




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


SCREAMING_SNAKE_CASE_            = 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 			a					(		UpperCAmelCase			):


											def __init__(    self  ,   A_ = 101		):







															'''simple docstring'''


															_UpperCAmelCase    :						int							=      length


											def __len__(    self		):







															'''simple docstring'''


															return self.length





											def __getitem__(    self  ,   A_		):







															'''simple docstring'''


															return i







					class 			a					:


											def __call__(    self  ,   A_		):







															'''simple docstring'''


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







					class 			a					(		nn.Module			):


											def __init__(    self		):







															'''simple docstring'''


															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															_UpperCAmelCase    :						List[str]							=      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 			a					(		UpperCAmelCase			):


						@require_torch_neuroncore
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						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()
										_UpperCAmelCase    :						Optional[int]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'--output_dir {output_dir}'.split()
										_UpperCAmelCase    :						List[str]							=      ["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 			a					(		UpperCAmelCase			):


						@require_torch_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      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    :						Optional[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Any							=      f'--output_dir {output_dir}'.split()
										_UpperCAmelCase    :						str							=      ["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

					SCREAMING_SNAKE_CASE_            = HfArgumentParser((TrainingArguments,))
					SCREAMING_SNAKE_CASE_            = 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]:
										SCREAMING_SNAKE_CASE_            = DummyDataset(dataset_length)

										def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					EvalPrediction	)				->    Dict:
														_UpperCAmelCase    :						int							=      list(range(len(lowerCAmelCase	)	)	)
														_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}

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

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

										SCREAMING_SNAKE_CASE_            = 2

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

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





										SCREAMING_SNAKE_CASE_            = None





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 			a					:


						def __init__(    self  ,   A_ = None		):







										'''simple docstring'''


										if components is None:
														_UpperCAmelCase    :						Dict							=      []
										_UpperCAmelCase    :						Dict							=      list(A_		)


						def __len__(    self		):







										'''simple docstring'''


										return len(self.__components		)


						def __str__(    self		):







										'''simple docstring'''


										return "(" + ",".join(map(A_  ,   self.__components		)		) + ")"


						def __add__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      len(self		)
										if size == len(A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      [self.__components[i] + other.component(A_		) for i in range(A_		)]
														return Vector(A_		)
										else:
														raise Exception("must have the same size"		)


						def __sub__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      len(self		)
										if size == len(A_		):
														_UpperCAmelCase    :						Optional[Any]							=      [self.__components[i] - other.component(A_		) for i in range(A_		)]
														return Vector(A_		)
										else:  # error case
														raise Exception("must have the same size"		)


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   (float, int)		):
														_UpperCAmelCase    :						str							=      [c * other for c in self.__components]
														return Vector(A_		)
										elif isinstance(A_  ,   A_		) and len(self		) == len(A_		):
														_UpperCAmelCase    :						int							=      len(self		)
														_UpperCAmelCase    :						Any							=      [self.__components[i] * other.component(A_		) for i in range(A_		)]
														return sum(A_		)
										else:  # error case
														raise Exception("invalid operand!"		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return Vector(self.__components		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   A_		) and -len(self.__components		) <= i < len(self.__components		):
														return self.__components[i]
										else:
														raise Exception("index out of range"		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										assert -len(self.__components		) <= pos < len(self.__components		)
										_UpperCAmelCase    :						Any							=      value


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if len(self.__components		) == 0:
														raise Exception("Vector is empty"		)
										_UpperCAmelCase    :						Dict							=      [c**2 for c in self.__components]
										return math.sqrt(sum(A_		)		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self * other
										_UpperCAmelCase    :						str							=      self.euclidean_length() * other.euclidean_length()
										if deg:
														return math.degrees(math.acos(num / den		)		)
										else:
														return math.acos(num / den		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Vector:
				assert isinstance(lowerCAmelCase					, lowerCAmelCase	)
				return Vector([0] * dimension	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Vector:
				assert isinstance(lowerCAmelCase					, lowerCAmelCase	) and (isinstance(lowerCAmelCase					, lowerCAmelCase	))
				_UpperCAmelCase    :						Tuple							=      [0] * dimension
				_UpperCAmelCase    :						Optional[Any]							=      1
				return Vector(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					float					, lowerCAmelCase:					Vector					, lowerCAmelCase:					Vector	)				->    Vector:
				assert (
				    isinstance(lowerCAmelCase					, lowerCAmelCase	)
				    and isinstance(lowerCAmelCase					, lowerCAmelCase	)
				    and (isinstance(lowerCAmelCase					, (int, float)	))
				)
				return x * scalar + y

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Vector:
				random.seed(lowerCAmelCase	)
				_UpperCAmelCase    :						Optional[int]							=      [random.randint(lowerCAmelCase					, lowerCAmelCase	) for _ in range(lowerCAmelCase	)]
				return Vector(lowerCAmelCase	)

class 			a					:


						def __init__(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      matrix
										_UpperCAmelCase    :						Union[str, Any]							=      w
										_UpperCAmelCase    :						str							=      h


						def __str__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						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  ,   A_		):







										'''simple docstring'''


										if self.__width == other.width() and self.__height == other.height():
														_UpperCAmelCase    :						Optional[int]							=      []
														for i in range(self.__height		):
																		_UpperCAmelCase    :						List[str]							=      [
																		    self.__matrix[i][j] + other.component(A_  ,   A_		)
																		    for j in range(self.__width		)
																		]
																		matrix.append(A_		)
														return Matrix(A_  ,   self.__width  ,   self.__height		)
										else:
														raise Exception("matrix must have the same dimension!"		)


						def __sub__(    self  ,   A_		):







										'''simple docstring'''


										if self.__width == other.width() and self.__height == other.height():
														_UpperCAmelCase    :						Optional[Any]							=      []
														for i in range(self.__height		):
																		_UpperCAmelCase    :						Tuple							=      [
																		    self.__matrix[i][j] - other.component(A_  ,   A_		)
																		    for j in range(self.__width		)
																		]
																		matrix.append(A_		)
														return Matrix(A_  ,   self.__width  ,   self.__height		)
										else:
														raise Exception("matrices must have the same dimension!"		)


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						@overload
						def __mul__(    self  ,   A_		):







										'''simple docstring'''


										...


						def __mul__(    self  ,   A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.__height


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.__width


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if 0 <= x < self.__height and 0 <= y < self.__width:
														_UpperCAmelCase    :						Tuple							=      value
										else:
														raise Exception("change_component: indices out of bounds"		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if self.__height != self.__width:
														raise Exception("Matrix is not square"		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(A_		)		):
														_UpperCAmelCase    :						Tuple							=      minor[i][:y] + minor[i][y + 1 :]
										return Matrix(A_  ,   self.__width - 1  ,   self.__height - 1		).determinant()


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										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(A_  ,   A_		)
										else:
														raise Exception("Indices out of bounds"		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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:
														_UpperCAmelCase    :						Dict							=      [
														    self.__matrix[0][y] * self.cofactor(0  ,   A_		) for y in range(self.__width		)
														]
														return sum(A_		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Matrix:
				_UpperCAmelCase    :						list[list[float]]							=      [[0] * n for _ in range(lowerCAmelCase	)]
				return Matrix(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    Matrix:
				random.seed(lowerCAmelCase	)
				_UpperCAmelCase    :						list[list[float]]							=      [
				    [random.randint(lowerCAmelCase					, lowerCAmelCase	) for _ in range(lowerCAmelCase	)] for _ in range(lowerCAmelCase	)
				]
				return Matrix(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)


