



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": 650, "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": 600, "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=SCREAMING_SNAKE_CASE_,      )
          assert hasattr(self,      'env'	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_=1	)					->		List[str]:
          # 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=SCREAMING_SNAKE_CASE_,      instance_type=self.instance_type,      debugger_hook_config=SCREAMING_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,      SCREAMING_SNAKE_CASE_	)					->		str:
          TrainingJobAnalytics(SCREAMING_SNAKE_CASE_	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)






       def  snake_case_	(      self	)					->		Optional[int]:
          # create estimator
          UpperCamelCase   :  List[Any]						     =     self.create_estimator()

          # run training
          estimator.fit()

          # result dataframe
          UpperCamelCase   :  List[Any]						     =     TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

          # extract kpis
          UpperCamelCase   :  List[str]						     =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']	)
          UpperCamelCase   :  Optional[int]						     =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']	)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          UpperCamelCase   :  Any						     =     (
              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},      SCREAMING_SNAKE_CASE_	)








import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:



    class     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     depth_estimator(
              [
                  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  # RGBA
                  dataset[0]['file'],
                  # LA
                  dataset[1]['file'],
                  # L
                  dataset[2]['file'],
              ]	)
          self.assertEqual(
              [
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          # This is highly irregular to have no small tests.
          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'	)



