






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 :    List[str]								=				logging.getLogger()
__a :    Optional[int]								=				torch.cuda.is_available()
__a :    Optional[Any]								=				{
    "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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Path     ,			__lowercase				:     list      )  -> Tuple:





    """simple docstring"""

    __A             =		"""\n""".join(__lowercase      )
    Path(__lowercase      ).open("""w"""      ).writelines(__lowercase      )


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


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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[int]      )  -> Optional[Any]:





    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(__lowercase     ,			f"{split}.source"      )     ,			__lowercase      )
        _dump_articles(os.path.join(__lowercase     ,			f"{split}.target"      )     ,			__lowercase      )
    return tmp_dir


class  __lowercase (		lowercase_					):







     '''simple docstring'''




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     Optional[int]			):


         """simple docstring"""



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




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


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         __A             =		{
             """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""",
         }
         __A             =		self._test_distiller_cli(UpperCamelCase_	,		check_contents=UpperCamelCase_			)

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

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




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


         """simple docstring"""



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

         __A             =		model(UpperCamelCase_	,		attention_mask=UpperCamelCase_	,		decoder_input_ids=UpperCamelCase_	,		use_cache=UpperCamelCase_			).logits

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




     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         __A             =		{
             """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""",
         }
         __A             =		self._test_distiller_cli(UpperCamelCase_	,		check_contents=UpperCamelCase_			)
         assert model.model.config.model_type == "mbart"

         __A             =		list(Path(model.output_dir			).glob("""*.ckpt"""			)			)
         self.assertEqual(1	,		len(UpperCamelCase_			)			)
         __A             =		list(Path(model.output_dir			).glob("""**/*.bin"""			)			)
         __A             =		list(Path(model.output_dir			).glob("""best_tfmr/*"""			)			)
         assert len(UpperCamelCase_			) > 2
         self.assertEqual(len(UpperCamelCase_			)	,		2			)




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         __A             =		{
             """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(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		{
             """teacher""": T5_TINY,
             """student""": T5_TINIER,
             """model_name_or_path""": T5_TINIER,
             """tokenizer_name""": T5_TINIER,
         }
         self._test_distiller_cli(UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     str=True			):


         """simple docstring"""



         __A             =		{
             """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(UpperCamelCase_			)
         __A             =		CHEAP_ARGS.copy()
         __A             =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
         __A             =		self.get_auto_remove_tmp_dir()

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

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

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


class  __lowercase (		lowercase_					):







     '''simple docstring'''




     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]	,		)
     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



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

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

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

         __A             =		load_json(module.output_dir / """text_batch.json"""			)
         assert isinstance(UpperCamelCase_	,		UpperCamelCase_			)
         assert len(UpperCamelCase_			) >= 4




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         __A             =		CHEAP_ARGS.copy()

         __A             =		"""summarization"""
         __A             =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

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

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

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





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		CHEAP_ARGS.copy()

         __A             =		"""summarization"""
         __A             =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

         __A             =		BART_TINY
         __A             =		self.get_auto_remove_tmp_dir()

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

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

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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     int      )  -> int:





    """simple docstring"""

    __A             =		1.0
    for i in range(1     ,			r + 1      ):
        ret *= (n + 1 - i) / i
    return round(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return choose(8 + n     ,			__lowercase      ) + choose(9 + n     ,			__lowercase      ) - 1_0



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return sum(non_bouncy_exact(__lowercase      ) for i in range(1     ,			n + 1      )      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int = 1_0_0      )  -> int:





    """simple docstring"""

    return non_bouncy_upto(__lowercase      )


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




