code: 
  
  
  
  
  
  
  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 			lowercase__  (				A_:	Path     ,			A_:	list		)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							="""\n""".join(A_		)
         Path(A_		).open("""w"""		).writelines(A_		)
  
  
  __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 			lowercase__  (				A_:	str		)  ->							Dict:
  
  
  
  
  
  
  
         """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 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      @classmethod
      def _a       (							cls			:    int			)			->       Dict:
             logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
             return cls
  
  
  
      @slow
      @require_torch_gpu
      def _a       (							self			:    Union[str, Any]			)			->       Tuple:
             __UpperCAmelCase    							=list_models()
             __UpperCAmelCase    							="""sshleifer"""
             __UpperCAmelCase    							=[x.modelId for x in model_list if x.modelId.startswith(__SCREAMING_SNAKE_CASE			)]
             __UpperCAmelCase    							=["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
             __UpperCAmelCase    							=[]
             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 _a       (							self			:    str			)			->       List[str]:
             __UpperCAmelCase    							={"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
             self._test_distiller_cli(__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       Optional[Any]:
             __UpperCAmelCase    							={
                 """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    							=self._test_distiller_cli(__SCREAMING_SNAKE_CASE   ,    check_contents=__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=list(Path(model.output_dir			).glob("""*.ckpt"""			)			)
             self.assertEqual(1   ,    len(__SCREAMING_SNAKE_CASE			)			)
             __UpperCAmelCase    							=list(Path(model.output_dir			).glob("""**/*.bin"""			)			)
             self.assertEqual(len(__SCREAMING_SNAKE_CASE			)   ,    2			)
             __UpperCAmelCase    							=lmap(str.strip   ,    Path(model.hparams.data_dir			).joinpath("""test.source"""			).open().readlines()			)
             __UpperCAmelCase    							=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()			)
  
             __UpperCAmelCase    							=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 _a       (							self			:    Optional[Any]			)			->       List[Any]:
             __UpperCAmelCase    							=AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase							,   __UpperCAmelCase    							=model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
             __UpperCAmelCase    							=torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]   ,    dtype=torch.long   ,    device=model.device			)
             __UpperCAmelCase    							=target_ids[:, :-1].contiguous()  # Why this line?
             __UpperCAmelCase    							=target_ids[:, 1:].clone()  # why clone?
             __UpperCAmelCase    							=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
  
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    decoder_input_ids=__SCREAMING_SNAKE_CASE   ,    use_cache=__SCREAMING_SNAKE_CASE			).logits
  
             __UpperCAmelCase    							=nn.functional.log_softmax(__SCREAMING_SNAKE_CASE   ,    dim=-1			)
             __UpperCAmelCase							,   __UpperCAmelCase    							=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 _a       (							self			:    Any			)			->       List[Any]:
             __UpperCAmelCase    							={
                 """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    							=self._test_distiller_cli(__SCREAMING_SNAKE_CASE   ,    check_contents=__SCREAMING_SNAKE_CASE			)
             assert model.model.config.model_type == "mbart"
  
             __UpperCAmelCase    							=list(Path(model.output_dir			).glob("""*.ckpt"""			)			)
             self.assertEqual(1   ,    len(__SCREAMING_SNAKE_CASE			)			)
             __UpperCAmelCase    							=list(Path(model.output_dir			).glob("""**/*.bin"""			)			)
             __UpperCAmelCase    							=list(Path(model.output_dir			).glob("""best_tfmr/*"""			)			)
             assert len(__SCREAMING_SNAKE_CASE			) > 2
             self.assertEqual(len(__SCREAMING_SNAKE_CASE			)   ,    2			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
             __UpperCAmelCase    							={
                 """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 _a       (							self			:    List[str]			)			->       str:
             __UpperCAmelCase    							={
                 """teacher""": T5_TINY,
                 """student""": T5_TINIER,
                 """model_name_or_path""": T5_TINIER,
                 """tokenizer_name""": T5_TINIER,
             }
             self._test_distiller_cli(__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True			)			->       List[Any]:
             __UpperCAmelCase    							={
                 """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			)
             __UpperCAmelCase    							=CHEAP_ARGS.copy()
             __UpperCAmelCase    							=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
             __UpperCAmelCase    							=self.get_auto_remove_tmp_dir()
  
             args_d.update(data_dir=__SCREAMING_SNAKE_CASE   ,    output_dir=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=distill_main(argparse.Namespace(**__SCREAMING_SNAKE_CASE			)			)
             if not check_contents:
                    return model
             __UpperCAmelCase    							=os.listdir(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							={os.path.basename(__SCREAMING_SNAKE_CASE			) for p in contents}
             __UpperCAmelCase    							=[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			)
  
             __UpperCAmelCase    							=load_json(model.metrics_save_path			)
             __UpperCAmelCase    							=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			)
             __UpperCAmelCase    							=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 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,    )
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
             __UpperCAmelCase    							=CHEAP_ARGS.copy()
             __UpperCAmelCase    							="""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
             __UpperCAmelCase    							=0.1 if task == """translation""" else 0
  
             __UpperCAmelCase    							=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=argparse.Namespace(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=main(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=module.model.get_input_embeddings()
             assert not input_embeds.weight.requires_grad
             if model == T5_TINY:
                    __UpperCAmelCase    							=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    							=module.model.model
                    __UpperCAmelCase    							=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    							=module.model.model
                    __UpperCAmelCase    							=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    							=load_json(module.output_dir / """text_batch.json"""			)
             assert isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
             assert len(__SCREAMING_SNAKE_CASE			) >= 4
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Tuple:
             __UpperCAmelCase    							=CHEAP_ARGS.copy()
  
             __UpperCAmelCase    							="""summarization"""
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=BART_TINY
             __UpperCAmelCase    							=self.get_auto_remove_tmp_dir()
             __UpperCAmelCase    							=args_d.copy()
             args_da.update(
                 model_name_or_path=__SCREAMING_SNAKE_CASE   ,    output_dir=__SCREAMING_SNAKE_CASE   ,    )
             __UpperCAmelCase    							=("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
             for p in extra_model_params:
                    __UpperCAmelCase    							=0.5
             __UpperCAmelCase    							=argparse.Namespace(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=T5_TINY
             __UpperCAmelCase    							=self.get_auto_remove_tmp_dir()
             __UpperCAmelCase    							=args_d.copy()
             args_da.update(
                 model_name_or_path=__SCREAMING_SNAKE_CASE   ,    output_dir=__SCREAMING_SNAKE_CASE   ,    )
             __UpperCAmelCase    							="""encoder_layerdrop"""
             __UpperCAmelCase    							=0.5
             __UpperCAmelCase    							=argparse.Namespace(**__SCREAMING_SNAKE_CASE			)
             with pytest.raises(__SCREAMING_SNAKE_CASE			) as excinfo:
                    __UpperCAmelCase    							=main(__SCREAMING_SNAKE_CASE			)
             assert str(excinfo.value			) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''
  
  
  
      def _a       (							self			:    Tuple			)			->       Tuple:
             __UpperCAmelCase    							=CHEAP_ARGS.copy()
  
             __UpperCAmelCase    							="""summarization"""
             __UpperCAmelCase    							=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
  
             __UpperCAmelCase    							=BART_TINY
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=argparse.ArgumentParser()
             __UpperCAmelCase    							=pl.Trainer.add_argparse_args(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=SummarizationModule.add_model_specific_args(__SCREAMING_SNAKE_CASE   ,    os.getcwd()			)
             __UpperCAmelCase    							={"""--help""": True}
  
             # --help test
             with pytest.raises(__SCREAMING_SNAKE_CASE			) as excinfo:
                    with CaptureStdout() as cs:
                           __UpperCAmelCase    							=parser.parse_args(__SCREAMING_SNAKE_CASE			)
                    assert False, "--help is expected to sys.exit"
             assert excinfo.type == SystemExit
             __UpperCAmelCase    							=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    							="""non_existing_scheduler"""
             __UpperCAmelCase    							={f'''--lr_scheduler={unsupported_param}'''}
             with pytest.raises(__SCREAMING_SNAKE_CASE			) as excinfo:
                    with CaptureStderr() as cs:
                           __UpperCAmelCase    							=parser.parse_args(__SCREAMING_SNAKE_CASE			)
                    assert False, "invalid argument is expected to sys.exit"
             assert excinfo.type == SystemExit
             __UpperCAmelCase    							=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    							="""cosine"""
             __UpperCAmelCase    							=args_d.copy()
             __UpperCAmelCase    							=supported_param
             __UpperCAmelCase    							=argparse.Namespace(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=main(__SCREAMING_SNAKE_CASE			)
             assert (
                 getattr(model.hparams   ,    """lr_scheduler"""			) == supported_param
             ), f'''lr_scheduler={supported_param} shouldn\'t fail'''
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  