'''simple docstring'''




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__SCREAMING_SNAKE_CASE       :     str     =  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
  from run_translation import main  # noqa


set_seed(42)
__SCREAMING_SNAKE_CASE       :     Any     =  """sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE       :     int     =  """sshleifer/tiny-mbart"""

@require_torch
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				Union[str, Any]						,					A						:				List[str]=False						,					A						:				Union[str, Any]=None						,					A						:				Dict=True						,					A						:				Any=True						,					A						:				Any=True						,					A						:				Tuple=True						,					):
             _UpperCAmelCase     :  List[str]							      =    self.run_trainer(
                 eval_steps=1						,					max_len=12						,					model_name=A						,					num_train_epochs=1						,					distributed=A						,					extra_args_str=A						,					predict_with_generate=A						,					do_train=A						,					do_eval=A						,					do_predict=A						,					)
             _UpperCAmelCase     :  int							      =    TrainerState.load_from_json(os.path.join(A						,					"trainer_state.json"       )       ).log_history

             if not do_eval:
                    return

             _UpperCAmelCase     :  Union[str, Any]							      =    [log for log in logs if "eval_loss" in log.keys()]

             _UpperCAmelCase     :  Optional[int]							      =    eval_metrics[0]
             if predict_with_generate:
                    assert "eval_bleu" in first_step_stats

                    _UpperCAmelCase     :  int							      =    eval_metrics[-1]
                    assert isinstance(last_step_stats["eval_bleu"]						,					A       )
                    assert not math.isnan(float(last_step_stats["eval_loss"]       )       ), "eval_loss must not be `nan`"




      @require_torch_non_multi_gpu
      def      _A	(       self						:				Tuple       ):
             self.run_seqaseq_quick()




      @require_torch_multi_gpu
      def      _A	(       self						:				int       ):
             self.run_seqaseq_quick(distributed=A       )




      @require_torch_multi_gpu
      def      _A	(       self						:				Any       ):
             self.run_seqaseq_quick(distributed=A       )




      @unittest.skip("Requires an update of the env running those tests"       )
      @require_torch_multi_gpu
      @require_fairscale
      def      _A	(       self						:				Any       ):
             self.run_seqaseq_quick(distributed=A						,					extra_args_str="--sharded_ddp simple"       )




      @unittest.skip("Requires an update of the env running those tests"       )
      @require_torch_multi_gpu
      @require_fairscale
      def      _A	(       self						:				Dict       ):
             self.run_seqaseq_quick(distributed=A						,					extra_args_str="--sharded_ddp simple --fp16"       )




      @unittest.skip("Requires an update of the env running those tests"       )
      @require_torch_multi_gpu
      @require_fairscale
      def      _A	(       self						:				Optional[int]       ):
             self.run_seqaseq_quick(distributed=A						,					extra_args_str="--sharded_ddp zero_dp_2"						,					predict_with_generate=A       )




      @unittest.skip("Requires an update of the env running those tests"       )
      @require_torch_multi_gpu
      @require_fairscale
      def      _A	(       self						:				Tuple       ):
             self.run_seqaseq_quick(
                 distributed=A						,					extra_args_str="--sharded_ddp zero_dp_2 --fp16"						,					predict_with_generate=A       )




      @require_apex
      @require_torch_gpu
      def      _A	(       self						:				Tuple       ):
             # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
             # program and it breaks other tests that run from the same pytest worker, therefore until this is
             # sorted out it must be run only in an external program, that is distributed=True in this
             # test and only under one or more gpus - if we want cpu will need to make a special test
             #
             # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
             # 2nd main() call it botches the future eval.
             #
             self.run_seqaseq_quick(distributed=A						,					extra_args_str="--fp16 --fp16_backend=apex"       )
             # test 2nd time - was getting eval_loss': nan'
             # to reproduce the problem set distributed=False
             self.run_seqaseq_quick(distributed=A						,					extra_args_str="--fp16 --fp16_backend=apex"       )




      @parameterized.expand(["base", "low", "high", "mixed"]       )
      @require_torch_multi_gpu
      def      _A	(       self						:				Tuple						,					A						:				int       ):
             # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
             _UpperCAmelCase     :  str							      =    {
                 # test with the default log_level - should be info and thus log info once
                 "base": {"extra_args_str": "", "n_matches": 1},
                 # test with low log_level and log_level_replica - should be noisy on all processes
                 # now the info string should appear twice on 2 processes
                 "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
                 # test with high log_level and low log_level_replica
                 # now the info string should appear once only on the replica
                 "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
                 # test with high log_level and log_level_replica - should be quiet on all processes
                 "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
             }

             _UpperCAmelCase     :  List[Any]							      =    experiments[experiment_id]
             _UpperCAmelCase     :  Dict							      =    {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
             _UpperCAmelCase     :  List[str]							      =    "Running training"
             with CaptureStderr() as cl:
                    self.run_seqaseq_quick(**A						,					extra_args_str=data["extra_args_str"]       )
             _UpperCAmelCase     :  Tuple							      =    len(re.findall(A						,					cl.err       )       )
             self.assertEqual(A						,					data["n_matches"]       )




      @slow
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  List[str]							      =    self.run_trainer(
                 eval_steps=2						,					max_len=128						,					model_name=A						,					learning_rate=3E-4						,					num_train_epochs=10						,					distributed=A						,					)

             # Check metrics
             _UpperCAmelCase     :  Any							      =    TrainerState.load_from_json(os.path.join(A						,					"trainer_state.json"       )       ).log_history
             _UpperCAmelCase     :  Tuple							      =    [log for log in logs if "eval_loss" in log.keys()]
             _UpperCAmelCase     :  Union[str, Any]							      =    eval_metrics[0]
             _UpperCAmelCase     :  List[str]							      =    eval_metrics[-1]

             assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
             assert isinstance(last_step_stats["eval_bleu"]						,					A       )

             # test if do_predict saves generations and metrics
             _UpperCAmelCase     :  Optional[int]							      =    os.listdir(A       )
             _UpperCAmelCase     :  str							      =    {os.path.basename(A       ) for p in contents}
             assert "generated_predictions.txt" in contents
             assert "predict_results.json" in contents




      @slow
      @require_bitsandbytes
      def      _A	(       self						:				int       ):
             from transformers.training_args import OptimizerNames

             def train_and_return_metrics(A						:				str       ) -> Tuple[int, float]:
                    _UpperCAmelCase     :  int							      =    "--skip_memory_metrics 0"

                    _UpperCAmelCase     :  Tuple							      =    self.run_trainer(
                        max_len=128						,					model_name=A						,					learning_rate=3E-4						,					num_train_epochs=1						,					optim=A						,					distributed=A						,					extra_args_str=A						,					do_eval=A						,					do_predict=A						,					n_gpus_to_use=1						,					)

                    # Check metrics
                    _UpperCAmelCase     :  Optional[Any]							      =    TrainerState.load_from_json(Path(A						,					"trainer_state.json"       )       ).log_history
                    _UpperCAmelCase     :  int							      =    int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20       )

                    _UpperCAmelCase     :  Optional[int]							      =    logs[0]["train_loss"]
                    return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

             _UpperCAmelCase     :  str							      =    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

             _UpperCAmelCase     :  Optional[Any]							      =    gpu_peak_mem_orig + gpu_alloc_mem_orig
             _UpperCAmelCase     :  Union[str, Any]							      =    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
             _UpperCAmelCase     :  int							      =    gpu_total_mem_orig - gpu_total_mem_bnb

             # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
             # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
             # in 2 bytes and the diff in optim memory usage is derived as so:
             #
             # - normal 25*8=~200MB (8 bytes per param)
             # - bnb    25*2= ~50MB (2 bytes per param)
             #
             # Thus we should expect ~150MB total memory saved.
             #
             # Peak memory should be the same - the total should be different by about that same margin
             #
             # After leaving a small margin to accommodate for differences between gpus let's check
             # that we have at least 120MB in savings
             _UpperCAmelCase     :  List[str]							      =    120

             # uncomment the following if this test starts failing - requires py38 for a new print feature
             # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
             # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
             # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
             # print(f"{gpu_alloc_mem_diff=}MB")
             # print(f"{gpu_peak_mem_diff=}MB")
             # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
             # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

             self.assertGreater(
                 A						,					A						,					"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
                 F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
                 F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""						,					)

             self.assertGreater(
                 A						,					A						,					"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
                 F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
                 F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""						,					)

             self.assertEqual(
                 A						,					A						,					F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""       )





      def      _A	(       self						:				Dict						,					A						:				int						,					A						:				str						,					A						:				int						,					A						:				float = 3E-3						,					A						:				str = "adafactor"						,					A						:				bool = False						,					A						:				str = None						,					A						:				int = 0						,					A						:				bool = True						,					A						:				bool = True						,					A						:				bool = True						,					A						:				bool = True						,					A						:				int = None						,					):
             _UpperCAmelCase     :  Optional[int]							      =    self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
             _UpperCAmelCase     :  List[str]							      =    self.get_auto_remove_tmp_dir()
             _UpperCAmelCase     :  List[Any]							      =    F"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(A       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(A       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

             _UpperCAmelCase     :  List[Any]							      =    F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(A       )}
        """.split()

             _UpperCAmelCase     :  Tuple							      =    "\n            --do_predict\n        ".split()

             _UpperCAmelCase     :  Union[str, Any]							      =    []
             if do_train:
                    args += args_train

             if do_eval:
                    args += args_eval

             if do_predict:
                    args += args_predict

             if predict_with_generate:
                    args += "--predict_with_generate".split()

             if do_train:
                    if optim == "adafactor":
                           args += "--adafactor".split()
                    else:
                           args += F"""--optim {optim}""".split()

             if extra_args_str is not None:
                    args += extra_args_str.split()

             if distributed:
                    if n_gpus_to_use is None:
                           _UpperCAmelCase     :  Dict							      =    get_gpu_count()
                    _UpperCAmelCase     :  Dict							      =    get_torch_dist_unique_port()
                    _UpperCAmelCase     :  List[str]							      =    F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
                    _UpperCAmelCase     :  str							      =    [sys.executable] + distributed_args + args
                    # keep for quick debug
                    # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                    execute_subprocess_async(A						,					env=self.get_env()       )
             else:
                    _UpperCAmelCase     :  List[str]							      =    ["run_translation.py"] + args
                    with patch.object(A						,					"argv"						,					A       ):
                           main()

             return output_dir


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    1  # To kept the Calculated Value
       # Since C(n, k) = C(n, n-k)
       if k > (n - k):
              _UpperCAmelCase     :  Any							      =    n - k
       # Calculate C(n,k)
       for i in range(_UpperCAmelCase    ):
              result *= n - i
              result //= i + 1
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       return binomial_coefficient(2 * node_count			,	_UpperCAmelCase    ) // (node_count + 1)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       if n < 0:
              raise ValueError("factorial() not defined for negative values"    )
       _UpperCAmelCase     :  List[str]							      =    1
       for i in range(1			,	n + 1    ):
              result *= i
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       return catalan_number(_UpperCAmelCase    ) * factorial(_UpperCAmelCase    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Dict     =  int(input("""Enter the number of nodes: """).strip() or 0)
  if node_count <= 0:
    raise ValueError("""We need some nodes to work with.""")
  print(
      F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
      F'binary trees and {catalan_number(node_count)} binary search trees.'
  )

