





def 							lowerCAmelCase_				(    A_ = 10_00):
    return sum(i for i in range(A_) if i % 3 == 0 or i % 5 == 0)


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








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__:  Union[str, Any]       					=					logging.getLogger()
A__:  Any       					=					torch.cuda.is_available()
A__:  Tuple       					=					{
    '''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 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					List[Any]												=      "\n".join(A_)
    Path(A_).open("w").writelines(A_)


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


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



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




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      @classmethod
      def 	UpperCAmelCase_   (						cls:	str			):
          '''simple docstring'''


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






      @slow
      @require_torch_gpu
      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      list_models()
          UpperCamelCase__:					Any												=      "sshleifer"
          UpperCamelCase__:					Dict												=      [x.modelId for x in model_list if x.modelId.startswith(__lowerCamelCase			)]
          UpperCamelCase__:					Union[str, Any]												=      ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
          UpperCamelCase__:					List[Any]												=      []
          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 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
          self._test_distiller_cli(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					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__:					List[str]												=      self._test_distiller_cli(__lowerCamelCase					,  check_contents=__lowerCamelCase			)

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

          UpperCamelCase__:					List[str]												=      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 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


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

          UpperCamelCase__:					Optional[int]												=      model(__lowerCamelCase					,  attention_mask=__lowerCamelCase					,  decoder_input_ids=__lowerCamelCase					,  use_cache=__lowerCamelCase			).logits

          UpperCamelCase__:					Optional[Any]												=      nn.functional.log_softmax(__lowerCamelCase					,  dim=-1			)
          UpperCamelCase__							,				UpperCamelCase__:					int												=      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 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					List[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(__lowerCamelCase					,  check_contents=__lowerCamelCase			)
          assert model.model.config.model_type == "mbart"

          UpperCamelCase__:					Tuple												=      list(Path(model.output_dir			).glob("*.ckpt"			)			)
          self.assertEqual(1					,  len(__lowerCamelCase			)			)
          UpperCamelCase__:					Tuple												=      list(Path(model.output_dir			).glob("**/*.bin"			)			)
          UpperCamelCase__:					Dict												=      list(Path(model.output_dir			).glob("best_tfmr/*"			)			)
          assert len(__lowerCamelCase			) > 2
          self.assertEqual(len(__lowerCamelCase			)					,  2			)






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					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(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      {
              "teacher": T5_TINY,
              "student": T5_TINIER,
              "model_name_or_path": T5_TINIER,
              "tokenizer_name": T5_TINIER,
          }
          self._test_distiller_cli(__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  __lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	List[str]=True			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      {
              "label_smoothing": 0.0,
              "early_stopping_patience": -1,
              "train_batch_size": 1,
              "eval_batch_size": 2,
              "max_epochs": 2,
              "alpha_mlm": 0.2,
              "alpha_ce": 0.8,
              "do_predict": True,
              "model_name_or_path": "sshleifer/tinier_bart",
              "teacher": CHEAP_ARGS["model_name_or_path"],
              "val_check_interval": 0.5,
          }
          default_updates.update(__lowerCamelCase			)
          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()
          UpperCamelCase__:					int												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
          UpperCamelCase__:					Any												=      self.get_auto_remove_tmp_dir()

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

          UpperCamelCase__:					Any												=      load_json(model.metrics_save_path			)
          UpperCamelCase__:					List[Any]												=      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			)
          UpperCamelCase__:					Any												=      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    _a  (  UpperCamelCase__):

      """simple docstring"""


      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,  )
      def 	UpperCAmelCase_   (						self:	List[str]					,  __lowerCamelCase:	Optional[Any]			):
          '''simple docstring'''


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

          UpperCamelCase__:					Any												=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
          UpperCamelCase__:					Optional[int]												=      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
          UpperCamelCase__:					Dict												=      argparse.Namespace(**__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      main(__lowerCamelCase			)

          UpperCamelCase__:					Any												=      module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              UpperCamelCase__:					Tuple												=      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__:					Tuple												=      module.model.model
              UpperCamelCase__:					Dict												=      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__:					Any												=      module.model.model
              UpperCamelCase__:					Union[str, Any]												=      bart.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not bart.shared.weight.requires_grad
              # check that embeds are the same
              assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
              assert bart.decoder.embed_tokens == bart.shared

          UpperCamelCase__:					Tuple												=      load_json(module.output_dir / "text_batch.json"			)
          assert isinstance(__lowerCamelCase					,  __lowerCamelCase			)
          assert len(__lowerCamelCase			) >= 4






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






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					dict												=      CHEAP_ARGS.copy()

          UpperCamelCase__:					str												=      "summarization"
          UpperCamelCase__:					Any												=      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=__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
          UpperCamelCase__:					Dict												=      argparse.ArgumentParser()
          UpperCamelCase__:					str												=      pl.Trainer.add_argparse_args(__lowerCamelCase			)
          UpperCamelCase__:					Dict												=      SummarizationModule.add_model_specific_args(__lowerCamelCase					,  os.getcwd()			)
          UpperCamelCase__:					List[Any]												=      {"--help": True}

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

