


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      			=  logging.getLogger()
__UpperCAmelCase      			=  torch.cuda.is_available()
__UpperCAmelCase      			=  {
    """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_  :	List[str]							,  A_  :	int      )			->			Any:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						="""\n""".join(A_      )
   Path(A_      ).open("w"      ).writelines(A_      )


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


__UpperCAmelCase      			=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 	_lowerCamelCase  (		A_  :	Dict      )			->			Optional[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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      @classmethod
      def    UpperCAmelCase							(		cls)					->						int:







         """simple docstring"""







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




      @slow
      @require_torch_gpu
      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=list_models()
         UpperCamelCase__ :			str 						="""sshleifer"""
         UpperCamelCase__ :			Any 						=[x.modelId for x in model_list if x.modelId.startswith(__SCREAMING_SNAKE_CASE)]
         UpperCamelCase__ :			List[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(__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)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						={"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
         self._test_distiller_cli(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[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__ :			Union[str, Any] 						=self._test_distiller_cli(__SCREAMING_SNAKE_CASE   ,			check_contents=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Union[str, Any] 						=list(Path(model.output_dir).glob("*.ckpt"))
         self.assertEqual(1   ,			len(__SCREAMING_SNAKE_CASE))
         UpperCamelCase__ :			Optional[int] 						=list(Path(model.output_dir).glob("**/*.bin"))
         self.assertEqual(len(__SCREAMING_SNAKE_CASE)   ,			2)
         UpperCamelCase__ :			int 						=lmap(str.strip   ,			Path(model.hparams.data_dir).joinpath("test.source").open().readlines())
         UpperCamelCase__ :			List[str] 						=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__ :			str 						=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)					->						Tuple:







         """simple docstring"""







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

         UpperCamelCase__ :			Any 						=nn.functional.log_softmax(__SCREAMING_SNAKE_CASE   ,			dim=-1)
         UpperCamelCase__ :			Any 						=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)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						={
             """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__ :			Dict 						=self._test_distiller_cli(__SCREAMING_SNAKE_CASE   ,			check_contents=__SCREAMING_SNAKE_CASE)
         assert model.model.config.model_type == "mbart"

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




      def    UpperCAmelCase							(		self)					->						List[Any]:







         """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(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						={
             """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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=True)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						={
             """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__ :			dict 						=CHEAP_ARGS.copy()
         UpperCamelCase__ :			Optional[int] 						=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         UpperCamelCase__ :			str 						=self.get_auto_remove_tmp_dir()

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






      '''simple docstring'''







      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,			)
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







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

         UpperCamelCase__ :			List[str] 						=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         UpperCamelCase__ :			str 						=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__ :			Union[str, Any] 						=argparse.Namespace(**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=main(__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Union[str, Any] 						=module.model.get_input_embeddings()
         assert not input_embeds.weight.requires_grad
         if model == T5_TINY:
            UpperCamelCase__ :			str 						=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__ :			List[Any] 						=module.model.model
            UpperCamelCase__ :			Any 						=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__ :			Dict 						=module.model.model
            UpperCamelCase__ :			int 						=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__ :			Any 						=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)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			dict 						=CHEAP_ARGS.copy()

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




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			dict 						=CHEAP_ARGS.copy()

         UpperCamelCase__ :			List[Any] 						="""summarization"""
         UpperCamelCase__ :			List[Any] 						=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

         UpperCamelCase__ :			str 						=BART_TINY
         UpperCamelCase__ :			Optional[int] 						=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__ :			Union[str, Any] 						=argparse.ArgumentParser()
         UpperCamelCase__ :			Union[str, Any] 						=pl.Trainer.add_argparse_args(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Union[str, Any] 						=SummarizationModule.add_model_specific_args(__SCREAMING_SNAKE_CASE   ,			os.getcwd())
         UpperCamelCase__ :			int 						={"""--help""": True}

         # --help test
         with pytest.raises(__SCREAMING_SNAKE_CASE) as excinfo:
            with CaptureStdout() as cs:
               UpperCamelCase__ :			Any 						=parser.parse_args(__SCREAMING_SNAKE_CASE)
            assert False, "--help is expected to sys.exit"
         assert excinfo.type == SystemExit
         UpperCamelCase__ :			Dict 						=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__ :			List[str] 						="""non_existing_scheduler"""
         UpperCamelCase__ :			Optional[Any] 						={F'''--lr_scheduler={unsupported_param}'''}
         with pytest.raises(__SCREAMING_SNAKE_CASE) as excinfo:
            with CaptureStderr() as cs:
               UpperCamelCase__ :			List[Any] 						=parser.parse_args(__SCREAMING_SNAKE_CASE)
            assert False, "invalid argument is expected to sys.exit"
         assert excinfo.type == SystemExit
         UpperCamelCase__ :			Tuple 						=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__ :			List[Any] 						="""cosine"""
         UpperCamelCase__ :			List[Any] 						=args_d.copy()
         UpperCamelCase__ :			Any 						=supported_param
         UpperCamelCase__ :			int 						=argparse.Namespace(**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			List[Any] 						=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







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Any					, lowerCamelCase	:					int						)->	None:
          snake_case__					:	Optional[int]          =					value
          snake_case__					:	Node | None          =					None
          snake_case__					:	Node | None          =					None







class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42




   def __init__(	self	:					str						)->	None:
          snake_case__					:	int          =					0




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int						)->	None:
          if node is None:
                 return

          if path_sum == self.target:
                 self.paths += 1

          if node.left:
                 self.depth_first_search(node.left					, path_sum + node.left.value						)
          if node.right:
                 self.depth_first_search(node.right					, path_sum + node.right.value						)




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int | None = None						)->	int:
          if node is None:
                 return 0
          if target is not None:
                 snake_case__					:	Dict          =					target

          self.depth_first_search(lowerCamelCase					, node.value						)
          self.path_sum(node.left						)
          self.path_sum(node.right						)

          return self.paths


if __name__ == "__main__":
     import doctest

     doctest.testmod()




