"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor

class 		UpperCamelCase			(    unittest.TestCase						):



				def __init__(self				:      Optional[int]	,  _A				:      Optional[int]	,  _A				:      str=7	,  _A				:      Union[str, Any]=3	,  _A				:      List[str]=30	,  _A				:      Dict=4_00	,  _A				:      Optional[Any]=True	,  _A				:      Optional[int]=None	,  _A				:      List[Any]=0.9	,  _A				:      List[Any]=None	,  _A				:      Dict=True	,  _A				:      Union[str, Any]=[0.5, 0.5, 0.5]	,  _A				:      str=[0.5, 0.5, 0.5]	,  )    ->	Optional[Any]:
					__snake_case			:							Tuple      		= size if size is not None else {'shortest_edge': 30}
					__snake_case			:							Any      		= crop_size if crop_size is not None else {'height': 30, 'width': 30}
					__snake_case			:							Tuple      		= parent
					__snake_case			:							Tuple      		= batch_size
					__snake_case			:							Union[str, Any]      		= num_channels
					__snake_case			:							Union[str, Any]      		= min_resolution
					__snake_case			:							int      		= max_resolution
					__snake_case			:							str      		= do_resize_and_center_crop
					__snake_case			:							Tuple      		= size
					__snake_case			:							int      		= crop_pct
					__snake_case			:							Any      		= crop_size
					__snake_case			:							Optional[Any]      		= do_normalize
					__snake_case			:							List[str]      		= image_mean
					__snake_case			:							List[str]      		= image_std






				def 					_lowercase     (self				:      List[Any])    ->	Union[str, Any]:
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}







@require_torch
@require_vision
class 		UpperCamelCase			(    lowerCamelCase_      ,	unittest.TestCase						):
				UpperCAmelCase				:      Tuple    =		PoolFormerImageProcessor if is_vision_available() else None



				def 					_lowercase     (self				:      Optional[Any])    ->	Optional[Any]:
					__snake_case			:							Tuple      		= PoolFormerImageProcessingTester(self)



				@property
				def 					_lowercase     (self				:      List[str])    ->	str:
					return self.image_processor_tester.prepare_image_processor_dict()



				def 					_lowercase     (self				:      Tuple)    ->	List[Any]:
					__snake_case			:							Dict      		= self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__snake_case	,  'do_resize_and_center_crop'))
					self.assertTrue(hasattr(__snake_case	,  'size'))
					self.assertTrue(hasattr(__snake_case	,  'crop_pct'))
					self.assertTrue(hasattr(__snake_case	,  'do_normalize'))
					self.assertTrue(hasattr(__snake_case	,  'image_mean'))
					self.assertTrue(hasattr(__snake_case	,  'image_std'))



				def 					_lowercase     (self				:      Tuple)    ->	Dict:
					__snake_case			:							List[str]      		= self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size	,  {'shortest_edge': 30})
					self.assertEqual(image_processor.crop_size	,  {'height': 30, 'width': 30})

					__snake_case			:							Optional[Any]      		= self.image_processing_class.from_dict(self.image_processor_dict	,  size=42	,  crop_size=84)
					self.assertEqual(image_processor.size	,  {'shortest_edge': 42})
					self.assertEqual(image_processor.crop_size	,  {'height': 84, 'width': 84})



				def 					_lowercase     (self				:      int)    ->	int:
					pass



				def 					_lowercase     (self				:      Dict)    ->	Dict:
					# Initialize image_processing
					__snake_case			:							Dict      		= self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case			:							Any      		= prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__snake_case)
					for image in image_inputs:
						self.assertIsInstance(__snake_case	,  Image.Image)

					# Test not batched input
					__snake_case			:							Dict      		= image_processing(image_inputs[0]	,  return_tensors='pt').pixel_values

					self.assertEqual(
					    encoded_images.shape	,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )

					# Test batched
					__snake_case			:							List[Any]      		= image_processing(__snake_case	,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )



				def 					_lowercase     (self				:      List[str])    ->	Optional[Any]:
					# Initialize image_processing
					__snake_case			:							Dict      		= self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					__snake_case			:							Union[str, Any]      		= prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__snake_case	,  numpify=__snake_case)
					for image in image_inputs:
						self.assertIsInstance(__snake_case	,  np.ndarray)

					# Test not batched input
					__snake_case			:							Any      		= image_processing(image_inputs[0]	,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )

					# Test batched
					__snake_case			:							Any      		= image_processing(__snake_case	,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )






				def 					_lowercase     (self				:      Tuple)    ->	str:
					# Initialize image_processing
					__snake_case			:							List[Any]      		= self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					__snake_case			:							Union[str, Any]      		= prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__snake_case	,  torchify=__snake_case)
					for image in image_inputs:
						self.assertIsInstance(__snake_case	,  torch.Tensor)

					# Test not batched input
					__snake_case			:							int      		= image_processing(image_inputs[0]	,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )

					# Test batched
					__snake_case			:							List[Any]      		= image_processing(__snake_case	,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )	,  )









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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    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=__snake_case			,)
												assert hasattr(self			,'env'      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# 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  lowerCamelCase__(      self   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# run training
												estimator.fit()

												# result dataframe
												a__             =     TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

												# extract kpis
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												a__             =     (
												    Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'			,99_99_99      )
												)

												# 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      )




