
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)

UpperCAmelCase__				:	Tuple					=						logging.getLogger()
UpperCAmelCase__				:	Optional[Any]					=						torch.cuda.is_available()
UpperCAmelCase__				:	Union[str, Any]					=						{
    '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 					A       ( UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       Optional[int] )   ->    Tuple:







			'''simple docstring'''



			lowerCAmelCase__									=							'\n'.join(_UpperCAmelCase )
			Path(_UpperCAmelCase ).open("w" ).writelines(_UpperCAmelCase )


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


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







def 					A       ( UpperCamelCase_   :       Optional[Any] )   ->    Dict:







			'''simple docstring'''



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






class 				A       (		UpperCAmelCase__      ):





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    str							):


				"""simple docstring"""

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





	@slow
	@require_torch_gpu
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
				self._test_distiller_cli(snake_case_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    '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',
				}
				lowerCAmelCase__									=							self._test_distiller_cli(snake_case_ ,							check_contents=snake_case_							)

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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

				lowerCAmelCase__									=							model(snake_case_ ,							attention_mask=snake_case_ ,							decoder_input_ids=snake_case_ ,							use_cache=snake_case_							).logits

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    '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',
				}
				lowerCAmelCase__									=							self._test_distiller_cli(snake_case_ ,							check_contents=snake_case_							)
				assert model.model.config.model_type == "mbart"

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    '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(snake_case_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'teacher': T5_TINY,
				    'student': T5_TINIER,
				    'model_name_or_path': T5_TINIER,
				    'tokenizer_name': T5_TINIER,
				}
				self._test_distiller_cli(snake_case_							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    Dict ,							__magic_name__     :    int=True							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    '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(snake_case_							)
				lowerCAmelCase__									=							CHEAP_ARGS.copy()
				lowerCAmelCase__									=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()

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

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

				lowerCAmelCase__									=							load_json(model.metrics_save_path							)
				lowerCAmelCase__									=							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}"""] ,							snake_case_							)
				lowerCAmelCase__									=							int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1							)
				self.assertEqual(len(metrics["val"]							) ,							snake_case_							)
				self.assertEqual(len(metrics["test"]							) ,							1							)
				return model






class 				A       (		UpperCAmelCase__      ):





	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,							)
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()
				lowerCAmelCase__									=							'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
				lowerCAmelCase__									=							0.1 if task == 'translation' else 0

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

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()

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

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

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

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()

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

				lowerCAmelCase__									=							BART_TINY
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()

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

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

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







'''simple docstring'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





