






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 			=							logging.getLogger()
a 			=							torch.cuda.is_available()
a 			=							{
    '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    a__    (	A		,					A       ):
   lowercase__    :						Any       =			"""\n""".join(_UpperCAmelCase       )
   Path(_UpperCAmelCase       ).open("""w"""       ).writelines(_UpperCAmelCase       )


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


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






   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(_UpperCAmelCase		,					F'''{split}.source'''       )		,					_UpperCAmelCase       )
      _dump_articles(os.path.join(_UpperCAmelCase		,					F'''{split}.target'''       )		,					_UpperCAmelCase       )
   return tmp_dir
class 							__snake_case(       _snake_case   ):



 @classmethod
 def     UpperCAmelCase     (						cls : Tuple						):

    """simple docstring"""






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



 @slow
 @require_torch_gpu
 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






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



 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    lowercase__    :						Tuple       =			{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
    self._test_distiller_cli(lowercase__						)



 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						List[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""",
    }
    lowercase__    :						List[str]       =			self._test_distiller_cli(lowercase__			,		check_contents=lowercase__						)

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

    lowercase__    :						Tuple       =			self.get_auto_remove_tmp_dir()
    convert_pl_to_hf(ckpts[0]			,		transformer_ckpts[0].parent			,		lowercase__						)
    assert os.path.exists(os.path.join(lowercase__			,		"""pytorch_model.bin"""						)						)



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






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

    lowercase__    :						Any       =			model(lowercase__			,		attention_mask=lowercase__			,		decoder_input_ids=lowercase__			,		use_cache=lowercase__						).logits

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



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    lowercase__    :						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""",
    }
    lowercase__    :						Dict       =			self._test_distiller_cli(lowercase__			,		check_contents=lowercase__						)
    assert model.model.config.model_type == "mbart"

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



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						List[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(lowercase__						)



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{
        """teacher""": T5_TINY,
        """student""": T5_TINIER,
        """model_name_or_path""": T5_TINIER,
        """tokenizer_name""": T5_TINIER,
    }
    self._test_distiller_cli(lowercase__						)







 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : Dict=True						):

    """simple docstring"""






    lowercase__    :						List[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(lowercase__						)
    lowercase__    :						List[Any]       =			CHEAP_ARGS.copy()
    lowercase__    :						Optional[Any]       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
    lowercase__    :						Any       =			self.get_auto_remove_tmp_dir()

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

    self.assertIn("""test_generations.txt"""			,		lowercase__						)
    self.assertIn("""test_results.txt"""			,		lowercase__						)

    lowercase__    :						Tuple       =			load_json(model.metrics_save_path						)
    lowercase__    :						Dict       =			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}''']			,		lowercase__						)
    lowercase__    :						str       =			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1						)
    self.assertEqual(len(metrics["""val"""]						)			,		lowercase__						)
    self.assertEqual(len(metrics["""test"""]						)			,		1						)
    return model
class 							__snake_case(       _snake_case   ):



 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,		)
 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : Union[str, Any]						):

    """simple docstring"""






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

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

    lowercase__    :						Union[str, Any]       =			module.model.get_input_embeddings()
    assert not input_embeds.weight.requires_grad
    if model == T5_TINY:
       lowercase__    :						Optional[int]       =			module.model.lm_head
       assert not lm_head.weight.requires_grad
       assert (lm_head.weight == input_embeds.weight).all().item()
    elif model == FSMT_TINY:
       lowercase__    :						int       =			module.model.model
       lowercase__    :						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:
       lowercase__    :						Optional[Any]       =			module.model.model
       lowercase__    :						List[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

    lowercase__    :						Any       =			load_json(module.output_dir / """text_batch.json"""						)
    assert isinstance(lowercase__			,		lowercase__						)
    assert len(lowercase__						) >= 4



 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    lowercase__    :						Any       =			CHEAP_ARGS.copy()

    lowercase__    :						List[Any]       =			"""summarization"""
    lowercase__    :						Optional[Any]       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

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

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

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







 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			CHEAP_ARGS.copy()

    lowercase__    :						Optional[int]       =			"""summarization"""
    lowercase__    :						Union[str, Any]       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

    lowercase__    :						str       =			BART_TINY
    lowercase__    :						Optional[Any]       =			self.get_auto_remove_tmp_dir()

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

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

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







from collections.abc import Callable

__magic_name__       =     Callable[[float], float]  # type alias for a real -> real function





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):
					"""simple docstring"""






					lowercase						   =       float(_UpperCAmelCase				)
					while True:
										try:
															lowercase						   =       prev_guess - function(_UpperCAmelCase				) / derivative(_UpperCAmelCase				)
										except ZeroDivisionError:
															raise ZeroDivisionError('Could not find root'				) from None
										if abs(prev_guess - next_guess				) < 10**-5:
															return next_guess
										lowercase						   =       next_guess





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					return (x**3) - (2 * x) - 5





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					return 3 * (x**2) - 2


if __name__ == "__main__":
		print(newton(f, fa, 3))






