import unittest

import numpy as np

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

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 DPTImageProcessor





class      A							(				unittest.TestCase     ):


   def __init__(self     :						int       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						List[Any]=7       ,  __UpperCAmelCase     :						str=3       ,  __UpperCAmelCase     :						Tuple=1_8       ,  __UpperCAmelCase     :						int=3_0       ,  __UpperCAmelCase     :						str=4_0_0       ,  __UpperCAmelCase     :						str=True       ,  __UpperCAmelCase     :						List[Any]=None       ,  __UpperCAmelCase     :						Tuple=True       ,  __UpperCAmelCase     :						Any=[0.5, 0.5, 0.5]       ,  __UpperCAmelCase     :						str=[0.5, 0.5, 0.5]       ,  )						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  size if size is not None else {"height": 1_8, "width": 1_8}
         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  min_resolution
         UpperCAmelCase__								=  max_resolution
         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean
         UpperCAmelCase__								=  image_std





   def 			lowercase_      (self     :						Optional[Any]		)						->				int:
         """simple docstring"""



         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }





@require_torch
@require_vision
class      A							(				UpperCAmelCase_		,				unittest.TestCase     ):
   __UpperCAmelCase : Dict      =			DPTImageProcessor if is_vision_available() else None


   def 			lowercase_      (self     :						List[Any]		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  DPTImageProcessingTester(self		)


   @property
   def 			lowercase_      (self     :						str		)						->				Union[str, Any]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def 			lowercase_      (self     :						str		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(__UpperCAmelCase       ,  "image_mean"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase       ,  "image_std"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase       ,  "do_normalize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase       ,  "do_resize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase       ,  "size"		)		)


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size       ,  {"height": 1_8, "width": 1_8}		)

         UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict       ,  size=4_2		)
         self.assertEqual(image_processor.size       ,  {"height": 4_2, "width": 4_2}		)


   def 			lowercase_      (self     :						Optional[int]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=__UpperCAmelCase		)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase       ,  Image.Image		)

         # Test not batched input
         UpperCAmelCase__								=  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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,  )

         # Test batched
         UpperCAmelCase__								=  image_processing(__UpperCAmelCase       ,  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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,  )


   def 			lowercase_      (self     :						str		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=__UpperCAmelCase       ,  numpify=__UpperCAmelCase		)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase       ,  np.ndarray		)

         # Test not batched input
         UpperCAmelCase__								=  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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,  )

         # Test batched
         UpperCAmelCase__								=  image_processing(__UpperCAmelCase       ,  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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,  )





   def 			lowercase_      (self     :						Any		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=__UpperCAmelCase       ,  torchify=__UpperCAmelCase		)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase       ,  torch.Tensor		)

         # Test not batched input
         UpperCAmelCase__								=  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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,  )

         # Test batched
         UpperCAmelCase__								=  image_processing(__UpperCAmelCase       ,  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.size["height"],
                 self.image_processor_tester.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_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      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """simple docstring"""



         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     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]=1		)						->				Dict:
         """simple docstring"""



         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=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # run training
         estimator.fit()

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

         # extract kpis
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         UpperCAmelCase__								=  (
             Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"       ,  9_9_9_9_9_9		)
         )

         # 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		)



