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__					:       Union[str, Any]		=       logging.getLogger()
lowerCAmelCase__					:       Any		=       torch.cuda.is_available()
lowerCAmelCase__					:       Dict		=       {
    '''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__      ,			A__   )       -> Union[str, Any]:
				snake_case__ :     Tuple 							=				'\n'.join(A__   )
				Path(A__   ).open('w'   ).writelines(A__   )


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


lowerCAmelCase__					:       Union[str, Any]		=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 			UpperCamelCase__						(    A__   )       -> Any:
				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  (      _lowerCamelCase     ):




					@classmethod
					def       __a      (       cls		)					->  Tuple:



									'''simple docstring'''







									logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
									return cls




					@slow
					@require_torch_gpu
					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     int 							=				list_models()
									snake_case__ :     Any 							=				'sshleifer'
									snake_case__ :     int 							=				[x.modelId for x in model_list if x.modelId.startswith(__UpperCamelCase		)]
									snake_case__ :     Optional[int] 							=				['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
									snake_case__ :     str 							=				[]
									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       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
									self._test_distiller_cli(__UpperCamelCase		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				{
									    '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',
									}
									snake_case__ :     Any 							=				self._test_distiller_cli(__UpperCamelCase   ,	check_contents=__UpperCamelCase		)

									snake_case__ :     Optional[int] 							=				list(Path(model.output_dir		).glob('*.ckpt'		)		)
									self.assertEqual(1   ,	len(__UpperCamelCase		)		)
									snake_case__ :     List[str] 							=				list(Path(model.output_dir		).glob('**/*.bin'		)		)
									self.assertEqual(len(__UpperCamelCase		)   ,	2		)
									snake_case__ :     Union[str, Any] 							=				lmap(str.strip   ,	Path(model.hparams.data_dir		).joinpath('test.source'		).open().readlines()		)
									snake_case__ :     Any 							=				tempfile.mktemp()  # XXX: not being cleaned up
									generate_summaries_or_translations(__UpperCamelCase   ,	__UpperCamelCase   ,	str(model.output_dir / 'best_tfmr'		)		)
									self.assertTrue(Path(__UpperCamelCase		).exists()		)

									snake_case__ :     List[str] 							=				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       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Any 							=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase		)
									snake_case__							,  snake_case__ :     Union[str, Any] 							=				model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
									snake_case__ :     Tuple 							=				torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]   ,	dtype=torch.long   ,	device=model.device		)
									snake_case__ :     Tuple 							=				target_ids[:, :-1].contiguous()  # Why this line?
									snake_case__ :     Union[str, Any] 							=				target_ids[:, 1:].clone()  # why clone?
									snake_case__ :     int 							=				model(
									    __UpperCamelCase   ,	attention_mask=__UpperCamelCase   ,	decoder_input_ids=__UpperCamelCase   ,	labels=__UpperCamelCase   ,	use_cache=__UpperCamelCase		).loss

									snake_case__ :     Optional[Any] 							=				model(__UpperCamelCase   ,	attention_mask=__UpperCamelCase   ,	decoder_input_ids=__UpperCamelCase   ,	use_cache=__UpperCamelCase		).logits

									snake_case__ :     Any 							=				nn.functional.log_softmax(__UpperCamelCase   ,	dim=-1		)
									snake_case__							,  snake_case__ :     Union[str, Any] 							=				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       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				{
									    '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',
									}
									snake_case__ :     int 							=				self._test_distiller_cli(__UpperCamelCase   ,	check_contents=__UpperCamelCase		)
									assert model.model.config.model_type == "mbart"

									snake_case__ :     Optional[int] 							=				list(Path(model.output_dir		).glob('*.ckpt'		)		)
									self.assertEqual(1   ,	len(__UpperCamelCase		)		)
									snake_case__ :     Dict 							=				list(Path(model.output_dir		).glob('**/*.bin'		)		)
									snake_case__ :     int 							=				list(Path(model.output_dir		).glob('best_tfmr/*'		)		)
									assert len(__UpperCamelCase		) > 2
									self.assertEqual(len(__UpperCamelCase		)   ,	2		)




					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				{
									    '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       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				{
									    'teacher': T5_TINY,
									    'student': T5_TINIER,
									    'model_name_or_path': T5_TINIER,
									    'tokenizer_name': T5_TINIER,
									}
									self._test_distiller_cli(__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase=True		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     str 							=				{
									    '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		)
									snake_case__ :     dict 							=				CHEAP_ARGS.copy()
									snake_case__ :     str 							=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									snake_case__ :     Dict 							=				self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=__UpperCamelCase   ,	output_dir=__UpperCamelCase   ,	**__UpperCamelCase		)
									snake_case__ :     List[str] 							=				distill_main(argparse.Namespace(**__UpperCamelCase		)		)
									if not check_contents:
													return model
									snake_case__ :     Tuple 							=				os.listdir(__UpperCamelCase		)
									snake_case__ :     Dict 							=				{os.path.basename(__UpperCamelCase		) for p in contents}
									snake_case__ :     int 							=				[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		)

									snake_case__ :     Union[str, Any] 							=				load_json(model.metrics_save_path		)
									snake_case__ :     Optional[Any] 							=				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		)
									snake_case__ :     Dict 							=				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  (      _lowerCamelCase     ):




					@parameterized.expand(
					    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,	)
					def       __a      (       self   ,	__UpperCamelCase		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     dict 							=				CHEAP_ARGS.copy()
									snake_case__ :     List[Any] 							=				'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
									snake_case__ :     str 							=				0.1 if task == 'translation' else 0

									snake_case__ :     Tuple 							=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									snake_case__ :     List[str] 							=				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
									snake_case__ :     List[str] 							=				argparse.Namespace(**__UpperCamelCase		)
									snake_case__ :     Tuple 							=				main(__UpperCamelCase		)

									snake_case__ :     str 							=				module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
													snake_case__ :     Any 							=				module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
									elif model == FSMT_TINY:
													snake_case__ :     Union[str, Any] 							=				module.model.model
													snake_case__ :     Any 							=				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:
													snake_case__ :     Any 							=				module.model.model
													snake_case__ :     List[Any] 							=				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

									snake_case__ :     List[Any] 							=				load_json(module.output_dir / 'text_batch.json'		)
									assert isinstance(__UpperCamelCase   ,	__UpperCamelCase		)
									assert len(__UpperCamelCase		) >= 4




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__ :     dict 							=				CHEAP_ARGS.copy()

									snake_case__ :     Tuple 							=				'summarization'
									snake_case__ :     Optional[Any] 							=				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
									snake_case__ :     Optional[int] 							=				BART_TINY
									snake_case__ :     List[Any] 							=				self.get_auto_remove_tmp_dir()
									snake_case__ :     int 							=				args_d.copy()
									args_da.update(
									    model_name_or_path=__UpperCamelCase   ,	output_dir=__UpperCamelCase   ,	)
									snake_case__ :     List[str] 							=				('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
													snake_case__ :     Dict 							=				0.5
									snake_case__ :     Tuple 							=				argparse.Namespace(**__UpperCamelCase		)
									snake_case__ :     Optional[int] 							=				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
									snake_case__ :     str 							=				T5_TINY
									snake_case__ :     List[Any] 							=				self.get_auto_remove_tmp_dir()
									snake_case__ :     Optional[int] 							=				args_d.copy()
									args_da.update(
									    model_name_or_path=__UpperCamelCase   ,	output_dir=__UpperCamelCase   ,	)
									snake_case__ :     str 							=				'encoder_layerdrop'
									snake_case__ :     List[str] 							=				0.5
									snake_case__ :     List[str] 							=				argparse.Namespace(**__UpperCamelCase		)
									with pytest.raises(__UpperCamelCase		) as excinfo:
													snake_case__ :     Union[str, Any] 							=				main(__UpperCamelCase		)
									assert str(excinfo.value		) == F"""model config doesn't have a `{unsupported_param}` attribute"""




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     dict 							=				CHEAP_ARGS.copy()

									snake_case__ :     str 							=				'summarization'
									snake_case__ :     int 							=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

									snake_case__ :     Optional[Any] 							=				BART_TINY
									snake_case__ :     Union[str, Any] 							=				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
									snake_case__ :     Any 							=				argparse.ArgumentParser()
									snake_case__ :     Dict 							=				pl.Trainer.add_argparse_args(__UpperCamelCase		)
									snake_case__ :     Any 							=				SummarizationModule.add_model_specific_args(__UpperCamelCase   ,	os.getcwd()		)
									snake_case__ :     Optional[Any] 							=				{'--help': True}

									# --help test
									with pytest.raises(__UpperCamelCase		) as excinfo:
													with CaptureStdout() as cs:
																	snake_case__ :     Optional[int] 							=				parser.parse_args(__UpperCamelCase		)
													assert False, "--help is expected to sys.exit"
									assert excinfo.type == SystemExit
									snake_case__ :     Tuple 							=				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
									snake_case__ :     Union[str, Any] 							=				'non_existing_scheduler'
									snake_case__ :     Any 							=				{F"""--lr_scheduler={unsupported_param}"""}
									with pytest.raises(__UpperCamelCase		) as excinfo:
													with CaptureStderr() as cs:
																	snake_case__ :     Optional[Any] 							=				parser.parse_args(__UpperCamelCase		)
													assert False, "invalid argument is expected to sys.exit"
									assert excinfo.type == SystemExit
									snake_case__ :     List[Any] 							=				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
									snake_case__ :     Union[str, Any] 							=				'cosine'
									snake_case__ :     Optional[Any] 							=				args_d.copy()
									snake_case__ :     Tuple 							=				supported_param
									snake_case__ :     List[Any] 							=				argparse.Namespace(**__UpperCamelCase		)
									snake_case__ :     str 							=				main(__UpperCamelCase		)
									assert (
									    getattr(model.hparams   ,	'lr_scheduler'		) == supported_param
									), F"""lr_scheduler={supported_param} shouldn't fail"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


