





'''simple docstring'''






from __future__ import annotations

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




class  _A :

		def __init__(   self					:   List[Any]  ,      __magic_name__					:   Collection[float] | None = None			)					->    None:


					"""simple docstring"""

					if components is None:
								__snake_case					:      Tuple							      =     []
					__snake_case					:      List[str]							      =     list(__magic_name__			)

		def __len__(   self					:   Optional[Any]			)					->    int:


					"""simple docstring"""

					return len(self.__components			)

		def __str__(   self					:   str			)					->    str:


					"""simple docstring"""

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

		def __add__(   self					:   int  ,      __magic_name__					:   Vector			)					->    Vector:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     len(self			)
					if size == len(__magic_name__			):
								__snake_case					:      Any							      =     [self.__components[i] + other.component(__magic_name__			) for i in range(__magic_name__			)]
								return Vector(__magic_name__			)
					else:
								raise Exception("""must have the same size"""			)

		def __sub__(   self					:   List[Any]  ,      __magic_name__					:   Vector			)					->    Vector:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     len(self			)
					if size == len(__magic_name__			):
								__snake_case					:      Dict							      =     [self.__components[i] - other.component(__magic_name__			) for i in range(__magic_name__			)]
								return Vector(__magic_name__			)
					else:  # error case
								raise Exception("""must have the same size"""			)

		@overload
		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   float			)					->    Vector:


					"""simple docstring"""

					...

		@overload
		def __mul__(   self					:   Any  ,      __magic_name__					:   Vector			)					->    float:


					"""simple docstring"""

					...

		def __mul__(   self					:   Dict  ,      __magic_name__					:   float | Vector			)					->    float | Vector:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict			)					->    Vector:


					"""simple docstring"""

					return Vector(self.__components			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   int  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert -len(self.__components			) <= pos < len(self.__components			)
					__snake_case					:      str							      =     value

		def 	lowercase__     (   self					:   Any			)					->    float:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   Vector  ,      __magic_name__					:   bool = False			)					->    float:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self * other
					__snake_case					:      Optional[Any]							      =     self.euclidean_length() * other.euclidean_length()
					if deg:
								return math.degrees(math.acos(num / den			)			)
					else:
								return math.acos(num / den			)







def 					_a						(       _lowerCamelCase ) ->				Vector:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Vector:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Vector:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Vector:



			"""simple docstring"""


			random.seed(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     [random.randint(_lowerCamelCase    ,	_lowerCamelCase ) for _ in range(_lowerCamelCase )]
			return Vector(_lowerCamelCase )




class  _A :

		def __init__(   self					:   List[Any]  ,      __magic_name__					:   list[list[float]]  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    None:


					"""simple docstring"""

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

		def __str__(   self					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      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					:   Tuple  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

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

		def __sub__(   self					:   int  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

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

		@overload
		def __mul__(   self					:   int  ,      __magic_name__					:   float			)					->    Matrix:


					"""simple docstring"""

					...

		@overload
		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   Vector			)					->    Vector:


					"""simple docstring"""

					...

		def __mul__(   self					:   Dict  ,      __magic_name__					:   float | Vector			)					->    Vector | Matrix:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					return self.__height

		def 	lowercase__     (   self					:   int			)					->    int:


					"""simple docstring"""

					return self.__width

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""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 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					if 0 <= x < self.__height and 0 <= y < self.__width:
								__snake_case					:      Optional[Any]							      =     value
					else:
								raise Exception("""change_component: indices out of bounds"""			)

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


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

		def 	lowercase__     (   self					:   Optional[int]			)					->    float:


					"""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:
								__snake_case					:      Tuple							      =     [
								    self.__matrix[0][y] * self.cofactor(0  ,      __magic_name__			) for y in range(self.__width			)
								]
								return sum(__magic_name__			)







def 					_a						(       _lowerCamelCase ) ->				Matrix:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Matrix:



			"""simple docstring"""


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









import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
       from sagemaker import Session, TrainingJobAnalytics
       from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""			,"""False"""	)	) is not True			,reason="""Skipping test because should only be run when releasing minor transformers version"""			,)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             if self.framework == "pytorch":
                    subprocess.run(
                        F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()   ,				encoding='''utf-8'''   ,				check=a__   ,				)
             assert hasattr(self   ,				'''env''')


      def 					__snake_case      (		self   ,				a__=1):



             """simple docstring"""





             return HuggingFace(
                 entry_point=self.script   ,				source_dir=self.env.test_path   ,				role=self.env.role   ,				image_uri=self.env.image_uri   ,				base_job_name=F"""{self.env.base_job_name}-single"""   ,				instance_count=a__   ,				instance_type=self.instance_type   ,				debugger_hook_config=a__   ,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}   ,				metric_definitions=self.env.metric_definitions   ,				py_version='''py36'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             TrainingJobAnalytics(a__).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.create_estimator()

             # run training
             estimator.fit()

             # result dataframe
             _lowerCamelCase				:  Tuple						=		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

             # extract kpis
             _lowerCamelCase				:  Tuple						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
             _lowerCamelCase				:  Dict						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             _lowerCamelCase				:  Optional[Any]						=		(
                 Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''   ,				99_9999)
             )

             # assert kpis
             assert train_runtime <= self.results["train_runtime"]
             assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy)
             assert all(t <= self.results['''eval_loss'''] for t in eval_loss)

             # dump tests result into json file to share in PR
             with open(F"""{estimator.latest_training_job.name}.json"""   ,				'''w''') as outfile:
                    json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}   ,				a__)

