





"""simple docstring"""





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase__				:Tuple				   =       logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer




		class 							snake_case							(			__a ):

								'''simple docstring'''



								def __init__(							self       :      Dict						,				__lowercase       :      int = 101 ):


										'''simple docstring'''




										__UpperCAmelCase    :						Optional[Any]       =						length

								def __len__(							self       :      Dict ):


										'''simple docstring'''




										return self.length







								def __getitem__(							self       :      Optional[Any]						,				__lowercase       :      Union[str, Any] ):


										'''simple docstring'''




										return i




		class 							snake_case							:

								'''simple docstring'''



								def __call__(							self       :      Tuple						,				__lowercase       :      Optional[int] ):


										'''simple docstring'''




										return {"input_ids": torch.tensor(a_ ), "labels": torch.tensor(a_ )}




		class 							snake_case							(			nn.Module ):

								'''simple docstring'''



								def __init__(							self       :      Union[str, Any] ):


										'''simple docstring'''




										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										__UpperCAmelCase    :						Optional[int]       =						nn.Linear(120						,				80 )







								def       A_					(							self       :      Optional[int]						,				__lowercase       :      Dict						,				__lowercase       :      Optional[int]=None ):


										'''simple docstring'''




										if labels is not None:
												return torch.tensor(0.0						,				device=input_ids.device ), input_ids
										else:
												return input_ids




class 							snake_case							(			__a ):

						'''simple docstring'''



						@require_torch_neuroncore
						def       A_					(							self       :      List[str] ):


								'''simple docstring'''




								__UpperCAmelCase    :						int       =						f'''--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
								__UpperCAmelCase    :						Union[str, Any]       =						self.get_auto_remove_tmp_dir()
								__UpperCAmelCase    :						int       =						f'''--output_dir {output_dir}'''.split()
								__UpperCAmelCase    :						List[Any]       =						["""torchrun"""] + distributed_args + args
								execute_subprocess_async(a_						,				env=self.get_env() )
								# successful return here == success - any errors would have caused an error in the sub-call




class 							snake_case							(			__a ):

						'''simple docstring'''



						@require_torch_multi_gpu
						def       A_					(							self       :      List[Any] ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[int]       =						f'''--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
								__UpperCAmelCase    :						Tuple       =						self.get_auto_remove_tmp_dir()
								__UpperCAmelCase    :						Tuple       =						f'''--output_dir {output_dir}'''.split()
								__UpperCAmelCase    :						Optional[int]       =						["""torchrun"""] + distributed_args + args
								execute_subprocess_async(a_						,				env=self.get_env() )
								# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
		# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
		#
		# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

		lowercase__				:str				   =       HfArgumentParser((TrainingArguments,))
		lowercase__				:List[Any]				   =       parser.parse_args_into_dataclasses()[0]

		logger.warning(
		    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
		    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
		)

		# Essentially, what we want to verify in the distributed case is that we get all samples back,
		# in the right order. (this is crucial for prediction for instance)
		for dataset_length in [1_0_1, 4_0, 7]:
				lowercase__				:List[str]				   =       DummyDataset(dataset_length)


				def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





						"""simple docstring"""


						__UpperCAmelCase    :						Tuple       =						list(range(len(snake_case__  )  )  )
						__UpperCAmelCase    :						Union[str, Any]       =						p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
						if not success and training_args.local_rank == 0:
								logger.warning(
								    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
								    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''  )
						return {"success": success}

				lowercase__				:Tuple				   =       Trainer(
				    model=DummyModel(),
				    args=training_args,
				    data_collator=DummyDataCollator(),
				    eval_dataset=dataset,
				    compute_metrics=compute_metrics,
				)
				lowercase__				:Union[str, Any]				   =       trainer.evaluate()
				logger.info(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

				lowercase__				:Dict				   =       trainer.predict(dataset)
				logger.info(p.metrics)
				if p.metrics["test_success"] is not True:
						logger.error(p.metrics)
						exit(1)

				lowercase__				:int				   =       2

				lowercase__				:str				   =       trainer.evaluate()
				logger.info(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

				lowercase__				:List[Any]				   =       trainer.predict(dataset)
				logger.info(p.metrics)
				if p.metrics["test_success"] is not True:
						logger.error(p.metrics)
						exit(1)







				lowercase__				:Tuple				   =       None






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__				:int				   =       TypeVar('T')




class 							snake_case							(			Generic[T] ):

      '''simple docstring'''



      def __init__(							self       :      Optional[Any]						,				__lowercase       :      list[T]						,				__lowercase       :      Callable[[T, T], T] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any | T       =						None

        __UpperCAmelCase    :						int       =						len(__lowercase )
        __UpperCAmelCase    :						list[T]       =						[any_type for _ in range(self.N )] + arr
        __UpperCAmelCase    :						List[Any]       =						fnc
        self.build()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for p in range(self.N - 1						,				0						,				-1 ):
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int						,				__lowercase       :      T ):


        '''simple docstring'''




        p += self.N
        __UpperCAmelCase    :						Tuple       =						v
        while p > 1:
          __UpperCAmelCase    :						Union[str, Any]       =						p // 2
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):  # noqa: E741


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						l + self.N, r + self.N

        __UpperCAmelCase    :						T | None       =						None
        while l <= r:
          if l % 2 == 1:
            __UpperCAmelCase    :						Any       =						self.st[l] if res is None else self.fn(__lowercase						,				self.st[l] )
          if r % 2 == 0:
            __UpperCAmelCase    :						Any       =						self.st[r] if res is None else self.fn(__lowercase						,				self.st[r] )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
  from functools import reduce

  lowercase__				:Any				   =       [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  lowercase__				:List[Any]				   =       {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  lowercase__				:List[str]				   =       SegmentTree(test_array, min)
  lowercase__				:List[str]				   =       SegmentTree(test_array, max)
  lowercase__				:Tuple				   =       SegmentTree(test_array, lambda a, b: a + b)


  def       lowerCamelCase_       (   )						->None:





    """simple docstring"""


    for i in range(len(UpperCAmelCase_  )  ):
      for j in range(UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ):
        __UpperCAmelCase    :						Optional[Any]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Optional[int]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Union[str, Any]       =						reduce(lambda UpperCAmelCase_  ,			UpperCAmelCase_  : a + b  ,			test_array[i : j + 1]  )
        assert min_range == min_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert max_range == max_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert sum_range == sum_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )

  test_all_segments()

  for index, value in test_updates.items():
    lowercase__				:Optional[Any]				   =       value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()