
								for p in extra_model_params:
												SCREAMING_SNAKE_CASE__  				=					0.5
								SCREAMING_SNAKE_CASE__  				=					argparse.Namespace(**UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					main(UpperCAmelCase__  )
								for p in extra_model_params:
												assert getattr(model.config							,		UpperCAmelCase__  ) == 0.5, f'''failed to override the model config for param {p}'''

								# test models whose config doesn't include the extra_model_args
								SCREAMING_SNAKE_CASE__  				=					T5_TINY
								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE__  				=					args_d.copy()
								args_da.update(
								    model_name_or_path=UpperCAmelCase__							,		output_dir=UpperCAmelCase__							,		)
								SCREAMING_SNAKE_CASE__  				=					"encoder_layerdrop"
								SCREAMING_SNAKE_CASE__  				=					0.5
								SCREAMING_SNAKE_CASE__  				=					argparse.Namespace(**UpperCAmelCase__  )
								with pytest.raises(UpperCAmelCase__  ) as excinfo:
												SCREAMING_SNAKE_CASE__  				=					main(UpperCAmelCase__  )
								assert str(excinfo.value  ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					CHEAP_ARGS.copy()

								SCREAMING_SNAKE_CASE__  				=					"summarization"
								SCREAMING_SNAKE_CASE__  				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

								SCREAMING_SNAKE_CASE__  				=					BART_TINY
								SCREAMING_SNAKE_CASE__  				=					self.get_auto_remove_tmp_dir()

								args_d.update(
								    data_dir=UpperCAmelCase__							,		model_name_or_path=UpperCAmelCase__							,		output_dir=UpperCAmelCase__							,		tokenizer_name=UpperCAmelCase__							,		train_batch_size=2							,		eval_batch_size=2							,		do_predict=UpperCAmelCase__							,		task=UpperCAmelCase__							,		src_lang="en_XX"							,		tgt_lang="ro_RO"							,		freeze_encoder=UpperCAmelCase__							,		freeze_embeds=UpperCAmelCase__							,		)

								# emulate finetune.py
								SCREAMING_SNAKE_CASE__  				=					argparse.ArgumentParser()
								SCREAMING_SNAKE_CASE__  				=					pl.Trainer.add_argparse_args(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					SummarizationModule.add_model_specific_args(UpperCAmelCase__							,		os.getcwd()  )
								SCREAMING_SNAKE_CASE__  				=					{"--help": True}

								# --help test
								with pytest.raises(UpperCAmelCase__  ) as excinfo:
												with CaptureStdout() as cs:
																SCREAMING_SNAKE_CASE__  				=					parser.parse_args(UpperCAmelCase__  )
												assert False, "--help is expected to sys.exit"
								assert excinfo.type == SystemExit
								SCREAMING_SNAKE_CASE__  				=					lightning_base.arg_to_scheduler_metavar
								assert expected in cs.out, "--help is expected to list the supported schedulers"

								# --lr_scheduler=non_existing_scheduler test
								SCREAMING_SNAKE_CASE__  				=					"non_existing_scheduler"
								SCREAMING_SNAKE_CASE__  				=					{f'''--lr_scheduler={unsupported_param}'''}
								with pytest.raises(UpperCAmelCase__  ) as excinfo:
												with CaptureStderr() as cs:
																SCREAMING_SNAKE_CASE__  				=					parser.parse_args(UpperCAmelCase__  )
												assert False, "invalid argument is expected to sys.exit"
								assert excinfo.type == SystemExit
								SCREAMING_SNAKE_CASE__  				=					f'''invalid choice: \'{unsupported_param}\''''
								assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

								# --lr_scheduler=existing_scheduler test
								SCREAMING_SNAKE_CASE__  				=					"cosine"
								SCREAMING_SNAKE_CASE__  				=					args_d.copy()
								SCREAMING_SNAKE_CASE__  				=					supported_param
								SCREAMING_SNAKE_CASE__  				=					argparse.Namespace(**UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					main(UpperCAmelCase__  )
								assert (
								    getattr(model.hparams							,		"lr_scheduler"  ) == supported_param
								), f'''lr_scheduler={supported_param} shouldn\'t fail'''




"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
