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': 6_5_0, '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': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]				)
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    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=__snake_case	,					)
										assert hasattr(self	,					'''env'''		)







							def 				snake_case  (				self     :    List[str]	,					__snake_case     :    List[str]=1		):
										# 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}-single"	,					instance_count=__snake_case	,					instance_type=self.instance_type	,					debugger_hook_config=__snake_case	,					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     :    str	,					__snake_case     :    Tuple		):
										TrainingJobAnalytics(__snake_case		).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										# create estimator
										lowerCamelCase						:List[str]						  =     self.create_estimator()

										# run training
										estimator.fit()

										# result dataframe
										lowerCamelCase						:Any						  =     TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

										# extract kpis
										lowerCamelCase						:Tuple						  =     list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
										lowerCamelCase						:int						  =     list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										lowerCamelCase						:Tuple						  =     (
										    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}	,					__snake_case		)




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__           =							imread(R"""digital_image_processing/image_data/lena_small.jpg""")
A__           =							cvtColor(img, COLOR_BGR2GRAY)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     cn.convert_to_negative(a_)
			# assert negative_img array for at least one True
			assert negative_img.any()





def  _lowerCamelCase					(   ):
			with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
						# Work around assertion for response
						assert str(cc.change_contrast(a_						,			1_10)).startswith(
						    '''<PIL.Image.Image image mode=RGB size=100x100 at''')





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     canny.gen_gaussian_kernel(9						,			sigma=1.4)
			# Assert ambiguous array
			assert resp.all()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:str						  =     imread('''digital_image_processing/image_data/lena_small.jpg'''						,			0)
			# assert ambiguous array for all == True
			assert canny_img.all()
			lowerCamelCase						:Optional[Any]						  =     canny.canny(a_)
			# assert canny array for at least one True
			assert canny_array.any()





def  _lowerCamelCase					(   ):
			assert gg.gaussian_filter(a_						,			5						,			sigma=0.9).all()





def  _lowerCamelCase					(   ):
			# laplace diagonals
			lowerCamelCase						:List[Any]						  =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
			lowerCamelCase						:List[Any]						  =     conv.img_convolve(a_						,			a_).astype(a_)
			assert res.any()





def  _lowerCamelCase					(   ):
			assert med.median_filter(a_						,			3).any()





def  _lowerCamelCase					(   ):
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     sob.sobel_filter(a_)
			assert grad.any() and theta.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Dict						  =     sp.make_sepia(a_						,			20)
			assert sepia.all()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"):
			lowerCamelCase						:Any						  =     bs.Burkes(imread(a_						,			1)						,			1_20)
			burkes.process()
			assert burkes.output_img.any()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"						,			):
			lowerCamelCase						:Tuple						  =     rs.NearestNeighbour(imread(a_						,			1)						,			4_00						,			2_00)
			nn.process()
			assert nn.output.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''digital_image_processing/image_data/lena.jpg'''

			# Reading the image and converting it to grayscale.
			lowerCamelCase						:Tuple						  =     imread(a_						,			0)

			# Test for get_neighbors_pixel function() return not None
			lowerCamelCase						:Dict						  =     0
			lowerCamelCase						:Optional[Any]						  =     0
			lowerCamelCase						:str						  =     image[x_coordinate][y_coordinate]

			lowerCamelCase						:Any						  =     lbp.get_neighbors_pixel(
			    a_						,			a_						,			a_						,			a_)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			lowerCamelCase						:int						  =     np.zeros((image.shape[0], image.shape[1]))

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0						,			image.shape[0]):
						for j in range(0						,			image.shape[1]):
									lowerCamelCase						:Optional[int]						  =     lbp.local_binary_value(a_						,			a_						,			a_)

			assert lbp_image.any()



