

"""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)

a__ :	Dict						= logging.getLogger()
a__ :	Any						= torch.cuda.is_available()
a__ :	List[str]						= {
    """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 				__UpperCamelCase   (    a__: Path ,	a__: list			)				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			'''\n'''.join(a__			)
					Path(a__			).open('''w'''			).writelines(a__			)


a__ :	int						= [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__ :	str						= ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__ :	Optional[int]						= """patrickvonplaten/t5-tiny-random"""
a__ :	Tuple						= """sshleifer/t5-tinier-random"""
a__ :	Union[str, Any]						= """sshleifer/bart-tiny-random"""
a__ :	Any						= """sshleifer/tiny-mbart"""
a__ :	Optional[Any]						= """sshleifer/tiny-marian-en-de"""
a__ :	str						= """stas/tiny-wmt19-en-de"""


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





def 				__UpperCamelCase   (    a__: Tuple			)				->			Tuple:




					"""simple docstring"""



					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(a__ ,	f'''{split}.source'''			) ,	a__			)
										_dump_articles(os.path.join(a__ ,	f'''{split}.target'''			) ,	a__			)
					return tmp_dir


class 			_snake_case     (						UpperCamelCase       ):







						@classmethod
						def        _UpperCamelCase    (cls							)			->						List[Any]:
											logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
											return cls







						@slow
						@require_torch_gpu
						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			list_models()
											__lowercase												=			'''sshleifer'''
											__lowercase												=			[x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_							)]
											__lowercase												=			['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
											__lowercase												=			[]
											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        _UpperCamelCase    (self							)			->						Union[str, Any]:
											__lowercase												=			{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
											self._test_distiller_cli(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						List[str]:
											__lowercase												=			{
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''max_epochs''': 4,
											    '''val_check_interval''': 0.2_5,
											    '''alpha_hid''': 2.0,
											    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
											}
											__lowercase												=			self._test_distiller_cli(UpperCAmelCase_     ,							check_contents=UpperCAmelCase_							)

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

											__lowercase												=			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        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_							)
											__lowercase						, __lowercase												=			model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
											__lowercase												=			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]     ,							dtype=torch.long     ,							device=model.device							)
											__lowercase												=			target_ids[:, :-1].contiguous()  # Why this line?
											__lowercase												=			target_ids[:, 1:].clone()  # why clone?
											__lowercase												=			model(
											    UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							decoder_input_ids=UpperCAmelCase_     ,							labels=UpperCAmelCase_     ,							use_cache=UpperCAmelCase_							).loss

											__lowercase												=			model(UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							decoder_input_ids=UpperCAmelCase_     ,							use_cache=UpperCAmelCase_							).logits

											__lowercase												=			nn.functional.log_softmax(UpperCAmelCase_     ,							dim=-1							)
											__lowercase						, __lowercase												=			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        _UpperCamelCase    (self							)			->						int:
											__lowercase												=			{
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''num_train_epochs''': 4,
											    '''val_check_interval''': 0.2_5,
											    '''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''',
											}
											__lowercase												=			self._test_distiller_cli(UpperCAmelCase_     ,							check_contents=UpperCAmelCase_							)
											assert model.model.config.model_type == "mbart"

											__lowercase												=			list(Path(model.output_dir							).glob('''*.ckpt'''							)							)
											self.assertEqual(1     ,							len(UpperCAmelCase_							)							)
											__lowercase												=			list(Path(model.output_dir							).glob('''**/*.bin'''							)							)
											__lowercase												=			list(Path(model.output_dir							).glob('''best_tfmr/*'''							)							)
											assert len(UpperCAmelCase_							) > 2
											self.assertEqual(len(UpperCAmelCase_							)     ,							2							)







						def        _UpperCamelCase    (self							)			->						int:
											__lowercase												=			{
											    '''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        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			{
											    '''teacher''': T5_TINY,
											    '''student''': T5_TINIER,
											    '''model_name_or_path''': T5_TINIER,
											    '''tokenizer_name''': T5_TINIER,
											}
											self._test_distiller_cli(UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=True							)			->						int:
											__lowercase												=			{
											    '''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_							)
											__lowercase												=			CHEAP_ARGS.copy()
											__lowercase												=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											__lowercase												=			self.get_auto_remove_tmp_dir()

											args_d.update(data_dir=UpperCAmelCase_     ,							output_dir=UpperCAmelCase_     ,							**UpperCAmelCase_							)
											__lowercase												=			distill_main(argparse.Namespace(**UpperCAmelCase_							)							)
											if not check_contents:
																return model
											__lowercase												=			os.listdir(UpperCAmelCase_							)
											__lowercase												=			{os.path.basename(UpperCAmelCase_							) for p in contents}
											__lowercase												=			[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_							)

											__lowercase												=			load_json(model.metrics_save_path							)
											__lowercase												=			metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     ,							0.0_1							)
											self.assertGreaterEqual(1.0     ,							last_step_stats['''val_avg_gen_time''']							)
											self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']     ,							UpperCAmelCase_							)
											__lowercase												=			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 			_snake_case     (						UpperCamelCase       ):







						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,							)
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Union[str, Any]:
											__lowercase												=			CHEAP_ARGS.copy()
											__lowercase												=			'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
											__lowercase												=			0.1 if task == '''translation''' else 0

											__lowercase												=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											__lowercase												=			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
											__lowercase												=			argparse.Namespace(**UpperCAmelCase_							)
											__lowercase												=			main(UpperCAmelCase_							)

											__lowercase												=			module.model.get_input_embeddings()
											assert not input_embeds.weight.requires_grad
											if model == T5_TINY:
																__lowercase												=			module.model.lm_head
																assert not lm_head.weight.requires_grad
																assert (lm_head.weight == input_embeds.weight).all().item()
											elif model == FSMT_TINY:
																__lowercase												=			module.model.model
																__lowercase												=			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:
																__lowercase												=			module.model.model
																__lowercase												=			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

											__lowercase												=			load_json(module.output_dir / '''text_batch.json'''							)
											assert isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							)
											assert len(UpperCAmelCase_							) >= 4







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			CHEAP_ARGS.copy()

											__lowercase												=			'''summarization'''
											__lowercase												=			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
											__lowercase												=			BART_TINY
											__lowercase												=			self.get_auto_remove_tmp_dir()
											__lowercase												=			args_d.copy()
											args_da.update(
											    model_name_or_path=UpperCAmelCase_     ,							output_dir=UpperCAmelCase_     ,							)
											__lowercase												=			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																__lowercase												=			0.5
											__lowercase												=			argparse.Namespace(**UpperCAmelCase_							)
											__lowercase												=			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
											__lowercase												=			T5_TINY
											__lowercase												=			self.get_auto_remove_tmp_dir()
											__lowercase												=			args_d.copy()
											args_da.update(
											    model_name_or_path=UpperCAmelCase_     ,							output_dir=UpperCAmelCase_     ,							)
											__lowercase												=			'''encoder_layerdrop'''
											__lowercase												=			0.5
											__lowercase												=			argparse.Namespace(**UpperCAmelCase_							)
											with pytest.raises(UpperCAmelCase_							) as excinfo:
																__lowercase												=			main(UpperCAmelCase_							)
											assert str(excinfo.value							) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''







						def        _UpperCamelCase    (self							)			->						Optional[Any]:
											__lowercase												=			CHEAP_ARGS.copy()

											__lowercase												=			'''summarization'''
											__lowercase												=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

											__lowercase												=			BART_TINY
											__lowercase												=			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
											__lowercase												=			argparse.ArgumentParser()
											__lowercase												=			pl.Trainer.add_argparse_args(UpperCAmelCase_							)
											__lowercase												=			SummarizationModule.add_model_specific_args(UpperCAmelCase_     ,							os.getcwd()							)
											__lowercase												=			{'''--help''': True}

											# --help test
											with pytest.raises(UpperCAmelCase_							) as excinfo:
																with CaptureStdout() as cs:
																					__lowercase												=			parser.parse_args(UpperCAmelCase_							)
																assert False, "--help is expected to sys.exit"
											assert excinfo.type == SystemExit
											__lowercase												=			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
											__lowercase												=			'''non_existing_scheduler'''
											__lowercase												=			{f'''--lr_scheduler={unsupported_param}'''}
											with pytest.raises(UpperCAmelCase_							) as excinfo:
																with CaptureStderr() as cs:
																					__lowercase												=			parser.parse_args(UpperCAmelCase_							)
																assert False, "invalid argument is expected to sys.exit"
											assert excinfo.type == SystemExit
											__lowercase												=			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
											__lowercase												=			'''cosine'''
											__lowercase												=			args_d.copy()
											__lowercase												=			supported_param
											__lowercase												=			argparse.Namespace(**UpperCAmelCase_							)
											__lowercase												=			main(UpperCAmelCase_							)
											assert (
											    getattr(model.hparams     ,							'''lr_scheduler'''							) == supported_param
											), f'''lr_scheduler={supported_param} shouldn\'t fail'''





"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


a__ :	List[Any]						= logging.getLogger(__name__)


@dataclass
class 			_snake_case     :
						__snake_case 			=     field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						__snake_case 			=     field(default=UpperCamelCase						,			metadata={'help': 'Set this flag to use fast tokenization.'}       )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,			)





def 				__UpperCamelCase   (    )				->			Union[str, Any]:




					"""simple docstring"""



					__lowercase												=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

					if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir			)
					    and os.listdir(training_args.output_dir			)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ''' --overwrite_output_dir to overcome.'''			)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,	datefmt='''%m/%d/%Y %H:%M:%S''' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1			) ,	training_args.fpaa ,	)
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank			):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s''' ,	a__			)

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowercase												=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
					__lowercase												=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	use_fast=a__ ,	)
					__lowercase												=			AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,	config=a__ ,	cache_dir=model_args.cache_dir ,	)

					# Get datasets
					__lowercase												=			hasattr(model.config ,	'''lang2id'''			)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir			)
					    if training_args.do_train
					    else None
					)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	mode='''dev''' ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir ,	)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					__lowercase												=			DataCollatorWithPadding(a__ ,	pad_to_multiple_of=8			) if training_args.fpaa else None

					# Initialize our Trainer
					__lowercase												=			Trainer(
					    model=a__ ,	args=a__ ,	train_dataset=a__ ,	eval_dataset=a__ ,	data_collator=a__ ,	)

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir			)





def 				__UpperCamelCase   (    a__: Any			)				->			Any:




					"""simple docstring"""



					main()


if __name__ == "__main__":
							main()


