


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor







class        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :Tuple			,__snake_case   :Optional[int]			,__snake_case   :Any=7			,__snake_case   :Optional[int]=3			,__snake_case   :Dict=30			,__snake_case   :Optional[Any]=4_00			,__snake_case   :str=True			,__snake_case   :List[str]=None			,__snake_case   :str=True			,__snake_case   :Any=[0.5, 0.5, 0.5]			,__snake_case   :int=[0.5, 0.5, 0.5]			,__snake_case   :Any=True			,__snake_case   :List[str]=1 / 2_55			,__snake_case   :List[Any]=True			,)					->    str:
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												a__             =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												a__             =     parent
												a__             =     batch_size
												a__             =     num_channels
												a__             =     min_resolution
												a__             =     max_resolution
												a__             =     do_resize
												a__             =     size
												a__             =     do_normalize
												a__             =     image_mean
												a__             =     image_std
												a__             =     do_rescale
												a__             =     rescale_factor
												a__             =     do_pad
					def  lowerCamelCase__(      self   :str      )					->    Dict:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}



					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str			,__snake_case   :Tuple=False      )					->    Tuple:
												if not batched:
																			a__             =     image_inputs[0]
																			if isinstance(__snake_case			,Image.Image      ):
																										a__      ,					a__             =     image.size
																			else:
																										a__      ,					a__             =     image.shape[1], image.shape[2]
																			if w < h:
																										a__             =     int(self.size['shortest_edge'] * h / w      )
																										a__             =     self.size['shortest_edge']
																			elif w > h:
																										a__             =     self.size['shortest_edge']
																										a__             =     int(self.size['shortest_edge'] * w / h      )
																			else:
																										a__             =     self.size['shortest_edge']
																										a__             =     self.size['shortest_edge']

												else:
																			a__             =     []
																			for image in image_inputs:
																										a__      ,					a__             =     self.get_expected_values([image]      )
																										expected_values.append((expected_height, expected_width)      )
																			a__             =     max(__snake_case			,key=lambda __snake_case      : item[0]      )[0]
																			a__             =     max(__snake_case			,key=lambda __snake_case      : item[1]      )[1]

												return expected_height, expected_width







@require_torch
@require_vision
class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Optional[Any]          =			DeformableDetrImageProcessor if is_vision_available() else None
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												a__             =     DeformableDetrImageProcessingTester(self      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Any:
												return self.image_processor_tester.prepare_image_processor_dict()
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__snake_case			,'image_mean'      )      )
												self.assertTrue(hasattr(__snake_case			,'image_std'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_normalize'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_resize'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_rescale'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_pad'      )      )
												self.assertTrue(hasattr(__snake_case			,'size'      )      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												a__             =     self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 18, 'longest_edge': 13_33}      )
												self.assertEqual(image_processor.do_pad			,__snake_case      )

												a__             =     self.image_processing_class.from_dict(
												    self.image_processor_dict			,size=42			,max_size=84			,pad_and_return_pixel_mask=__snake_case      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 42, 'longest_edge': 84}      )
												self.assertEqual(image_processor.do_pad			,__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												pass
					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												a__             =     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
												a__             =     image_processing(image_inputs[0]			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,)

												# Test batched
												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												a__             =     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,
												        expected_height,
												        expected_width,
												    )			,)
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												a__             =     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
												a__             =     image_processing(image_inputs[0]			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,)

												# Test batched
												a__             =     image_processing(__snake_case			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )			,)
					def  lowerCamelCase__(      self   :Any      )					->    Optional[int]:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												a__             =     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
												a__             =     image_processing(image_inputs[0]			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,)

												# Test batched
												a__             =     image_processing(__snake_case			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )			,)
					@slow
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												# prepare image and target
												a__             =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,'r'      ) as f:
																			a__             =     json.loads(f.read()      )

												a__             =     {'image_id': 3_97_69, 'annotations': target}

												# encode them
												a__             =     DeformableDetrImageProcessor()
												a__             =     image_processing(images=__snake_case			,annotations=__snake_case			,return_tensors='pt'      )

												# verify pixel values
												a__             =     torch.Size([1, 3, 8_00, 10_66]      )
												self.assertEqual(encoding['pixel_values'].shape			,__snake_case      )

												a__             =     torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,__snake_case			,atol=1E-4      )      )

												# verify area
												a__             =     torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,__snake_case      )      )
												# verify boxes
												a__             =     torch.Size([6, 4]      )
												self.assertEqual(encoding['labels'][0]['boxes'].shape			,__snake_case      )
												a__             =     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,__snake_case			,atol=1E-3      )      )
												# verify image_id
												a__             =     torch.tensor([3_97_69]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,__snake_case      )      )
												# verify is_crowd
												a__             =     torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,__snake_case      )      )
												# verify class_labels
												a__             =     torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,__snake_case      )      )
												# verify orig_size
												a__             =     torch.tensor([4_80, 6_40]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,__snake_case      )      )
												# verify size
												a__             =     torch.tensor([8_00, 10_66]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,__snake_case      )      )



					@slow
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												# prepare image, target and masks_path
												a__             =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
												with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,'r'      ) as f:
																			a__             =     json.loads(f.read()      )

												a__             =     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

												a__             =     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

												# encode them
												a__             =     DeformableDetrImageProcessor(format='coco_panoptic'      )
												a__             =     image_processing(images=__snake_case			,annotations=__snake_case			,masks_path=__snake_case			,return_tensors='pt'      )

												# verify pixel values
												a__             =     torch.Size([1, 3, 8_00, 10_66]      )
												self.assertEqual(encoding['pixel_values'].shape			,__snake_case      )

												a__             =     torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,__snake_case			,atol=1E-4      )      )

												# verify area
												a__             =     torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,__snake_case      )      )
												# verify boxes
												a__             =     torch.Size([6, 4]      )
												self.assertEqual(encoding['labels'][0]['boxes'].shape			,__snake_case      )
												a__             =     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,__snake_case			,atol=1E-3      )      )
												# verify image_id
												a__             =     torch.tensor([3_97_69]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,__snake_case      )      )
												# verify is_crowd
												a__             =     torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,__snake_case      )      )
												# verify class_labels
												a__             =     torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,__snake_case      )      )
												# verify masks
												a__             =     82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,__snake_case      )
												# verify orig_size
												a__             =     torch.tensor([4_80, 6_40]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,__snake_case      )      )
												# verify size
												a__             =     torch.tensor([8_00, 10_66]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,__snake_case      )      )








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      )




