






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)

SCREAMING_SNAKE_CASE								=  logging.getLogger()
SCREAMING_SNAKE_CASE								=  torch.cuda.is_available()
SCREAMING_SNAKE_CASE								=  {
    '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        _lowerCamelCase		(  __A      :							Path						,						__A      :							list	) ->   Any:
    _UpperCAmelCase			: Any     					=			'''\n'''.join(__A	)
    Path(__A	).open('''w'''	).writelines(__A	)


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


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

def        _lowerCamelCase		(  __A      :							List[Any]	) ->   Optional[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 							A_      (  __lowercase     ):






 '''simple docstring'''




 @classmethod
 def 	snake_case__			(		cls)	->						List[Any]:

     """simple docstring"""



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


 @slow
 @require_torch_gpu
 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



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

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
     self._test_distiller_cli(_A)


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			{
         '''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''',
     }
     _UpperCAmelCase			: List[Any]     					=			self._test_distiller_cli(_A							,					check_contents=_A)

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

     _UpperCAmelCase			: List[str]     					=			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 	snake_case__			(		self)	->						Any:

     """simple docstring"""



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

     _UpperCAmelCase			: str     					=			model(_A							,					attention_mask=_A							,					decoder_input_ids=_A							,					use_cache=_A).logits

     _UpperCAmelCase			: Any     					=			nn.functional.log_softmax(_A							,					dim=-1)
     _UpperCAmelCase					,	_UpperCAmelCase			: Any     					=			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 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			{
         '''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''',
     }
     _UpperCAmelCase			: List[str]     					=			self._test_distiller_cli(_A							,					check_contents=_A)
     assert model.model.config.model_type == "mbart"

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


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

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			{
         '''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 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A=True)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: List[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(_A)
     _UpperCAmelCase			: dict     					=			CHEAP_ARGS.copy()
     _UpperCAmelCase			: Tuple     					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
     _UpperCAmelCase			: Union[str, Any]     					=			self.get_auto_remove_tmp_dir()

     args_d.update(data_dir=_A							,					output_dir=_A							,					**_A)
     _UpperCAmelCase			: Union[str, Any]     					=			distill_main(argparse.Namespace(**_A))
     if not check_contents:
         return model
     _UpperCAmelCase			: str     					=			os.listdir(_A)
     _UpperCAmelCase			: str     					=			{os.path.basename(_A) for p in contents}
     _UpperCAmelCase			: Tuple     					=			[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)

     _UpperCAmelCase			: List[str]     					=			load_json(model.metrics_save_path)
     _UpperCAmelCase			: int     					=			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}''']							,					_A)
     _UpperCAmelCase			: Any     					=			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 							A_      (  __lowercase     ):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase			: dict     					=			CHEAP_ARGS.copy()
     _UpperCAmelCase			: Tuple     					=			'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
     _UpperCAmelCase			: Optional[int]     					=			0.1 if task == '''translation''' else 0

     _UpperCAmelCase			: Optional[Any]     					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
     _UpperCAmelCase			: Tuple     					=			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
     _UpperCAmelCase			: Union[str, Any]     					=			argparse.Namespace(**_A)
     _UpperCAmelCase			: Dict     					=			main(_A)

     _UpperCAmelCase			: Dict     					=			module.model.get_input_embeddings()
     assert not input_embeds.weight.requires_grad
     if model == T5_TINY:
         _UpperCAmelCase			: List[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:
         _UpperCAmelCase			: Optional[int]     					=			module.model.model
         _UpperCAmelCase			: 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:
         _UpperCAmelCase			: Optional[Any]     					=			module.model.model
         _UpperCAmelCase			: 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

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


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: dict     					=			CHEAP_ARGS.copy()

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


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: dict     					=			CHEAP_ARGS.copy()

     _UpperCAmelCase			: Tuple     					=			'''summarization'''
     _UpperCAmelCase			: Tuple     					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

     _UpperCAmelCase			: List[Any]     					=			BART_TINY
     _UpperCAmelCase			: Any     					=			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
     _UpperCAmelCase			: Optional[Any]     					=			argparse.ArgumentParser()
     _UpperCAmelCase			: int     					=			pl.Trainer.add_argparse_args(_A)
     _UpperCAmelCase			: Tuple     					=			SummarizationModule.add_model_specific_args(_A							,					os.getcwd())
     _UpperCAmelCase			: str     					=			{'''--help''': True}

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












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'Binary tree: {binary_tree}')
  SCREAMING_SNAKE_CASE								=  binary_tree_mirror(binary_tree, 5)
  print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')




