

'''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)

UpperCAmelCase				: Optional[Any]         =  logging.getLogger()
UpperCAmelCase				: str         =  torch.cuda.is_available()
UpperCAmelCase				: List[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': 4_2,
    '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-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    '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__		)     ->   Any:




     """simple docstring"""


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


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


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







def      a__ (						a__		)     ->   int:




     """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 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[int]    )						->				Optional[int]:


      """simple docstring"""




      logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
      return cls
 @slow
 @require_torch_gpu
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Optional[int]:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
      self._test_distiller_cli(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """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""",
      }
      __SCREAMING_SNAKE_CASE						     =   self._test_distiller_cli(__SCREAMING_SNAKE_CASE      ,							check_contents=__SCREAMING_SNAKE_CASE    )

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

      __SCREAMING_SNAKE_CASE						     =   self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]      ,							transformer_ckpts[0].parent      ,							__SCREAMING_SNAKE_CASE    )
      assert os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE      ,							"""pytorch_model.bin"""    )    )
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				List[str]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							decoder_input_ids=__SCREAMING_SNAKE_CASE      ,							use_cache=__SCREAMING_SNAKE_CASE    ).logits

      __SCREAMING_SNAKE_CASE						     =   nn.functional.log_softmax(__SCREAMING_SNAKE_CASE      ,							dim=-1    )
      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   label_smoothed_nll_loss(
          __SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							0.1      ,							ignore_index=model.config.pad_token_id    )
      with self.assertRaises(__SCREAMING_SNAKE_CASE    ):
           # TODO: understand why this breaks
           self.assertEqual(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				Optional[int]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE						     =   list(Path(model.output_dir    ).glob("""*.ckpt"""    )    )
      self.assertEqual(1      ,							len(__SCREAMING_SNAKE_CASE    )    )
      __SCREAMING_SNAKE_CASE						     =   list(Path(model.output_dir    ).glob("""**/*.bin"""    )    )
      __SCREAMING_SNAKE_CASE						     =   list(Path(model.output_dir    ).glob("""best_tfmr/*"""    )    )
      assert len(__SCREAMING_SNAKE_CASE    ) > 2
      self.assertEqual(len(__SCREAMING_SNAKE_CASE    )      ,							2    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """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(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Tuple:


      """simple docstring"""




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


 def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : str=True    )						->				List[Any]:


      """simple docstring"""




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

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

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

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







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,							)
 def    UpperCAmelCase__	(	self      : int      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				str:


      """simple docstring"""




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

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

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

      __SCREAMING_SNAKE_CASE						     =   load_json(module.output_dir / """text_batch.json"""    )
      assert isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
      assert len(__SCREAMING_SNAKE_CASE    ) >= 4
 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   CHEAP_ARGS.copy()

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

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

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

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


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   CHEAP_ARGS.copy()

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

      __SCREAMING_SNAKE_CASE						     =   BART_TINY
      __SCREAMING_SNAKE_CASE						     =   self.get_auto_remove_tmp_dir()

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

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

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









'''simple docstring'''



from __future__ import annotations

from collections.abc import Callable







def      a__ (						a__   ,					a__   ,					a__   ,					a__ = 1_00   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   x_start
     __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
     __SCREAMING_SNAKE_CASE						     =   0.0

     for _ in range(a__		):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          __SCREAMING_SNAKE_CASE						     =   (x_end - x_start) / steps + xa
          __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
          area += abs(fxa + fxa		) * (xa - xa) / 2

          # Increment step
          __SCREAMING_SNAKE_CASE						     =   xa
          __SCREAMING_SNAKE_CASE						     =   fxa
     return area


if __name__ == "__main__":







  def      a__ (						a__		):




       """simple docstring"""


       return x**3

  print('f(x) = x^3')
  print('The area between the curve, x = -10, x = 10 and the x axis is:')
  UpperCAmelCase				: Optional[Any]         =  1_0
  while i <= 1_0_0_0_0_0:
    UpperCAmelCase				: Union[str, Any]         =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 1_0






