






"""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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]      )
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       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=UpperCAmelCase__							,						)
   assert hasattr(self							,						"""env"""							)


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						Union[str, Any]							)					->       List[str]:
   # configuration for running training on smdistributed Model Parallel
   _a  :       List[str]             =	{
       """enabled""": True,
       """processes_per_host""": 8,
   }
   _a  :       Dict             =	{
       """enabled""": True,
       """parameters""": {
           """microbatches""": 4,
           """placement_strategy""": """spread""",
           """pipeline""": """interleaved""",
           """optimize""": """speed""",
           """partitions""": 4,
           """ddp""": True,
       },
   }

   _a  :       List[str]             =	{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

   _a  :       Dict             =	"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
   # 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=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""							,						instance_count=UpperCAmelCase__							,						instance_type=self.instance_type							,						debugger_hook_config=UpperCAmelCase__							,						hyperparameters={
           **self.env.hyperparameters,
           """model_name_or_path""": self.model_name_or_path,
           """max_steps""": 500,
       }							,						metric_definitions=self.env.metric_definitions							,						distribution=UpperCAmelCase__							,						py_version="""py36"""							,						)


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						str							)					->       Union[str, Any]:
   TrainingJobAnalytics(UpperCAmelCase__							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)







 @parameterized.expand([(1,)]							)
 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						Tuple							)					->       Optional[Any]:
   # create estimator
   _a  :       Optional[int]             =	self.create_estimator(UpperCAmelCase__							)

   # run training
   estimator.fit()

   # result dataframe
   _a  :       Optional[Any]             =	TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

   # extract kpis
   _a  :       Tuple             =	list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
   _a  :       List[str]             =	list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]							)
   # get train time from SageMaker job, this includes starting, preprocessing, stopping
   _a  :       List[str]             =	(
       Session().describe_training_job(estimator.latest_training_job.name							).get("""TrainingTimeInSeconds"""							,						999999							)
   )

   # 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}							,						UpperCAmelCase__							)









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	det - k * (trace**2)
       # Can change the value
       if r > 0.5:
         corner_list.append([x, y, r]							)
         color_img.itemset((y, x, 0)							,						0							)
         color_img.itemset((y, x, 1)							,						0							)
         color_img.itemset((y, x, 2)							,						255							)
   return color_img, corner_list


if __name__ == "__main__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

