

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__	:  List[str] 		=logging.getLogger()
lowerCAmelCase__	:  List[Any] 		=torch.cuda.is_available()
lowerCAmelCase__	:  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 			__lowercase     (				a__							,     a__     )       ->				Optional[Any]:
							__SCREAMING_SNAKE_CASE								=      '\n'.join(a__     )
							Path(a__     ).open('w'     ).writelines(a__     )


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


lowerCAmelCase__	:  Optional[Any] 		=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






def 			__lowercase     (				a__     )       ->				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      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						@classmethod
						def  _A			(					cls	):







													'''simple docstring'''

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






						@slow
						@require_torch_gpu
						def  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    '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',
													}
													__SCREAMING_SNAKE_CASE								=      self._test_distiller_cli(_A   ,							check_contents=_A	)

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

													__SCREAMING_SNAKE_CASE								=      self.get_auto_remove_tmp_dir()
													convert_pl_to_hf(ckpts[0]   ,							transformer_ckpts[0].parent   ,							_A	)
													assert os.path.exists(os.path.join(_A   ,							'pytorch_model.bin'	)	)






						def  _A			(					self	):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE								=      model(_A   ,							attention_mask=_A   ,							decoder_input_ids=_A   ,							use_cache=_A	).logits

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






						def  _A			(					self	):







													'''simple docstring'''

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

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    '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(_A	)






						def  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self   ,							_A   ,							_A=True	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    '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(_A	)
													__SCREAMING_SNAKE_CASE								=      CHEAP_ARGS.copy()
													__SCREAMING_SNAKE_CASE								=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
													__SCREAMING_SNAKE_CASE								=      self.get_auto_remove_tmp_dir()

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

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

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







class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,							)
						def  _A			(					self   ,							_A	):







													'''simple docstring'''

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

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

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

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      CHEAP_ARGS.copy()

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

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

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

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      CHEAP_ARGS.copy()

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

													__SCREAMING_SNAKE_CASE								=      BART_TINY
													__SCREAMING_SNAKE_CASE								=      self.get_auto_remove_tmp_dir()

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

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

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











def 			__lowercase     (				a__							,     a__     )       ->				List[str]:
							if n == 0:
														return 1

							elif n % 2 == 1:
														return binary_exponentiation(a__							,     n - 1     ) * a

							else:
														__SCREAMING_SNAKE_CASE								=      binary_exponentiation(a__							,     n / 2     )
														return b * b


if __name__ == "__main__":
			try:
						lowerCAmelCase__	:  str 		=int(input('''Enter Base : ''').strip())
						lowerCAmelCase__	:  Optional[int] 		=int(input('''Enter Power : ''').strip())
			except ValueError:
						print('''Invalid literal for integer''')

			lowerCAmelCase__	:  Any 		=binary_exponentiation(BASE, POWER)
			print(F'''{BASE}^({POWER}) : {RESULT}''')






