


"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, 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.p3.16xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]				)
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      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=SCREAMING_SNAKE_CASE      ,   )
      assert hasattr(self      ,   """env""")





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Any   =		F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
      # distributed data settings
      _lowerCamelCase	:					Tuple   =		{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # creates estimator
      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=SCREAMING_SNAKE_CASE      ,   instance_count=SCREAMING_SNAKE_CASE      ,   instance_type=self.instance_type      ,   debugger_hook_config=SCREAMING_SNAKE_CASE      ,   hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,   metric_definitions=self.env.metric_definitions      ,   distribution=SCREAMING_SNAKE_CASE      ,   py_version="""py36"""      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      TrainingJobAnalytics(SCREAMING_SNAKE_CASE).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')





   @parameterized.expand([(2,)])
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      # create estimator
      _lowerCamelCase	:					str   =		self.create_estimator(SCREAMING_SNAKE_CASE)

      # run training
      estimator.fit()

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

      # extract kpis
      _lowerCamelCase	:					int   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
      _lowerCamelCase	:					str   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _lowerCamelCase	:					str   =		(
          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}      ,   SCREAMING_SNAKE_CASE)


'''simple docstring'''


from random import randint, random





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: int = 5     ,       ):


	"""simple docstring"""
	__UpperCAmelCase      					=      [[-1] * number_of_cells]  # Create a highway without any car
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      max(UpperCamelCase__     ,       0							)
	while i < number_of_cells:
		__UpperCAmelCase      					=      (
		    randint(0     ,       UpperCamelCase__							) if random_speed else initial_speed
		)  # Place the cars
		i += (
		    randint(1     ,       max_speed * 2							) if random_frequency else frequency
		)  # Arbitrary number, may need tuning
	return highway





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      highway_now[car_index + 1 :]
	for cell in range(len(UpperCamelCase__							)							):  # May need a better name for this
		if cells[cell] != -1:  # If the cell is not empty then
			return distance  # we have the distance we wanted
		distance += 1
	# Here if the car is near the end of the highway
	return distance + get_distance(UpperCamelCase__     ,       -1							)





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(UpperCamelCase__							)
	# Beforce calculations, the highway is empty
	__UpperCAmelCase      					=      [-1] * number_of_cells

	for car_index in range(UpperCamelCase__							):
		if highway_now[car_index] != -1:
			# Add 1 to the current speed of the car and cap the speed
			__UpperCAmelCase      					=      min(highway_now[car_index] + 1     ,       UpperCamelCase__							)
			# Number of empty cell before the next car
			__UpperCAmelCase      					=      get_distance(UpperCamelCase__     ,       UpperCamelCase__							) - 1
			# We can't have the car causing an accident
			__UpperCAmelCase      					=      min(next_highway[car_index]     ,       UpperCamelCase__							)
			if random() < probability:
				# Randomly, a driver will slow down
				__UpperCAmelCase      					=      max(next_highway[car_index] - 1     ,       0							)
	return next_highway





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(highway[0]							)

	for i in range(UpperCamelCase__							):
		__UpperCAmelCase      					=      update(highway[i]     ,       UpperCamelCase__     ,       UpperCamelCase__							)
		__UpperCAmelCase      					=      [-1] * number_of_cells

		for car_index in range(UpperCamelCase__							):
			__UpperCAmelCase      					=      next_speeds_calculated[car_index]
			if speed != -1:
				# Change the position based on the speed (with % to create the loop)
				__UpperCAmelCase      					=      (car_index + speed) % number_of_cells
				# Commit the change of position
				__UpperCAmelCase      					=      speed
		highway.append(UpperCamelCase__							)

	return highway


if __name__ == "__main__":
					import doctest

					doctest.testmod()
