






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_50, '''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_00, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]					)
class 		_snake_case  (	unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						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=__lowerCamelCase		,					)
						assert hasattr(self		,					'env')






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=1)      ->       Union[str, Any]:
						# 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=__lowerCamelCase		,					instance_type=self.instance_type		,					debugger_hook_config=__lowerCamelCase		,					hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}		,					metric_definitions=self.env.metric_definitions		,					py_version='py36'		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						TrainingJobAnalytics(__lowerCamelCase).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''')







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						# create estimator
						SCREAMING_SNAKE_CASE						= self.create_estimator()

						# run training
						estimator.fit()

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

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












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 MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})

						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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'],
						    )		,					)




