





"""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


_UpperCamelCase							=      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 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
											"""simple docstring"""




											def __init__(     self  :str						,    __lowercase  :int = 101			):
																		__lowerCamelCase      :      int         =length




											def __len__(     self  :List[str]			):
																		return self.length




											def __getitem__(     self  :int						,    __lowercase  :List[Any]			):
																		return i

							class 		SCREAMING_SNAKE_CASE_							:
											"""simple docstring"""




											def __call__(     self  :List[str]						,    __lowercase  :List[Any]			):
																		return {"input_ids": torch.tensor(__lowercase			), "labels": torch.tensor(__lowercase			)}

							class 		SCREAMING_SNAKE_CASE_							(    nn.Module   ):
											"""simple docstring"""




											def __init__(     self  :str			):
																		super().__init__()
																		# Add some (unused) params otherwise DDP will complain.
																		__lowerCamelCase      :      List[Any]         =nn.Linear(120						,    80			)




											def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :Any						,    __lowercase  :int=None			):
																		if labels is not None:
																									return torch.tensor(0.0						,    device=input_ids.device			), input_ids
																		else:
																									return input_ids
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				@require_torch_neuroncore
				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      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()
											__lowerCamelCase      :      Tuple         =self.get_auto_remove_tmp_dir()
											__lowerCamelCase      :      str         =f'--output_dir {output_dir}'.split()
											__lowerCamelCase      :      Optional[int]         =['''torchrun'''] + distributed_args + args
											execute_subprocess_async(__lowercase						,    env=self.get_env()			)
											# successful return here == success - any errors would have caused an error in the sub-call

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				@require_torch_multi_gpu
				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      str         =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()
											__lowerCamelCase      :      int         =self.get_auto_remove_tmp_dir()
											__lowerCamelCase      :      Union[str, Any]         =f'--output_dir {output_dir}'.split()
											__lowerCamelCase      :      Optional[Any]         =['''torchrun'''] + distributed_args + args
											execute_subprocess_async(__lowercase						,    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

							_UpperCamelCase							=      HfArgumentParser((TrainingArguments,))
							_UpperCamelCase							=      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 [101, 40, 7]:
														_UpperCamelCase							=      DummyDataset(dataset_length)



														def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							EvalPrediction   ):


																					'''simple docstring'''







																					__lowerCamelCase      :      Optional[int]         =list(range(len(SCREAMING_SNAKE_CASE   )   )   )
																					__lowerCamelCase      :      Tuple         =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}

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

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

														_UpperCamelCase							=      2

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

														_UpperCamelCase							=      trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)
														_UpperCamelCase							=      None











"""simple docstring"""





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :Collection[float] | None = None			):
											if components is None:
																		__lowerCamelCase      :      str         =[]
											__lowerCamelCase      :      int         =list(__lowercase			)




				def __len__(     self  :Union[str, Any]			):
											return len(self.__components			)




				def __str__(     self  :List[Any]			):
											return "(" + ",".join(map(__lowercase						,    self.__components			)			) + ")"




				def __add__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											__lowerCamelCase      :      Tuple         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      int         =[self.__components[i] + other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:
																		raise Exception('''must have the same size'''			)




				def __sub__(     self  :int						,    __lowercase  :Vector			):
											__lowerCamelCase      :      int         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] - other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:  # error case
																		raise Exception('''must have the same size'''			)




				@overload
				def __mul__(     self  :List[str]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :List[Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    (float, int)			):
																		__lowerCamelCase      :      Optional[int]         =[c * other for c in self.__components]
																		return Vector(__lowercase			)
											elif isinstance(__lowercase						,    __lowercase			) and len(self			) == len(__lowercase			):
																		__lowerCamelCase      :      Dict         =len(self			)
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] * other.component(__lowercase			) for i in range(__lowercase			)]
																		return sum(__lowercase			)
											else:  # error case
																		raise Exception('''invalid operand!'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											return Vector(self.__components			)




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :int			):
											if isinstance(__lowercase						,    __lowercase			) and -len(self.__components			) <= i < len(self.__components			):
																		return self.__components[i]
											else:
																		raise Exception('''index out of range'''			)




				def 					__lowercase  (     self  :int						,    __lowercase  :int						,    __lowercase  :float			):
											assert -len(self.__components			) <= pos < len(self.__components			)
											__lowerCamelCase      :      int         =value




				def 					__lowercase  (     self  :Union[str, Any]			):
											if len(self.__components			) == 0:
																		raise Exception('''Vector is empty'''			)
											__lowerCamelCase      :      Tuple         =[c**2 for c in self.__components]
											return math.sqrt(sum(__lowercase			)			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :Vector						,    __lowercase  :bool = False			):
											__lowerCamelCase      :      Tuple         =self * other
											__lowerCamelCase      :      str         =self.euclidean_length() * other.euclidean_length()
											if deg:
																		return math.degrees(math.acos(num / den			)			)
											else:
																		return math.acos(num / den			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							return Vector([0] * dimension   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) and (isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ))
							__lowerCamelCase      :      List[Any]         =[0] * dimension
							__lowerCamelCase      :      Union[str, Any]         =1
							return Vector(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							Vector    ,						SCREAMING_SNAKE_CASE       :							Vector   ):


							'''simple docstring'''







							assert (
							    isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and (isinstance(SCREAMING_SNAKE_CASE    ,						(int, float)   ))
							)
							return x * scalar + y



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =[random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
							return Vector(SCREAMING_SNAKE_CASE   )
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :list[list[float]]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[Any]         =matrix
											__lowerCamelCase      :      int         =w
											__lowerCamelCase      :      str         =h




				def __str__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =''''''
											for i in range(self.__height			):
																		ans += "|"
																		for j in range(self.__width			):
																									if j < self.__width - 1:
																																ans += str(self.__matrix[i][j]			) + ","
																									else:
																																ans += str(self.__matrix[i][j]			) + "|\n"
											return ans




				def __add__(     self  :Any						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      Optional[int]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Optional[int]         =[
																									    self.__matrix[i][j] + other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrix must have the same dimension!'''			)




				def __sub__(     self  :Tuple						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      List[str]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Dict         =[
																									    self.__matrix[i][j] - other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrices must have the same dimension!'''			)




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :Union[str, Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    __lowercase			):  # matrix-vector
																		if len(__lowercase			) == self.__width:
																									__lowerCamelCase      :      int         =zero_vector(self.__height			)
																									for i in range(self.__height			):
																																__lowerCamelCase      :      Union[str, Any]         =[
																																    self.__matrix[i][j] * other.component(__lowercase			)
																																    for j in range(self.__width			)
																																]
																																ans.change_component(__lowercase						,    sum(__lowercase			)			)
																									return ans
																		else:
																									raise Exception(
																									    '''vector must have the same size as the '''
																									    '''number of columns of the matrix!'''			)
											elif isinstance(__lowercase						,    (int, float)			):  # matrix-scalar
																		__lowerCamelCase      :      Optional[Any]         =[
																		    [self.__matrix[i][j] * other for j in range(self.__width			)]
																		    for i in range(self.__height			)
																		]
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.__height




				def 					__lowercase  (     self  :Tuple			):
											return self.__width




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :int			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return self.__matrix[x][y]
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :float			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		__lowerCamelCase      :      int         =value
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											__lowerCamelCase      :      Union[str, Any]         =self.__matrix[:x] + self.__matrix[x + 1 :]
											for i in range(len(__lowercase			)			):
																		__lowerCamelCase      :      Optional[int]         =minor[i][:y] + minor[i][y + 1 :]
											return Matrix(__lowercase						,    self.__width - 1						,    self.__height - 1			).determinant()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return (-1) ** (x + y) * self.minor(__lowercase						,    __lowercase			)
											else:
																		raise Exception('''Indices out of bounds'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if self.__height < 1:
																		raise Exception('''Matrix has no element'''			)
											elif self.__height == 1:
																		return self.__matrix[0][0]
											elif self.__height == 2:
																		return (
																		    self.__matrix[0][0] * self.__matrix[1][1]
																		    - self.__matrix[0][1] * self.__matrix[1][0]
																		)
											else:
																		__lowerCamelCase      :      int         =[
																		    self.__matrix[0][y] * self.cofactor(0						,    __lowercase			) for y in range(self.__width			)
																		]
																		return sum(__lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      list[list[float]]         =[[0] * n for _ in range(SCREAMING_SNAKE_CASE   )]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      list[list[float]]         =[
							    [random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )
							]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )




