


'''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	:						Any       =	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 							__UpperCamelCase				(		A__					):




		def __init__(   self						,				__a = 101							):

							'''simple docstring'''





							__a			:       List[Any]					    =  length




		def __len__(   self							):

							'''simple docstring'''





							return self.length





		def __getitem__(   self						,				__a							):

							'''simple docstring'''





							return i





	class 							__UpperCamelCase				:




		def __call__(   self						,				__a							):

							'''simple docstring'''





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





	class 							__UpperCamelCase				(		nn.Module					):




		def __init__(   self							):

							'''simple docstring'''





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





		def       __UpperCAmelCase							(   self						,				__a						,				__a=None							):

							'''simple docstring'''





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





class 							__UpperCamelCase				(		A__					):




	@require_torch_neuroncore
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  f"""--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.split()
						__a			:       List[Any]					    =  self.get_auto_remove_tmp_dir()
						__a			:       int					    =  f"""--output_dir {output_dir}""".split()
						__a			:       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





class 							__UpperCamelCase				(		A__					):




	@require_torch_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       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()
						__a			:       List[str]					    =  self.get_auto_remove_tmp_dir()
						__a			:       str					    =  f"""--output_dir {output_dir}""".split()
						__a			:       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


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	:						Any       =	HfArgumentParser((TrainingArguments,))
	__lowercase	:						int       =	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_01, 40, 7]:
		__lowercase	:						List[str]       =	DummyDataset(dataset_length)






		def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
							__a			:       List[Any]					    =  list(range(len(__lowerCAmelCase   )   )   )
							__a			:       List[str]					    =  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	:						Any       =	Trainer(
		    model=DummyModel(),
		    args=training_args,
		    data_collator=DummyDataCollator(),
		    eval_dataset=dataset,
		    compute_metrics=compute_metrics,
		)
		__lowercase	:						Optional[Any]       =	trainer.evaluate()
		logger.info(metrics)
		if metrics["eval_success"] is not True:
			logger.error(metrics)
			exit(1)

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

		__lowercase	:						Any       =	2

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

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




		__lowercase	:						Any       =	None











from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






      return (
          F'{self.real}+'
          F'{"+".join(str(SCREAMING_SNAKE_CASE						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
      )







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           return Dual(self.real + other							,   self.duals						)
      UpperCamelCase__			:   Optional[int]       				=  self.duals.copy()
      UpperCamelCase__			:   Any       				=  other.duals.copy()
      if len(SCREAMING_SNAKE_CASE						) > len(SCREAMING_SNAKE_CASE						):
           o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      elif len(SCREAMING_SNAKE_CASE						) < len(SCREAMING_SNAKE_CASE						):
           s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      UpperCamelCase__			:   Optional[int]       				=  []
      for i in range(len(SCREAMING_SNAKE_CASE						)						):
           new_duals.append(s_dual[i] + o_dual[i]						)
      return Dual(self.real + other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i * other						)
           return Dual(self.real * other							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  [0] * (len(self.duals						) + len(other.duals						) + 1)
      for i, item in enumerate(self.duals						):
           for j, jtem in enumerate(other.duals						):
                new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals						)						):
           new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals						)						):
           new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i / other						)
           return Dual(self.real / other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  []
           for i in self.duals:
                new_duals.append(i // other						)
           return Dual(self.real // other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if n < 0 or isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           raise ValueError("power must be a positive integer"						)
      if n == 0:
           return 1
      if n == 1:
           return self
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     if not callable(__lowerCAmelCase			):
          raise ValueError("differentiate() requires a function as input for func"			)
     if not isinstance(__lowerCAmelCase					,    (float, int)			):
          raise ValueError("differentiate() requires a float as input for position"			)
     if not isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          raise ValueError("differentiate() requires an int as input for order"			)
     UpperCamelCase__			:   Optional[Any]       				=  Dual(__lowerCAmelCase					,    1			)
     UpperCamelCase__			:   Any       				=  func(__lowerCAmelCase			)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))