

"""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)

lowerCamelCase__				:  int													=  logging.getLogger()
lowerCamelCase__				:  Optional[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       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Union[str, Any]									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )


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


lowerCamelCase__				:  Dict													=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Any] )				->			Any:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
				@classmethod
				def 							__snake_case (		cls     )    ->    Optional[int]:






					'''simple docstring'''







					logging.disable(logging.CRITICAL     )  # remove noisy download output from tracebacks
					return cls
				@slow
				@require_torch_gpu
				def 							__snake_case (		self     )    ->    Tuple:






					'''simple docstring'''







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






					'''simple docstring'''







					_UpperCAmelCase    :							int									=					{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
					self._test_distiller_cli(_A     )
				def 							__snake_case (		self     )    ->    str:






					'''simple docstring'''







					_UpperCAmelCase    :							List[str]									=					{
					    """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    :							Dict									=					self._test_distiller_cli(_A      ,		check_contents=_A     )

					_UpperCAmelCase    :							Dict									=					list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
					self.assertEqual(1      ,		len(_A     )     )
					_UpperCAmelCase    :							str									=					list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
					self.assertEqual(len(_A     )      ,		2     )
					_UpperCAmelCase    :							int									=					lmap(str.strip      ,		Path(model.hparams.data_dir     ).joinpath("""test.source"""     ).open().readlines()     )
					_UpperCAmelCase    :							int									=					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    :							Dict									=					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     )    ->    Optional[int]:






					'''simple docstring'''







					_UpperCAmelCase    :							Optional[int]									=					AutoModelForSeqaSeqLM.from_pretrained(_A     )
					_UpperCAmelCase    :							Optional[Any]									=					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    :							List[str]									=					target_ids[:, :-1].contiguous()  # Why this line?
					_UpperCAmelCase    :							Optional[int]									=					target_ids[:, 1:].clone()  # why clone?
					_UpperCAmelCase    :							str									=					model(
					    _A      ,		attention_mask=_A      ,		decoder_input_ids=_A      ,		labels=_A      ,		use_cache=_A     ).loss

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

					_UpperCAmelCase    :							Tuple									=					nn.functional.log_softmax(_A      ,		dim=-1     )
					_UpperCAmelCase    :							int									=					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     )    ->    Union[str, Any]:






					'''simple docstring'''







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

					_UpperCAmelCase    :							Dict									=					list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
					self.assertEqual(1      ,		len(_A     )     )
					_UpperCAmelCase    :							Union[str, Any]									=					list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
					_UpperCAmelCase    :							Tuple									=					list(Path(model.output_dir     ).glob("""best_tfmr/*"""     )     )
					assert len(_A     ) > 2
					self.assertEqual(len(_A     )      ,		2     )
				def 							__snake_case (		self     )    ->    int:






					'''simple docstring'''







					_UpperCAmelCase    :							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     )    ->    List[Any]:






					'''simple docstring'''







					_UpperCAmelCase    :							List[str]									=					{
					    """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     )    ->    List[str]:






					'''simple docstring'''







					_UpperCAmelCase    :							Optional[Any]									=					{
					    """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    :							Optional[int]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
					_UpperCAmelCase    :							Dict									=					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    :							Union[str, Any]									=					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    :							Tuple									=					load_json(model.metrics_save_path     )
					_UpperCAmelCase    :							Optional[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    :							Optional[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     _UpperCAmelCase			(		__a):
				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,		)
				def 							__snake_case (		self      ,		_A     )    ->    Union[str, Any]:






					'''simple docstring'''







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

					_UpperCAmelCase    :							Optional[int]									=					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    :							str									=					argparse.Namespace(**_A     )
					_UpperCAmelCase    :							int									=					main(_A     )

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






					'''simple docstring'''







					_UpperCAmelCase    :							dict									=					CHEAP_ARGS.copy()

					_UpperCAmelCase    :							List[str]									=					"""summarization"""
					_UpperCAmelCase    :							Dict									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

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

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








"""simple docstring"""






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  int													=  {
    '''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       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """no_teacher""": True,
				    """freeze_encoder""": True,
				    """gpus""": 2,
				    """overwrite_output_dir""": True,
				    """sortish_sampler""": True,
				}
				self._test_distiller_cli_fork(_A      ,		check_contents=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """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    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
						return ""
					if v is False or v is None:
						return ""
					if v is True:  # or len(str(v))==0:
						return f'''--{k}'''
					return f'''--{k}={v}'''

				_UpperCAmelCase    :							List[Any]									=					[x for x in (convert(_A      ,		_A     ) for k, v in args_d.items()) if len(_A     )]
				_UpperCAmelCase    :							Union[str, Any]									=					[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
				execute_subprocess_async(_A      ,		env=self.get_env()     )

				_UpperCAmelCase    :							Union[str, Any]									=					os.listdir(_A     )
				_UpperCAmelCase    :							Dict									=					{os.path.basename(_A     ) for p in contents}
				_UpperCAmelCase    :							str									=					[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     )

				# get the following from the module, (we don't have access to `model` here)
				_UpperCAmelCase    :							List[str]									=					os.path.join(_A      ,		"""metrics.json"""     )
				_UpperCAmelCase    :							List[Any]									=					"""rouge2"""

				_UpperCAmelCase    :							List[Any]									=					load_json(_A     )
				# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
				print(_A     )
				_UpperCAmelCase    :							str									=					metrics["""val"""][-1]
				self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,		0.01     )
				self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,		_A     )
				self.assertEqual(len(metrics["""test"""]     )      ,		1     )
				_UpperCAmelCase    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





