




'''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)

A				: Tuple					      =			logging.getLogger()
A				: Optional[Any]					      =			torch.cuda.is_available()
A				: 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 							snake_case_    (  a__						:      Path       ,a__						:      list     ):



    """simple docstring"""




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


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


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



    """simple docstring"""




    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 			SCREAMING_SNAKE_CASE(							__A	):

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

           """simple docstring"""



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

       @slow
       @require_torch_gpu
       def        snake_case__     (       self	)       ->   Optional[Any]:

           """simple docstring"""



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

       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



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

       def        snake_case__     (       self	)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase     							=		{
               """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     							=		self._test_distiller_cli(lowerCamelCase__   ,						check_contents=lowerCamelCase__	)

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

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

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

           """simple docstring"""



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

           __lowercase     							=		model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						decoder_input_ids=lowerCamelCase__   ,						use_cache=lowerCamelCase__	).logits

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

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

           """simple docstring"""



           __lowercase     							=		{
               """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     							=		self._test_distiller_cli(lowerCamelCase__   ,						check_contents=lowerCamelCase__	)
           assert model.model.config.model_type == "mbart"

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

       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase     							=		{
               """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(lowerCamelCase__	)

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

           """simple docstring"""



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




       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__=True	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		{
               """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(lowerCamelCase__	)
           __lowercase     							=		CHEAP_ARGS.copy()
           __lowercase     							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
           __lowercase     							=		self.get_auto_remove_tmp_dir()

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

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

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






class 			SCREAMING_SNAKE_CASE(							__A	):

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

           """simple docstring"""



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

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

           __lowercase     							=		module.model.get_input_embeddings()
           assert not input_embeds.weight.requires_grad
           if model == T5_TINY:
               __lowercase     							=		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     							=		module.model.model
               __lowercase     							=		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     							=		module.model.model
               __lowercase     							=		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     							=		load_json(module.output_dir / """text_batch.json"""	)
           assert isinstance(lowerCamelCase__   ,						lowerCamelCase__	)
           assert len(lowerCamelCase__	) >= 4

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

           """simple docstring"""



           __lowercase     							=		CHEAP_ARGS.copy()

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

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

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

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




       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		CHEAP_ARGS.copy()

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

           __lowercase     							=		BART_TINY
           __lowercase     							=		self.get_auto_remove_tmp_dir()

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

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

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










'''simple docstring'''
from math import gcd
def 							snake_case_    (  a__						:      int = 1_20_00     ):



    """simple docstring"""




    __lowercase     							=		0
    for d in range(max_d + 1     ):
        for n in range(d // 3 + 1       ,(d + 1) // 2     ):
            if gcd(a__       ,a__     ) == 1:
                fractions_number += 1
    return fractions_number


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




