






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-0_8,
    '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   snake_case		(				UpperCAmelCase						:							Path,    UpperCAmelCase						:							list ):
				A									=   '\n'.join(UpperCAmelCase )
				Path(UpperCAmelCase ).open('w' ).writelines(UpperCAmelCase )


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   snake_case		(				UpperCAmelCase						:							List[Any] ):
				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   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		@classmethod
		def    A(	cls		:							Optional[int]		)     -> Optional[Any]:


						'''simple docstring'''
						logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
						return cls
		@slow
		@require_torch_gpu
		def    A(	self		:							Dict		)     -> List[Any]:


						'''simple docstring'''
						A									=   list_models()
						A									=   'sshleifer'
						A									=   [x.modelId for x in model_list if x.modelId.startswith(_SCREAMING_SNAKE_CASE		)]
						A									=   ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
						A									=   []
						for m in model_ids:
										if m in allowed_to_be_broken:
														continue
										try:
														AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
										except Exception:
														failures.append(_SCREAMING_SNAKE_CASE		)
						assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'
		def    A(	self		:							Optional[int]		)     -> List[str]:


						'''simple docstring'''
						A									=   {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
						self._test_distiller_cli(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Optional[Any]		)     -> Dict:


						'''simple docstring'''
						A									=   {
						    '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',
						}
						A									=   self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,check_contents=_SCREAMING_SNAKE_CASE		)

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

						A									=   self.get_auto_remove_tmp_dir()
						convert_pl_to_hf(ckpts[0]    ,transformer_ckpts[0].parent    ,_SCREAMING_SNAKE_CASE		)
						assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,'pytorch_model.bin'		)		)
		def    A(	self		:							List[Any]		)     -> List[str]:


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

						A									=   model(_SCREAMING_SNAKE_CASE    ,attention_mask=_SCREAMING_SNAKE_CASE    ,decoder_input_ids=_SCREAMING_SNAKE_CASE    ,use_cache=_SCREAMING_SNAKE_CASE		).logits

						A									=   nn.functional.log_softmax(_SCREAMING_SNAKE_CASE    ,dim=-1		)
						A			,		A									=   label_smoothed_nll_loss(
						    _SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,0.1    ,ignore_index=model.config.pad_token_id		)
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										# TODO: understand why this breaks
										self.assertEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							List[Any]		)     -> str:


						'''simple docstring'''
						A									=   {
						    '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',
						}
						A									=   self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,check_contents=_SCREAMING_SNAKE_CASE		)
						assert model.model.config.model_type == "mbart"

						A									=   list(Path(model.output_dir		).glob('*.ckpt'		)		)
						self.assertEqual(1    ,len(_SCREAMING_SNAKE_CASE		)		)
						A									=   list(Path(model.output_dir		).glob('**/*.bin'		)		)
						A									=   list(Path(model.output_dir		).glob('best_tfmr/*'		)		)
						assert len(_SCREAMING_SNAKE_CASE		) > 2
						self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,2		)
		def    A(	self		:							Tuple		)     -> str:


						'''simple docstring'''
						A									=   {
						    '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(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Dict		)     -> Optional[int]:


						'''simple docstring'''
						A									=   {
						    'teacher': T5_TINY,
						    'student': T5_TINIER,
						    'model_name_or_path': T5_TINIER,
						    'tokenizer_name': T5_TINIER,
						}
						self._test_distiller_cli(_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]=True		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   {
						    '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(_SCREAMING_SNAKE_CASE		)
						A									=   CHEAP_ARGS.copy()
						A									=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
						A									=   self.get_auto_remove_tmp_dir()

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

						self.assertIn('test_generations.txt'    ,_SCREAMING_SNAKE_CASE		)
						self.assertIn('test_results.txt'    ,_SCREAMING_SNAKE_CASE		)

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


class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,)
		def    A(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							Any		)     -> Any:


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

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

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

						A									=   load_json(module.output_dir / 'text_batch.json'		)
						assert isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
						assert len(_SCREAMING_SNAKE_CASE		) >= 4
		def    A(	self		:							List[str]		)     -> List[Any]:


						'''simple docstring'''
						A									=   CHEAP_ARGS.copy()

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

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

						# test models whose config includes the extra_model_args
						A									=   BART_TINY
						A									=   self.get_auto_remove_tmp_dir()
						A									=   args_d.copy()
						args_da.update(
						    model_name_or_path=_SCREAMING_SNAKE_CASE    ,output_dir=_SCREAMING_SNAKE_CASE    ,)
						A									=   ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
										A									=   0.5
						A									=   argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
						A									=   main(_SCREAMING_SNAKE_CASE		)
						for p in extra_model_params:
										assert getattr(model.config    ,_SCREAMING_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
						A									=   T5_TINY
						A									=   self.get_auto_remove_tmp_dir()
						A									=   args_d.copy()
						args_da.update(
						    model_name_or_path=_SCREAMING_SNAKE_CASE    ,output_dir=_SCREAMING_SNAKE_CASE    ,)
						A									=   'encoder_layerdrop'
						A									=   0.5
						A									=   argparse.Namespace(**_SCREAMING_SNAKE_CASE		)
						with pytest.raises(_SCREAMING_SNAKE_CASE		) as excinfo:
										A									=   main(_SCREAMING_SNAKE_CASE		)
						assert str(excinfo.value		) == f'model config doesn\'t have a `{unsupported_param}` attribute'







		def    A(	self		:							Any		)     -> Dict:


						'''simple docstring'''
						A									=   CHEAP_ARGS.copy()

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

						A									=   BART_TINY
						A									=   self.get_auto_remove_tmp_dir()

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

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

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













import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





