





'''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 :			Any							   =      logging.getLogger()
A :			int							   =      torch.cuda.is_available()
A :			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    lowercase_   (		lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: Dict	       =		"""\n""".join(_a    )
						Path(_a    ).open('w'    ).writelines(_a    )


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


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





def    lowercase_   (		lowercase__    ) ->List[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       lowerCamelCase       (    __snake_case	):


					@classmethod
					def  SCREAMING_SNAKE_CASE_		(       cls						:	Any							):





											'''simple docstring'''





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


					@slow
					@require_torch_gpu
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
											self._test_distiller_cli(lowerCamelCase_							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: int	       =		{
											    """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""",
											}
											_snake_case: Optional[int]	       =		self._test_distiller_cli(lowerCamelCase_							,  check_contents=lowerCamelCase_							)

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

											_snake_case: Optional[int]	       =		self.get_auto_remove_tmp_dir()
											convert_pl_to_hf(ckpts[0]							,  transformer_ckpts[0].parent							,  lowerCamelCase_							)
											assert os.path.exists(os.path.join(lowerCamelCase_							,  'pytorch_model.bin'							)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





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

											_snake_case: Tuple	       =		model(lowerCamelCase_							,  attention_mask=lowerCamelCase_							,  decoder_input_ids=lowerCamelCase_							,  use_cache=lowerCamelCase_							).logits

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: List[str]	       =		{
											    """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""",
											}
											_snake_case: int	       =		self._test_distiller_cli(lowerCamelCase_							,  check_contents=lowerCamelCase_							)
											assert model.model.config.model_type == "mbart"

											_snake_case: List[str]	       =		list(Path(model.output_dir							).glob('*.ckpt'							)							)
											self.assertEqual(1							,  len(lowerCamelCase_							)							)
											_snake_case: Optional[int]	       =		list(Path(model.output_dir							).glob('**/*.bin'							)							)
											_snake_case: Union[str, Any]	       =		list(Path(model.output_dir							).glob('best_tfmr/*'							)							)
											assert len(lowerCamelCase_							) > 2
											self.assertEqual(len(lowerCamelCase_							)							,  2							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: Any	       =		{
											    """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(lowerCamelCase_							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: str	       =		{
											    """teacher""": T5_TINY,
											    """student""": T5_TINIER,
											    """model_name_or_path""": T5_TINIER,
											    """tokenizer_name""": T5_TINIER,
											}
											self._test_distiller_cli(lowerCamelCase_							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	int							,  __snake_case						:	Union[str, Any]=True							):





											'''simple docstring'''





											_snake_case: Tuple	       =		{
											    """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(lowerCamelCase_							)
											_snake_case: dict	       =		CHEAP_ARGS.copy()
											_snake_case: int	       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											_snake_case: Optional[int]	       =		self.get_auto_remove_tmp_dir()

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

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

											_snake_case: List[str]	       =		load_json(model.metrics_save_path							)
											_snake_case: List[Any]	       =		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}''']							,  lowerCamelCase_							)
											_snake_case: Dict	       =		int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1							)
											self.assertEqual(len(metrics['val']							)							,  lowerCamelCase_							)
											self.assertEqual(len(metrics['test']							)							,  1							)
											return model







class       lowerCamelCase       (    __snake_case	):


					@parameterized.expand(
					    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]							,  )
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Dict							):





											'''simple docstring'''





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

											_snake_case: int	       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											_snake_case: Any	       =		self.get_auto_remove_tmp_dir()
											args_d.update(
											    data_dir=lowerCamelCase_							,  model_name_or_path=lowerCamelCase_							,  tokenizer_name=lowerCamelCase_							,  train_batch_size=2							,  eval_batch_size=2							,  output_dir=lowerCamelCase_							,  do_predict=lowerCamelCase_							,  task=lowerCamelCase_							,  src_lang='en_XX'							,  tgt_lang='ro_RO'							,  freeze_encoder=lowerCamelCase_							,  freeze_embeds=lowerCamelCase_							,  )
											assert "n_train" in args_d
											_snake_case: Optional[int]	       =		argparse.Namespace(**lowerCamelCase_							)
											_snake_case: List[str]	       =		main(lowerCamelCase_							)

											_snake_case: List[str]	       =		module.model.get_input_embeddings()
											assert not input_embeds.weight.requires_grad
											if model == T5_TINY:
																	_snake_case: Dict	       =		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: List[str]	       =		module.model.model
																	_snake_case: Union[str, 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: str	       =		module.model.model
																	_snake_case: List[str]	       =		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: Optional[Any]	       =		load_json(module.output_dir / 'text_batch.json'							)
											assert isinstance(lowerCamelCase_							,  lowerCamelCase_							)
											assert len(lowerCamelCase_							) >= 4


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: dict	       =		CHEAP_ARGS.copy()

											_snake_case: List[Any]	       =		"""summarization"""
											_snake_case: Union[str, Any]	       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

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

											# test models whose config includes the extra_model_args
											_snake_case: Union[str, Any]	       =		BART_TINY
											_snake_case: Dict	       =		self.get_auto_remove_tmp_dir()
											_snake_case: Union[str, Any]	       =		args_d.copy()
											args_da.update(
											    model_name_or_path=lowerCamelCase_							,  output_dir=lowerCamelCase_							,  )
											_snake_case: Optional[Any]	       =		("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
											for p in extra_model_params:
																	_snake_case: List[str]	       =		0.5
											_snake_case: Optional[Any]	       =		argparse.Namespace(**lowerCamelCase_							)
											_snake_case: int	       =		main(lowerCamelCase_							)
											for p in extra_model_params:
																	assert getattr(model.config							,  lowerCamelCase_							) == 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: List[Any]	       =		T5_TINY
											_snake_case: str	       =		self.get_auto_remove_tmp_dir()
											_snake_case: Tuple	       =		args_d.copy()
											args_da.update(
											    model_name_or_path=lowerCamelCase_							,  output_dir=lowerCamelCase_							,  )
											_snake_case: List[str]	       =		"""encoder_layerdrop"""
											_snake_case: Optional[Any]	       =		0.5
											_snake_case: Optional[Any]	       =		argparse.Namespace(**lowerCamelCase_							)
											with pytest.raises(lowerCamelCase_							) as excinfo:
																	_snake_case: Optional[Any]	       =		main(lowerCamelCase_							)
											assert str(excinfo.value							) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: dict	       =		CHEAP_ARGS.copy()

											_snake_case: Tuple	       =		"""summarization"""
											_snake_case: Any	       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

											_snake_case: Tuple	       =		BART_TINY
											_snake_case: int	       =		self.get_auto_remove_tmp_dir()

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

											# emulate finetune.py
											_snake_case: List[str]	       =		argparse.ArgumentParser()
											_snake_case: Dict	       =		pl.Trainer.add_argparse_args(lowerCamelCase_							)
											_snake_case: Optional[Any]	       =		SummarizationModule.add_model_specific_args(lowerCamelCase_							,  os.getcwd()							)
											_snake_case: int	       =		{"""--help""": True}

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










import math







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5   ,		int(math.sqrt(_a    ) + 1    )   ,		6    ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def       lowerCamelCase_  (    _a       :						int = 6008_5147_5143    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       str  		=					int(_a    )
    except (TypeError, ValueError):
        raise TypeError("""Parameter n must be int or castable to int."""    )
    if n <= 0:
        raise ValueError("""Parameter n must be greater than or equal to one."""    )
    UpperCAmelCase_     :       Optional[int]  		=					0
    if is_prime(_a    ):
        return n
    while n % 2 == 0:
        n //= 2
    if is_prime(_a    ):
        return n
    for i in range(3   ,		int(math.sqrt(_a    )    ) + 1   ,		2    ):
        if n % i == 0:
            if is_prime(n // i    ):
                UpperCAmelCase_     :       Optional[int]  		=					n // i
                break
            elif is_prime(_a    ):
                UpperCAmelCase_     :       str  		=					i
    return max_number


if __name__ == "__main__":
      print(F"{solution() = }")
